# Fokals

> Fokals is an AI-powered data intelligence provider delivering company data, market signals and competitive insights on businesses worldwide.

AI-powered company intelligence on how companies around the world grow, change and compete. Fokals derives advanced signals from company data, covering everything from technology and hiring to funding, buying intent and company health. Refreshed every day and brought together into a clear view of every market. It covers listed companies worldwide, the brands they own and verified private companies, each with one stable company ID. Every record carries the time it was observed. Daily and weekly datasets are written once, after the period closes, and are never revised. The data is delivered to AI assistants through an MCP, by REST API and as bulk files, and is licensed by written agreement for internal use, for embedding in a product or for redistribution.

## For AI agents and developers

- [MCP server](https://mcp.fokals.com/mcp): Streamable HTTP. Sign in with a Fokals account (OAuth), or send a key from the account page as Authorization: Bearer <key>. In the official MCP Registry as com.fokals/fokals.
- [REST API](https://api.fokals.com/v1): The same tools as endpoints, one POST a tool, on the same account, balance and prices.
- [OpenAPI document](https://api.fokals.com/v1/openapi.json): Every endpoint, its fields and its answers, in OpenAPI 3.1.
- [Prices](https://api.fokals.com/v1/prices): Today's price for each kind of answer, as JSON, with no key needed.
- [Setups and examples](https://github.com/fokals/fokals-examples): Ready setups for Claude Code, Cursor, VS Code and Cline, and API examples in curl, Node and Python.
- [AI SDK tools](https://www.fokals.com/docs/ai-sdk): The npm package @fokals/ai-sdk: every data tool for an agent built with Vercel’s AI SDK, with fokalsTools() for all of them.
- [LangChain tools](https://pypi.org/project/langchain-fokals/): The Python package langchain-fokals: every data tool for a LangChain agent, and FokalsToolkit for all of them.
- [Adding funds](https://www.fokals.com/docs/connect#add-funds-with-a-payment-token): A prepaid balance in US dollars. The person adds funds by card on their account page, or on the Fokals payment page (https://buy.stripe.com/aFa8wPeDKalk0Qf5BL3F600) with the email of their account. An agent that can open a web page pays on that page from the person's Stripe Link wallet: its checkout takes a Link Pay Token. Any other agent uses the add_funds tool: the person approves a one-time payment in their Link wallet, and the agent passes the payment token it receives.

## Datasets

- [All datasets](https://www.fokals.com/data): The five products and the datasets each one delivers.
- [Marketing stack](https://www.fokals.com/data/marketing-stack): The technologies on each company website with every change dated, price changes among them, monthly vendor scorecards and a score of how ready each company is for AI agents.
- [Hiring](https://www.fokals.com/data/hiring): Every job posting companies publish, labelled by function and seniority, with daily hiring totals, sales-organisation metrics and the projects postings state.
- [Intent](https://www.fokals.com/data/intent): Weekly scores for what each company is likely to buy or build, by topic, and the technologies it is likely to add next, with the evidence behind each.
- [Announcements and scale](https://www.fokals.com/data/announcements): What companies announce about themselves, by event type, with headcount history, monthly traffic tiers and a weekly health score.
- [Market series](https://www.fokals.com/data/market-series): Weekly aggregates showing how industries, countries, size bands and markets moved over 7, 30 and 180 days.

## Every dataset

- [Technology Stack](https://www.fokals.com/data/marketing-stack): One row per website and technology. Every technology detected on a company website, with first-seen and last-seen dates, how it was detected and the public account identifiers found with it.
- [Technology Changes](https://www.fokals.com/data/marketing-stack): One row per observed change. A dated record of every change: technologies added and removed, platform migrations, new markets, languages, currencies and apps, price and plan changes, label changes and reworded titles.
- [Website Profile](https://www.fokals.com/data/marketing-stack): One row per website. The current profile of each website: markets and languages served, currencies, mobile apps, social accounts, key pages and the latest promotion.
- [Vendor Scorecards](https://www.fokals.com/data/marketing-stack): One row per technology, month and segment. Each vendor’s companies at the start of the month, the companies won and lost, the churn rate, and the rivals its new customers left and its lost customers moved to.
- [Agent Readiness](https://www.fokals.com/data/marketing-stack): One row per company. A score from 0 to 100 for how ready the company is for AI agents, in four parts of 25 (find, price, buy, act), with the dated evidence behind each part.
- [Agent Readiness Changes](https://www.fokals.com/data/marketing-stack): One row per company and change of score. Every move of a company’s agent readiness score, dated, with the score before and after.
- [Job Postings](https://www.fokals.com/data/hiring): One row per posting. Each posting with location, work mode, pay, requirements and tools named, labelled by job function, seniority and ten role flags.
- [Hiring Activity](https://www.fokals.com/data/hiring): One row per company and day. Daily totals of open, new and closed postings, broken down by function, seniority, country and work mode.
- [Sales Team Metrics](https://www.fokals.com/data/hiring): One row per company and week. The sales organisation as advertised: open roles by type and segment, median base and on-target earnings, and new sales countries.
- [Sales Pay Benchmarks](https://www.fokals.com/data/hiring): One row per week, role and country. Quartiles of advertised base pay and on-target earnings for sales roles, across companies.
- [Job Posting Projects](https://www.fokals.com/data/hiring): One row per company and project its postings state. The projects a company’s postings state, each once: its kind and details in plain words, how many postings state it, and when they were first and last seen.
- [Intent Scores](https://www.fokals.com/data/intent): One row per company, topic and week. A 0 to 100 score per company, topic and week, with a surge flag and the dated evidence behind it.
- [Company Signals](https://www.fokals.com/data/intent): One row per signal. Every dated signal behind the scores, with where and when it was seen, its kind, its topics and what was seen.
- [Company Funding](https://www.fokals.com/data/intent): One row per private offering. Private offerings by operating companies, as reported in regulatory filings, with issuer, industry, amounts and date of first sale.
- [Likely Next Adoptions](https://www.fokals.com/data/intent): One row per company, week and technology. The estimated likelihood (0 to 1) that a company adds a technology it does not run yet, with the support counts behind it: the changes behind the prediction and how many companies made each change and then added it.
- [Adoption Sequences](https://www.fokals.com/data/intent): One row per week, change, technology and window. The changes that come before companies add a technology: how many made the change and then added it, the share that did, and how that share compares with comparable companies.
- [Company News](https://www.fokals.com/data/announcements): One row per announcement. Each announcement with its date, source, title, link and excerpt, its event types out of thirteen, and the role concerned on leadership changes.
- [Employee Headcount](https://www.fokals.com/data/announcements): One row per company, date and source. Employee counts over time, as stated by each company.
- [Web Traffic](https://www.fokals.com/data/announcements): One row per website and month. The monthly traffic tier of each company website.
- [Company Health](https://www.fokals.com/data/announcements): One row per company and week. A distress score and a breakout score from 0 to 100, the band they put the company in, the reasons behind them in plain words and the shifts in how it positions itself.
- [Market Series](https://www.fokals.com/data/market-series): One row per metric, dimension, window and week. Each series with its count, the cohort it is measured over, a rate in the metric’s unit, the previous window, growth and an index.

## What an agent can ask

Each question, the dataset that answers it, and the endpoint of the licensed API (https://www.fokals.com/docs/api) that answers it for one company or technology:

- Which technologies does a company run on its website, and what did it add or drop? Technology Stack and Technology Changes; GET /api/v1/companies/{id}/stack.
- Which customers did a vendor win and lose each month, at what churn rate, and to which rivals? Vendor Scorecards; GET /api/v1/technologies/{id}/scorecard.
- How ready is a company for AI agents to find it, read its prices, buy from it and act through it? Agent Readiness and Agent Readiness Changes; GET /api/v1/companies/{id}/readiness.
- Which projects do its job postings state: a move between systems, a first team, a compliance programme, a new country, an AI build? Job Posting Projects; GET /api/v1/companies/{id}/projects.
- Which companies raised or cut prices, added or dropped a plan, or moved to usage-based or per-seat pricing? Technology Changes, category pricing; GET /api/v1/changes.
- What is a company hiring for, at what advertised pay? Job Postings and Hiring Activity; GET /api/v1/companies/{id}/hiring.
- What is a company likely to buy or build next, and on what dated evidence? Intent Scores and Company Signals; GET /api/v1/companies/{id}/intent.
- Is a company showing signs of distress or breaking out this week, and why? Company Health; GET /api/v1/companies/{id}/health.
- Which technologies is a company likely to add next, and how many companies followed the same changes? Likely Next Adoptions and Adoption Sequences; GET /api/v1/companies/{id}/next.
- Which companies are likeliest to add a given technology next? Likely Next Adoptions; GET /api/v1/technologies/{id}/likely-adopters.
- Which companies are in the state my customers were in when they bought, this week? Lookalikes at the buying moment, answered on request; POST /api/v1/lookalikes.

## Documentation

- [Methodology](https://www.fokals.com/docs/methodology): What every dataset holds, how it is collected, dated and refreshed, in overview.
- [Data dictionary](https://www.fokals.com/docs/data-dictionary): Every table and column, with conventions, identifiers and value lists.
- [Sourcing and compliance statement](https://www.fokals.com/docs/sourcing): What is collected and how, what is never collected, opt-out and governance.
- [API reference](https://www.fokals.com/docs/api): Authentication, rate limits, pagination, every endpoint and bulk exports.
- [MCP and API](https://www.fokals.com/docs/connect): Add Fokals to an assistant or call it from your own code: sign-in, keys, prices, limits and every tool.

## About the data and the licence

- [Coverage](https://www.fokals.com/coverage): What the company index and each dataset cover.
- [Delivery](https://www.fokals.com/delivery): The REST API, the bulk exports, the formats and the versioning.
- [Compliance](https://www.fokals.com/compliance): How the data is sourced and governed, and how a company asks to be left out.
- [For investors](https://www.fokals.com/investors): Company data for investment research: security identifiers on every listed company, dated observations, point-in-time by construction.
- [Data licensing](https://www.fokals.com/data-platforms): Licences for embedding company data in a product and for redistribution.
- [Get started](https://www.fokals.com/signup): Create a free account; the first companies are on us.
- [Get sample data](https://www.fokals.com/contact?kind=sample): Sample data on the companies you track, with the data dictionary and the methodology.

## Blog

Notes on company data: how it is collected, structured, licensed and put to work.

- [A company data API response, field by field: an example account record](https://www.fokals.com/blog/company-data-api-response-field-by-field): What a company data API should return for one account, shown on an annotated example response, then checked against Apollo, People Data Labs and Crunchbase.
- [Account-level vs contact-level intent data in B2B, and the GDPR](https://www.fokals.com/blog/account-level-vs-contact-level-intent-data): What an account-level and a contact-level intent record hold, what each lets a B2B team do, and where official guidance says the rules on personal data attach.
- [AI readiness signals: how to read a company website's stance on AI](https://www.fokals.com/blog/ai-readiness-signals-from-company-websites): The AI readiness signals a company website publishes, from crawler blocks and TDMRep to llms.txt and WebMCP, read as dated company data with hiring and intent.
- [Alternative data delivery formats hedge funds expect from a vendor](https://www.fokals.com/blog/alternative-data-delivery-formats-for-hedge-funds): What a hedge fund's data team checks in an alternative data delivery: the route, the file format, the manifest, schema versions, timestamps and corrections.
- [Can you use licensed company data to train a machine learning model?](https://www.fokals.com/blog/training-a-model-on-licensed-company-data): What a data licence must say before company data trains, evaluates or grounds a model, the clauses to ask for, and what official bodies have published.
- [Choosing a lookback window for intent scoring: 30, 60 or 90 days](https://www.fokals.com/blog/lookback-window-for-intent-scoring): What a 30, 60 or 90 day lookback window does to an intent score, how to set it from your sales cycle, and one account worked through all three windows.
- [Data provider attribution: the rules when data is shown in a product](https://www.fokals.com/blog/attribution-when-licensed-data-is-shown-in-a-product): What open licences and API terms ask for when a provider's data appears on your screens, from names and links to placement, with a checklist and a worked screen.
- [Data vendor notice periods for breaking schema changes and API deprecation](https://www.fokals.com/blog/notice-of-breaking-schema-changes-from-a-data-vendor): Which vendor schema changes break a pipeline, how notice is given through versions, the Deprecation and Sunset headers and changelogs, and what to get in writing.
- [Do job postings predict company revenue growth? What official studies show](https://www.fokals.com/blog/do-job-postings-predict-company-growth): What central banks and statistics offices found on job postings as a leading indicator, each study with its sample, and a test to run on dated company data.
- [Firmographic, technographic, hiring and intent data: how they fit](https://www.fokals.com/blog/how-company-data-types-fit-together): The four kinds of company data answer four questions: who a company is, what it runs, where it invests and what it may buy. How to join them on one key.
- [How a technographic data vendor dates a technology adoption](https://www.fokals.com/blog/how-a-technology-adoption-is-dated): The three dates a technographic record can carry, why a first observation is a baseline and not an adoption, and a test against go-live dates you know.
- [How company events are dated: occurrence date or publication time](https://www.fokals.com/blog/how-company-events-are-dated): The dates a company event carries, which one a back-test or an alert should use, how to measure a feed's delay on a sample and the evidence that verifies it.
- [How job postings are classified by occupation taxonomy and seniority level](https://www.fokals.com/blog/how-job-postings-are-classified): How a job posting becomes an occupation, a job function and a seniority level: public classifications, vendor taxonomies, and a hand-grading test for the labels.
- [How long job postings typically stay open, and average time to fill](https://www.fokals.com/blog/how-long-job-postings-stay-open): What official statistics say about how long job postings stay open, how to measure posting duration from open and close dates, and how to read a stale role.
- [How to evaluate a company data vendor in ten questions](https://www.fokals.com/blog/ten-questions-for-a-company-data-vendor): Ten questions to put to any vendor of company data, each with the evidence to ask for and a test to run on sample data, and how Fokals answers all ten.
- [How to measure the lift from buyer intent data with a holdout test](https://www.fokals.com/blog/measuring-lift-from-buyer-intent-data): Measure the lift from buyer intent data with a randomised holdout test: the design, the size of each group, the arithmetic of lift and a pilot checklist.
- [How to run a trial of an alternative dataset: what the agreement covers](https://www.fokals.com/blog/trial-of-an-alternative-dataset): How a fund runs a trial of an alternative dataset: what to fix before delivery, what the trial agreement should cover, and a test plan with pass marks.
- [How to see which companies block AI crawlers in robots.txt](https://www.fokals.com/blog/blocking-ai-crawlers-in-robots-txt): Read a company's robots.txt for its stance on AI crawlers: the tokens, training versus search, how groups match, Content-Signal lines and dated changes.
- [How to test the accuracy of a technographic data provider](https://www.fokals.com/blog/testing-technographic-data-accuracy): A test to run on sample data before you buy technographic data: the match rate on your account list, then precision and recall against a hand-checked set.
- [Intent data providers with an API or data feed, not just a dashboard](https://www.fokals.com/blog/intent-data-by-api-or-data-feed): Which intent data providers document an API or a data feed, in each one's own words, and seven properties a data team should test on a sample before loading it.
- [Introducing Fokals: company intelligence data, refreshed daily](https://www.fokals.com/blog/introducing-fokals): What Fokals is: five datasets on one company index, refreshed daily, delivered by REST API or as bulk files and licensed by written agreement.
- [Material non-public information risk in alternative data: what to ask](https://www.fokals.com/blog/mnpi-risk-in-alternative-data): What the SEC, its staff and one settled order say about MNPI in alternative data, each with its date, and the questions a fund puts to a data vendor.
- [Measuring signal decay in an alternative dataset as funds crowd in](https://www.fokals.com/blog/measuring-signal-decay-in-an-alternative-dataset): A method for measuring signal decay in an alternative dataset: rank correlation by period, a fitted trend, and tests that separate crowding from a data break.
- [RFP questions to ask a buyer intent data vendor](https://www.fokals.com/blog/rfp-questions-for-an-intent-data-vendor): Eighteen RFP questions for a buyer intent data vendor on source, method, privacy, delivery and licence, with the evidence to request and how to grade each answer.
- [Salary information in a job postings dataset: measuring completeness](https://www.fokals.com/blog/salary-information-in-job-postings): How to measure the share of job postings that state pay, what pay transparency rules require and where, and how to bring pay ranges to one annual scale.
- [Same-store cohorts: how the weekly market series stay comparable](https://www.fokals.com/blog/same-store-cohorts-explained): How the Fokals weekly market series are built on same-store cohorts: who is in a cohort, and how count, rate, previous, growth and index are computed.
- [The difference between job postings data and workforce headcount data](https://www.fokals.com/blog/job-postings-data-vs-workforce-headcount-data): Job postings data counts the roles a company advertises and headcount data counts the people it employs. How each maps to BLS measures, with a worked example.
- [The identifiers that make company data joinable](https://www.fokals.com/blog/identifiers-that-make-company-data-joinable): The seven identifiers Fokals data carries, from a stable company key and a domain to ISIN, LEI, FIGI and CIK: what each one names and which join it serves.
- [Types of alternative data used by hedge funds, from card data to web data](https://www.fokals.com/blog/types-of-alternative-data-hedge-funds-use): Four types of alternative data used by hedge funds in official descriptions, and a fifth this post adds: what each observes and how SEC staff describe them.
- [What a data licence covers: internal use, embedding, redistribution](https://www.fokals.com/blog/what-a-data-licence-covers): Internal use, embedding in a product and redistribution: what each use of a data licence allows, what to settle and why Fokals licenses by agreement.
- [What a Fokals intent score gives you, and how to read one](https://www.fokals.com/blog/how-fokals-intent-scores-are-built): What a Fokals intent score delivers: a weekly 0 to 100 score per company and topic, 69 topics in nine groups, the surge flag and the dated evidence.
- [What a Fokals technology row tells you, and how to read it](https://www.fokals.com/blog/how-we-detect-technologies-on-a-website): What a row of Fokals technographic data holds: the technology, its evidence, first-seen and last-seen dates, dated adoptions and removals, and how to read it.
- [What daily updates mean in a job postings API, and how to test a provider](https://www.fokals.com/blog/job-postings-api-with-daily-updates): What daily updates mean in a job postings API: three delays, the date fields that measure them, a seven-day test on sample boards and what four providers document.
- [What is llms.txt? Format, example and which companies publish one](https://www.fokals.com/blog/what-is-llms-txt): What llms.txt is, who proposed it, its format with an example, how to tell a real file from a catch-all page, and what publishing one says about a company.
- [What is WebMCP, and how to track which websites adopt it](https://www.fokals.com/blog/what-is-webmcp): WebMCP lets a web page offer tools to AI agents in the browser. Its two forms, its status in Chrome, how it differs from MCP, and how to read adoption as data.
- [What refresh frequency does technographic data need?](https://www.fokals.com/blog/technographic-data-refresh-frequency): How often technographic data should refresh for each decision it feeds, how to measure the age of a detection on a sample, and what daily adds over monthly.
- [What to check before buying a job postings dataset: six tests](https://www.fokals.com/blog/what-to-check-before-buying-job-postings-data): Six tests to run on a sample before you buy job postings data: duplicates, stale listings, dates, filled fields, the company key and label accuracy.
- [Which technology categories are detected reliably from a company website](https://www.fokals.com/blog/which-technologies-a-website-shows): Which technology categories technographics detect reliably from a company website and its DNS, which only in part, and where job postings are the second source.
- [Who buys job postings data, and what each buyer needs from it](https://www.fokals.com/blog/who-buys-job-postings-data): Five kinds of buyer license job postings data. See what each asks of the same records, in fields, grain and freshness, and how to test a sample for your use.
- [Why intent data fails to improve pipeline: four common reasons](https://www.fokals.com/blog/why-intent-data-fails-to-improve-pipeline): Four reasons intent data adds no pipeline, each with a test to run on your own CRM and a fix: no rule for reps, no evidence, the wrong window, no holdout.
- [Why we read job postings from the company's own board](https://www.fokals.com/blog/reading-job-postings-at-the-source): Why Fokals sources job postings first-party, from each employer's own careers page, and what that means for duplicates, attribution and the dates on a posting.
- [Why we write each table once: point-in-time data by design](https://www.fokals.com/blog/why-we-write-each-table-once): Why Fokals writes each daily and weekly table once: what the rule guarantees, observation times, baselines, the flag on late periods and fixed definitions.

## Use cases

What investment, data, sales and marketing teams build on firmographic, technographic, hiring and intent data.

- [Adding company signals to lead scoring](https://www.fokals.com/use-cases/adding-company-signals-to-lead-scoring): Add company-level fit and timing to a lead score built on engagement: the tables to use, an as-of join, weights, decay and the double counts to avoid.
- [Adding technographic filters to a prospecting tool](https://www.fokals.com/use-cases/technographic-filters-in-a-prospecting-tool): How a product team builds technology, category, first seen and recently added filters on Fokals datasets, with the index design and the definitions to show users.
- [Benchmarking pay and talent demand for workforce planning](https://www.fokals.com/use-cases/benchmarking-pay-and-talent-demand): Build advertised pay ranges and demand by job function and country from postings across companies, with the sample sizes and traps that matter.
- [Benchmarking sales compensation from advertised pay](https://www.fokals.com/use-cases/benchmarking-sales-compensation): How to compare a sales compensation plan with the base pay and on-target earnings employers advertise by role and country, and where advertised pay stops.
- [Building a company knowledge graph from identifiers and events](https://www.fokals.com/use-cases/building-a-company-knowledge-graph): How to model companies, listings, technologies and dated events as graph nodes and edges: the keys, the SQL, the parent roll-up and the links to your own records.
- [Building a point-in-time company dataset for back-tests](https://www.fokals.com/use-cases/point-in-time-data-for-backtesting): How a point-in-time record makes a back-test honest: every observation dated, written once, never revised, with knowledge dates and as-of queries.
- [Building account-based marketing segments from company signals](https://www.fokals.com/use-cases/account-based-marketing-segments-from-signals): Recipes for account-based marketing segments that pair fit, from firmographics and technology stack, with timing from hiring, intent and events, written as queries.
- [Building company change alerts into your product](https://www.fokals.com/use-cases/building-company-change-alerts): How to turn dated website changes and company announcements into alerts: the tables to read, a schema, a dedupe key, a feed bookmark and the wording to use.
- [Building thematic baskets from technology adoption data](https://www.fokals.com/use-cases/thematic-baskets-from-technology-data): Build baskets of listed companies from the technologies on their websites, test them, and rebalance on the dated adoption and removal events in Technology Changes.
- [Catching companies as they enter a new market](https://www.fokals.com/use-cases/catching-companies-entering-a-new-market): Combine website, hiring and announcement signals to see which companies are entering a country, with the datasets, the queries and how to read each trace.
- [Commercial due diligence on a target with public signals](https://www.fokals.com/use-cases/commercial-due-diligence-with-public-signals): How a deal team builds an outside-in view of a target and its competitors from hiring, technology, announcement and funding signals, and reads it for hypotheses.
- [Company data as context for AI agents and copilots](https://www.fokals.com/use-cases/company-data-as-context-for-ai-agents): How to give an AI agent company data it can date and cite: the tools to build, what every result carries, and how to make it say when the data is silent.
- [Company signals in credit research and counterparty monitoring](https://www.fokals.com/use-cases/company-signals-in-credit-research): How a credit analyst can add dated public signals from hiring, company announcements, regulatory disclosures and websites to a borrower watchlist.
- [Defining an ideal customer profile from data](https://www.fokals.com/use-cases/defining-an-ideal-customer-profile-with-data): How to derive an ideal customer profile from won customers: enrich them with company data, measure what separates them from the market, and avoid overfitting.
- [Detecting go-to-market changes from pricing and key pages](https://www.fokals.com/use-cases/detecting-go-to-market-changes-on-websites): Read added and removed pricing, demo and contact pages, new markets and banner promotions as dated events, and confirm a go-to-market move with a second source.
- [Detecting restructuring early from postings and announcements](https://www.fokals.com/use-cases/detecting-restructuring-from-public-signals): How to spot a company cutting back before the numbers show it: the posting pattern in Hiring Activity, the disclosures and newsroom signals, and the false alarms.
- [Enriching company records in your data product](https://www.fokals.com/use-cases/enriching-company-records-in-a-data-product): How a product team adds company data to its own records: match on domain, choose the fields to add, keep them fresh and settle the licence for embedding.
- [Event-driven research with Form 8-K filings and newsrooms](https://www.fokals.com/use-cases/event-driven-research-with-8-k-filings): Build one dated calendar of corporate events from newsroom announcements and Form 8-K filings: the 13 event types, how items map to them and how timing differs.
- [Expansion signals for account management](https://www.fokals.com/use-cases/expansion-signals-for-account-management): How an account team can watch existing customers for new markets, funding, team growth and new initiatives, and turn each into a dated reason to talk.
- [Finding accounts by the technology they run](https://www.fokals.com/use-cases/finding-accounts-by-technology-stack): Build a target account list from website technology data: complements, competitors, categories and public account ids, with the checks to run before sales sees it.
- [Following private funding with SEC Form D filings](https://www.fokals.com/use-cases/following-private-funding-with-form-d): What a Form D filing says, how to read amount offered, amount sold and date of first sale, and how Company Funding delivers these private capital raises.
- [How duplicate job postings are removed across job boards and career sites](https://www.fokals.com/use-cases/deduplicating-job-postings): Why one role appears many times, the keys that identify a single posting, and a daily warehouse load that stays free of duplicates, with the queries to prove it.
- [How to build your own intent signals from public company data](https://www.fokals.com/use-cases/building-intent-signals-from-public-company-data): Build an intent signal of your own from dated company events: which events to count, how to date, weight and fade each one, sum them per account and test them.
- [How to detect a hiring freeze from a company's job postings](https://www.fokals.com/use-cases/detecting-a-hiring-freeze-from-job-postings): The signals in a company's own job postings that mark a hiring freeze, the SQL that measures each against its earlier weeks, and the look-alikes to rule out.
- [How to link private companies and brands to a listed parent company](https://www.fokals.com/use-cases/linking-brands-and-subsidiaries-to-a-listed-parent): Link brands and private subsidiaries to a listed parent company in a dataset with filed ownership records, a dated bridge table and a roll-up that counts once.
- [How to list the companies that recently switched ecommerce platforms](https://www.fokals.com/use-cases/finding-companies-that-switched-ecommerce-platform): A worked query that lists the companies whose commerce platform changed in a window you set, with the tests that tell a migration from a second storefront.
- [How to match technographic data to CRM accounts](https://www.fokals.com/use-cases/matching-technographic-data-to-crm-accounts): Match technographic data to CRM accounts on any warehouse: normalise the domain, run a waterfall of fallback keys, then measure false and missed matches.
- [How to measure hiring velocity from job postings: the formula](https://www.fokals.com/use-cases/measuring-hiring-velocity-from-job-postings): The formula for hiring velocity from dated job postings and headcount, a worked example, and what official statistics offer as a benchmark by company size.
- [How to track enterprise technology spending with alternative datasets](https://www.fokals.com/use-cases/tracking-enterprise-technology-spending-trends): Seven kinds of dataset that track enterprise technology spending, three measures built from adoption and hiring data, and the official statistics for the amounts.
- [Intent data for investors researching demand for a software category](https://www.fokals.com/use-cases/intent-data-for-software-category-demand): How investors read demand for a software category from company-level intent data: the share of a fixed cohort scoring on a topic, with a query and an example.
- [Intent data without alert fatigue: practices for sales reps](https://www.fokals.com/use-cases/intent-signals-without-alert-fatigue): How to stop intent data causing alert fatigue for sales reps: a score threshold, a trigger on change, a weekly cap and dated evidence, with the volumes worked.
- [Keeping a warehouse in step with incremental API sync](https://www.fokals.com/use-cases/incremental-sync-of-company-data): How to keep warehouse tables current from a company data API: a bookmark, a key and upsert per table, rate-limit pacing, and a backfill apart from the daily run.
- [Mapping a partner ecosystem from technology co-occurrence](https://www.fokals.com/use-cases/mapping-a-partner-ecosystem): Find which tools run on the same websites as yours, and rank integration and co-marketing partners by lift and adoption order.
- [Mapping alternative data to a security master](https://www.fokals.com/use-cases/mapping-alternative-data-to-a-security-master): Join company-level data to a security master: which identifiers to use and in what order, how brands map to a parent, why delisted names stay and how to check keys.
- [Measuring AI adoption across listed companies](https://www.fokals.com/use-cases/measuring-ai-adoption-across-listed-companies): Measure AI adoption across a set of listed companies from hiring, tools and announcements, combine the three into one score and avoid the traps of keyword counting.
- [Measuring an installed base and its weekly movement](https://www.fokals.com/use-cases/measuring-a-vendors-installed-base): Count the websites that run a technology, read weekly adds, removals and net change from Market Series, and split the base by industry and country.
- [Measuring remote and hybrid work trends by industry](https://www.fokals.com/use-cases/measuring-remote-work-trends-by-industry): Measure the share of new job postings that are remote, hybrid or on site by industry and country from weekly series, and learn how to read the rate and index.
- [Monitoring supplier and vendor risk with public company signals](https://www.fokals.com/use-cases/monitoring-supplier-risk-with-company-signals): How a procurement team can screen a supplier list for restructuring, leadership change, hiring collapse and site changes using public company signals.
- [Personalising campaigns by technology stack and growth stage](https://www.fokals.com/use-cases/personalising-campaigns-by-stack-and-stage): Choose a message variant for each account from its technology stack, hiring momentum and recent announcements, with the datasets, rules and guardrails to apply.
- [Portfolio company monitoring for private equity teams](https://www.fokals.com/use-cases/portfolio-monitoring-for-private-equity): Design a weekly digest for an operating partner: portfolio companies and their competitors tracked through hiring, site changes, announcements and traffic tier.
- [Powering account scores in a sales platform with licensed signals](https://www.fokals.com/use-cases/account-scores-in-a-sales-platform): How a sales platform can embed weekly company intent scores and their evidence: the join to accounts, what to show, version changes and what the licence must say.
- [Prioritising accounts with company-level intent scores](https://www.fokals.com/use-cases/prioritising-accounts-with-intent-scores): How revenue operations teams rank accounts every week from Fokals intent scores: choose topics, join fit, show the evidence and test the ranking against outcomes.
- [Producing market research from weekly aggregate series](https://www.fokals.com/use-cases/market-research-from-weekly-series): How research and media teams turn weekly market series into findings they can defend: reading cohort, rate, growth and index, then charting and citing them.
- [Reading a competitor's strategy from its hiring](https://www.fokals.com/use-cases/reading-competitor-strategy-from-hiring): What a competitor's job postings reveal about its priorities, markets and tools, the datasets to read each month, and how to read them correctly.
- [Reading technology adoption as a signal of vendor revenue](https://www.fokals.com/use-cases/technology-adoption-as-a-vendor-revenue-signal): Count a listed software vendor's installs and net adds across company websites, and test them against reported customer growth as a dated proxy between results.
- [Recruiting intelligence: finding companies that are hiring now](https://www.fokals.com/use-cases/finding-companies-that-are-hiring-now): How staffing firms and HR-technology sellers build a daily list of companies hiring now by role and location, and read new boards and growth without false leads.
- [Redistributing company data to your customers under licence](https://www.fokals.com/use-cases/redistributing-company-data-under-licence): What to settle before you pass licensed company data to your own customers: scope, attribution, manifests, notice of change and the end of the term.
- [Retrieval-augmented generation over structured company signals](https://www.fokals.com/use-cases/retrieval-over-structured-company-signals): How to index company signals for retrieval: one dated fact per row, company and date filters, an index that grows with the feed, and where SQL beats vectors.
- [Running competitor displacement plays from technology changes](https://www.fokals.com/use-cases/competitor-displacement-from-technology-changes): How to find companies that dropped or swapped a rival tool from dated technology events: four patterns, the play for each, timing windows and the measures to track.
- [Sector hiring trends for macro and thematic research](https://www.fokals.com/use-cases/sector-hiring-trends-for-thematic-research): How to read the weekly hiring series for industries, countries and size bands: rate, growth, index, the 7, 30 and 180-day windows and the cohorts behind them.
- [Sizing a total addressable market with technographic counts](https://www.fokals.com/use-cases/sizing-a-market-with-technographic-counts): Build a bottom-up market size from technology counts: which datasets to count, how to state the denominator and how to read the result.
- [Sourcing private companies for venture capital with growth signals](https://www.fokals.com/use-cases/sourcing-private-companies-for-venture-capital): A worked screen for a venture associate: hiring growth, stack maturity, private capital raises and announcements ranked into a list of private companies to call.
- [Spotting churn risk when a customer removes your technology](https://www.fokals.com/use-cases/churn-risk-from-technology-removals): How a vendor can watch its own tag on customer websites for removals and for a competitor's arrival, with the datasets, the queries and how to read a detection.
- [Territory planning with firmographic and hiring data](https://www.fokals.com/use-cases/territory-planning-with-firmographic-data): How sales operations teams balance territories on weight and not account count, using industry, country, size band and hiring momentum matched to an account list.
- [Timing outreach with hiring signals](https://www.fokals.com/use-cases/timing-outreach-with-hiring-signals): How sales teams turn job postings into outreach triggers: six signals in Fokals hiring data, the play for each, and how to test each play against your own deals.
- [Tracking competitor launches and partnerships at the source](https://www.fokals.com/use-cases/tracking-competitor-launches-at-the-source): Follow what competitors announce on their own newsrooms and feeds: the event types, regulatory disclosures, key pages and site changes, with queries.
- [Tracking sales team build-out as a sign of growth plans](https://www.fokals.com/use-cases/tracking-sales-team-build-out): Read a company's sales hiring week by week: roles by type and segment, advertised pay, new sales countries and the move upmarket, from one weekly dataset.
- [Training and evaluating models on dated company signals](https://www.fokals.com/use-cases/training-models-on-dated-company-signals): How to build feature tables as of a date from company signals, split by time and handle baseline readings, late rows and empty labels without leaking the future.
- [Trigger events for outbound: funding, leadership and launches](https://www.fokals.com/use-cases/trigger-events-for-outbound-sales): The 13 announcement event types as outbound triggers, how a leadership change is read by role, and a simple way to rank triggers by relevance and age.
- [Using hiring data in equity research between earnings](https://www.fokals.com/use-cases/hiring-data-for-equity-research): How to use job postings as a dated, company-level signal between reporting dates: the tables to load, the measures that matter and the traps to avoid.
- [What job postings reveal about the technologies a company is adopting](https://www.fokals.com/use-cases/reading-technology-adoption-from-job-postings): How to read the tools named in job postings as technographic data: the systems a website never shows, five tests for a named tool and a query joining both views.

## Platforms and marketplaces

Guides to the cloud data platforms and data marketplaces where company data is bought, loaded and used.

- [A market dashboard on company signals in Data Studio (Looker Studio)](https://www.fokals.com/platforms/market-dashboards-in-looker-studio): Build a market dashboard from Fokals market series in Data Studio, formerly Looker Studio: a BigQuery view, rate, index and growth charts, and an industry filter.
- [Alternative data due diligence questionnaires, question by question](https://www.fokals.com/platforms/alternative-data-due-diligence-questionnaires): What a standard alternative data due diligence questionnaire asks on sourcing, MNPI, personal data and web collection, and how a vendor answers from its documents.
- [Alternative data on Bloomberg: what buyers should know](https://www.fokals.com/platforms/alternative-data-on-bloomberg): What Bloomberg's own pages say about its alternative data, how the Terminal and Data License carry it, and how to join it to company data you load.
- [Apache Iceberg tables for licensed external data](https://www.fokals.com/platforms/apache-iceberg-tables-for-licensed-data): How to land vendor files as Apache Iceberg tables partitioned by day, load them without duplicates, and use time travel without mistaking it for write-once data.
- [AWS Data Exchange: a guide for buyers of company data](https://www.fokals.com/platforms/aws-data-exchange-for-data-buyers): How AWS Data Exchange works for a buyer of company data: data sets, subscriptions, entitlements, revisions and S3 exports, and loading data delivered direct.
- [AWS Glue pipelines for licensed external data](https://www.fokals.com/platforms/aws-glue-pipelines-for-licensed-data): A worked AWS Glue design for a daily vendor feed in S3: bucket layout, crawler settings, a bookmarked job, a UTC schedule and the checks that catch a bad load.
- [BattleFin: how the alternative data marketplace works](https://www.fokals.com/platforms/battlefin-for-alternative-data-buyers): How BattleFin's Discovery Day events and Ensemble platform work for a data buyer, what its pages say about testing and buying, and a five-question meeting plan.
- [BigQuery sharing (Analytics Hub): a guide for buyers of company data](https://www.fokals.com/platforms/bigquery-analytics-hub-for-data-buyers): How BigQuery sharing, formerly Analytics Hub, works for a data buyer: exchanges, listings, linked datasets, purchase, limits and the questions to ask a provider.
- [Bringing external company signals into HubSpot](https://www.fokals.com/platforms/bringing-external-company-signals-into-hubspot): Create custom company properties, match records on domain and write dated signals through the CRM API in batches of 100: a worked design for your own pipeline.
- [Company signals as context for Amazon Bedrock applications](https://www.fokals.com/platforms/company-signals-with-amazon-bedrock): When an Amazon Bedrock knowledge base fits company signals and when a tool call over your own tables fits, with a worked tool definition and loop.
- [Company signals as grounding data for Vertex AI](https://www.fokals.com/platforms/company-signals-as-grounding-for-vertex-ai): Ground a Gemini model on Fokals data in BigQuery, on the platform formerly called Vertex AI: function calling, a search API or retrieval, with dated, sourced facts.
- [Company-level data and identity platforms: where each fits](https://www.fokals.com/platforms/company-level-data-and-identity-platforms): Person-level identity platforms and company-level data answer different questions. Which jobs each does, how they meet at the account, and where each stops.
- [Data sharing in Oracle Autonomous Database, explained](https://www.fokals.com/platforms/data-sharing-in-oracle-autonomous-database): How Data Share in Oracle Autonomous Database works, what its Delta Sharing support covers, and what versioned and live shares mean for data you license.
- [Databricks Marketplace: a guide for buyers of company data](https://www.fokals.com/platforms/databricks-marketplace-for-data-buyers): What a Databricks Marketplace listing holds, how a buyer gets access, how OpenSharing works underneath, and how to load data a provider delivers direct.
- [Datarade: how the data marketplace works for buyers](https://www.fokals.com/platforms/datarade-for-data-buyers): How Datarade works for data buyers: what search shows, how listings and profiles are built, what samples and requests tell a provider, and how to shortlist.
- [Delta Sharing, explained for data licensing teams](https://www.fokals.com/platforms/delta-sharing-explained): What Delta Sharing, now OpenSharing at Databricks, does, who holds what, and the licence questions that arise when data moves by sharing instead of by file.
- [Eagle Alpha: a guide for alternative data buyers](https://www.fokals.com/platforms/eagle-alpha-for-alternative-data-buyers): What Eagle Alpha's platform, advisory, Surveyor compliance tool and onboarding plan offer a buyer, how a project runs from question to trial, and what stays yours.
- [Exploring bulk company data with DuckDB](https://www.fokals.com/platforms/exploring-bulk-company-data-with-duckdb): Profile a Fokals sample file in DuckDB before you license it: read CSV and JSON Lines correctly, then check grain, dates, nulls, labels and presence.
- [Feature engineering on company signals in Databricks](https://www.fokals.com/platforms/feature-engineering-on-company-signals-in-databricks): Build point-in-time feature tables from Fokals daily and weekly data in Databricks, with as-of joins that keep future information out of a training set.
- [Governing licensed data with Unity Catalog](https://www.fokals.com/platforms/governing-licensed-data-with-unity-catalog): How to use Unity Catalog catalogs, grants, tags, lineage and audit tables to keep licensed company data inside its licence scope, and what they cannot prove.
- [Grounding Azure AI applications on company signals](https://www.fokals.com/platforms/grounding-azure-ai-applications-on-company-data): How to ground a Microsoft Foundry agent or Azure AI application on Fokals company signals with a search index or a SQL tool, and cite the evidence behind answers.
- [How to evaluate company data providers on Datarade](https://www.fokals.com/platforms/evaluating-company-data-providers-on-datarade): A scoring sheet for company data providers found on Datarade: coverage, freshness, sourcing, delivery, licence and a sample test, with queries you can run.
- [HubSpot Breeze Intelligence, explained](https://www.fokals.com/platforms/hubspot-breeze-intelligence-explained): What HubSpot launched as Breeze Intelligence, what its data enrichment fills on a company record today, and what a feed of dated company signals adds to it.
- [Ingesting a vendor API with Fivetran or Airbyte](https://www.fokals.com/platforms/ingesting-a-vendor-api-with-fivetran-or-airbyte): How to build a custom connector for a cursor-paginated vendor API in Fivetran or Airbyte: key, paging, bookmark and rate limits, worked on the Fokals API.
- [Joining licensed company data to CRM tables in Snowflake](https://www.fokals.com/platforms/joining-company-data-to-crm-tables-in-snowflake): Join licensed company data to CRM accounts in Snowflake: normalise domains, build a bridge to the company ID, measure the match rate and publish a view for sales.
- [Listing a data product on Datarade: what providers should know](https://www.fokals.com/platforms/listing-a-data-product-on-datarade): What a Datarade listing holds, who can apply, which publishing policies bind it, how buyer leads arrive and what the plans cost, as Datarade states them.
- [LiveRamp Data Marketplace, explained for B2B data teams](https://www.fokals.com/platforms/liveramp-data-marketplace-explained): What the LiveRamp Data Marketplace is for, how buyers work in it, and where company-level signals loaded from your own pipeline answer a different question.
- [Loading company data into Amazon Redshift](https://www.fokals.com/platforms/loading-company-data-into-amazon-redshift): Load Fokals company data from S3 into Amazon Redshift: COPY for JSON Lines and CSV, SUPER columns for JSON cells, and a daily merge that is safe to re-run.
- [Loading company data into BigQuery](https://www.fokals.com/platforms/loading-company-data-into-bigquery): Load Fokals files into BigQuery from Cloud Storage with JSON columns, day partitions and an insert-only MERGE for daily files, and check each load.
- [Loading company data into Databricks with Auto Loader](https://www.fokals.com/platforms/loading-company-data-into-databricks): Land a vendor's company data in a Unity Catalog volume, ingest it with Auto Loader or COPY INTO, and build bronze, silver and gold tables with MERGE.
- [Loading company data into Oracle Autonomous Database](https://www.fokals.com/platforms/loading-company-data-into-oracle-autonomous-database): Load company CSV and JSON files from object storage into Oracle Autonomous Database with DBMS_CLOUD: credential, table, format options, load checks and JSON queries.
- [Loading company data into Snowflake from files and an API](https://www.fokals.com/platforms/loading-company-data-into-snowflake): How to load Fokals company data into Snowflake from CSV bulk files and API pages: stages, COPY INTO, VARIANT for JSON cells and a daily MERGE, with worked SQL.
- [Microsoft Fabric: bringing external company data into OneLake](https://www.fokals.com/platforms/microsoft-fabric-for-external-company-data): How to land Fokals bulk files and API output in a Microsoft Fabric lakehouse or warehouse: the choice, the landing folders, the load into Delta tables, the schedule.
- [Modelling licensed company data with dbt](https://www.fokals.com/platforms/modelling-licensed-company-data-with-dbt): How to declare Fokals tables as dbt sources, stage them, test keys, versions and freshness, and why write-once tables need no snapshots, with config and queries.
- [Neudata: how funds use it to find alternative data](https://www.fokals.com/platforms/neudata-for-alternative-data-buyers): What Neudata's scouting service includes, how a fund moves from discovery to compliance checks to a trial, and what a scouting report leaves for the buyer to test.
- [OneLake shortcuts and external data sharing in Microsoft Fabric](https://www.fokals.com/platforms/onelake-shortcuts-and-external-data-sharing): What OneLake shortcuts and external data sharing do in Microsoft Fabric, who can read the data through each, and what that means for a licence on company data.
- [Open:FactSet Marketplace: a guide for data buyers](https://www.fokals.com/platforms/open-factset-marketplace-for-data-buyers): What FactSet's own documents say about the Open:FactSet Marketplace, how a purchase works, how it maps data to its identifiers, and how to measure the same mapping.
- [Power BI reports on company signals](https://www.fokals.com/platforms/power-bi-reports-on-company-signals): A Power BI model for Fokals hiring, technology and market series tables: the star schema, measures that add up, refresh, and row-level security for licence scope.
- [Querying company data on Amazon S3 with Athena](https://www.fokals.com/platforms/querying-company-data-on-s3-with-athena): Query Fokals bulk files that you load into your own S3 bucket with Athena: external tables, day partitions with projection, and a Parquet conversion with CTAS.
- [S&P Global Marketplace: a guide for data buyers](https://www.fokals.com/platforms/s-and-p-global-marketplace-for-data-buyers): What S&P Global says its Marketplace is, how tiles, Blueprints and the Query Library help a buyer evaluate data, and how to write the same kind of query yourself.
- [Salesforce Data Cloud and external company data](https://www.fokals.com/platforms/salesforce-data-cloud-and-external-company-data): Salesforce now calls Data Cloud Data 360. How external company data gets in by files, API or zero copy, and how to match it to your Account records.
- [SAP Datasphere: adding external company data to business data](https://www.fokals.com/platforms/sap-datasphere-for-external-company-data): How to add Fokals company data to SAP business data in SAP Datasphere: a space for licensed tables, loading, a mapping to business partners and the enrichment views.
- [Serving company data to AI agents over Model Context Protocol](https://www.fokals.com/platforms/company-data-for-agents-over-model-context-protocol): How to wrap a company-data REST API as Model Context Protocol tools: which tools to expose, how to page, where the key lives and how to fail so an agent can recover.
- [Serving company signals from ClickHouse](https://www.fokals.com/platforms/serving-company-signals-from-clickhouse): Engines, ordering keys and projections for Fokals event feeds in ClickHouse, with the product queries each one serves and a load pattern that keeps results correct.
- [Snowflake Marketplace: a guide for buyers of company data](https://www.fokals.com/platforms/snowflake-marketplace-for-data-buyers): How Snowflake Marketplace listings, trials, billing and sharing work for a buyer of company data, what to ask a provider, and how data delivered direct is loaded.
- [Snowflake Secure Data Sharing, explained for licensing teams](https://www.fokals.com/platforms/snowflake-secure-data-sharing-explained): What shares, listings and reader accounts do in Snowflake, who pays, how access is revoked and which licence terms a share cannot carry, for licensing teams.
- [The SAP Datasphere Data Marketplace for data buyers](https://www.fokals.com/platforms/sap-datasphere-data-marketplace-for-buyers): How the Data Marketplace in SAP Datasphere lists, licenses and delivers third-party data, what a product page tells a buyer, and how outside data is loaded.
- [Using company signals with Snowflake Cortex AI functions](https://www.fokals.com/platforms/company-signals-with-snowflake-cortex): Summarise and search company announcements in Snowflake with Cortex AI functions and Cortex Search, keep each line traceable to its source, and govern use.
- [What alternative data scouts look for in a new dataset](https://www.fokals.com/platforms/what-alternative-data-scouts-look-for): The five tests a scout puts to a new dataset: history, coverage, mapping, point-in-time behaviour and compliance, drawn from Neudata and Eagle Alpha.
- [What it takes to list a data product on Snowflake Marketplace](https://www.fokals.com/platforms/listing-a-data-product-on-snowflake-marketplace): What a provider needs to list a data product on Snowflake Marketplace: terms, profile, listing fields, review, regions and duties, as Snowflake documents them.

## Comparisons

Side-by-side comparisons of data platforms, marketplaces and approaches to company data.

- [Alternative data providers of company-level web data mapped to tickers](https://www.fokals.com/compare/alternative-data-providers-of-company-web-data): Thinknum, Similarweb, LinkUp and Fokals side by side from their own documentation: what each observes, its tie to a listed security, its history and delivery.
- [API vs bulk files for company data delivery](https://www.fokals.com/compare/api-vs-bulk-files-for-company-data): When to take company data through a REST API and when as bulk files in CSV or JSON Lines: the jobs each route fits, the failure modes and a worked split.
- [AWS Data Exchange vs Databricks Marketplace](https://www.fokals.com/compare/aws-data-exchange-vs-databricks-marketplace): How AWS Data Exchange and Databricks Marketplace differ for a buyer of company data: what is listed, how access is granted, who bills and how the data arrives.
- [BigQuery sharing (formerly Analytics Hub) vs Snowflake Marketplace](https://www.fokals.com/compare/bigquery-analytics-hub-vs-snowflake-marketplace): BigQuery sharing, formerly Analytics Hub, and Snowflake Marketplace compared for a data buyer: what you receive, how you pay, regions, copy controls and history.
- [Building crawlers vs licensing company data](https://www.fokals.com/compare/building-crawlers-vs-licensing-company-data): Build company-data crawlers with Scrapy and Playwright, or license a feed: what each takes in engineering, upkeep, conduct, structure and labelling.
- [Company signals vs third-party intent data](https://www.fokals.com/compare/company-signals-vs-third-party-intent-data): How intent from what people read, as Bombora and G2 document it, compares with company signals in what each delivers, and how to rank accounts on both.
- [CSV vs JSON Lines vs Parquet for bulk data delivery](https://www.fokals.com/compare/csv-vs-json-lines-vs-parquet): CSV, JSON Lines and Apache Parquet compared for receiving a bulk dataset: types, nested values, size, failure modes and how to convert a delivery to Parquet.
- [Data marketplace vs direct licensing](https://www.fokals.com/compare/data-marketplace-vs-direct-licensing): Buying company data through Datarade, Snowflake Marketplace or AWS Data Exchange compared with a direct licence: discovery, procurement, terms and delivery.
- [Databricks vs Microsoft Fabric for external company data](https://www.fokals.com/compare/databricks-vs-microsoft-fabric): How Databricks and Microsoft Fabric each build a lakehouse, share data across organisations and govern licensed tables, with one vendor file loaded on each.
- [Datarade vs Snowflake Marketplace: two kinds of marketplace](https://www.fokals.com/compare/datarade-vs-snowflake-marketplace): Datarade helps a buyer find and compare data providers. Snowflake Marketplace delivers and bills data inside Snowflake. What each one settles, stage by stage.
- [Delta Sharing vs Snowflake Secure Data Sharing](https://www.fokals.com/compare/delta-sharing-vs-snowflake-secure-data-sharing): Delta Sharing, now OpenSharing, is an open protocol. Snowflake Secure Data Sharing works between Snowflake accounts. Who can receive each, and what revoking does.
- [Eagle Alpha vs BattleFin for alternative data sourcing](https://www.fokals.com/compare/eagle-alpha-vs-battlefin): Eagle Alpha and BattleFin compared from their own pages: a dataset catalogue with advisory and compliance tools, and an event series built on one-to-one meetings.
- [Firmographic vs technographic data](https://www.fokals.com/compare/firmographic-vs-technographic-data): Firmographic data says who a company is and technographic data says what it runs. Where each comes from, how fast each changes, and how to segment on both.
- [Fivetran vs Airbyte for ingesting a vendor API](https://www.fokals.com/compare/fivetran-vs-airbyte-for-vendor-apis): Fivetran and Airbyte compared for a custom connector to a vendor API: who runs it, Connector SDK against Connector Builder, bookmarks, billing units and licence.
- [ISIN vs LEI vs FIGI: company and security identifiers compared](https://www.fokals.com/compare/isin-vs-lei-vs-figi): An ISIN names a security, an LEI a legal entity and a FIGI an instrument down to its trading venue. Who issues each, on what terms, and how to check and join them.
- [Job postings data providers compared: sources, records and tickers](https://www.fokals.com/compare/job-postings-data-providers-compared): Lightcast, Revelio Labs, LinkUp, Coresignal, People Data Labs and Fokals compared on where postings come from, what a record carries and how it reaches a ticker.
- [Microsoft Fabric vs Snowflake for external company data](https://www.fokals.com/compare/microsoft-fabric-vs-snowflake): Microsoft Fabric and Snowflake compared for a team receiving vendor files: where files land, COPY INTO on each, JSON cells, billing units and running both together.
- [Neudata vs Eagle Alpha for alternative data discovery](https://www.fokals.com/compare/neudata-vs-eagle-alpha): What Neudata and Eagle Alpha each say they offer a fund looking for alternative data: scope, research, compliance tools, events and the terms for vendors.
- [Snowflake Marketplace vs AWS Data Exchange](https://www.fokals.com/compare/snowflake-marketplace-vs-aws-data-exchange): How Snowflake Marketplace and AWS Data Exchange differ for buyers and providers: what a listing holds, how it is delivered, how it is billed and who can sell.
- [Snowflake vs BigQuery for external company data](https://www.fokals.com/compare/snowflake-vs-bigquery): How Snowflake and BigQuery each load a vendor's CSV and JSON Lines files, hold JSON cells, receive shared data and charge for the work, with worked SQL.
- [Snowflake vs Databricks for licensed company data](https://www.fokals.com/compare/snowflake-vs-databricks): How Snowflake and Databricks each load a vendor's CSV and JSON files, hold JSON cells, share, govern and run AI functions, with worked SQL on one table.
- [Technographic data providers for B2B sales intelligence: a comparison](https://www.fokals.com/compare/technographic-data-providers-compared): What BuiltWith, Wappalyzer, HG Insights, 6sense, ZoomInfo and Fokals document about their technographic data: evidence, dates, delivery and join keys.
- [Technographic data vs intent data](https://www.fokals.com/compare/technographic-data-vs-intent-data): Technographic data shows what a company runs and intent data shows when it may buy. How the two differ, where they meet, and a query that ranks accounts on both.
- [Why intent data scores differ between vendors for the same account](https://www.fokals.com/compare/why-intent-scores-differ-between-vendors): Why two intent vendors score one account differently: what Bombora, 6sense and G2 each measure, the baseline behind each surge, and a worked example.

## Alternatives

How Fokals compares with other sources of company data, and what each one is built for.

- [6sense alternatives for account intent signals](https://www.fokals.com/alternatives/6sense-alternatives): How 6sense builds intent scores and buying stages, what the platform is built for, and how to score accounts in your own stack on a feed of dated company signals.
- [Alternatives to bidstream-based intent data, source by source](https://www.fokals.com/alternatives/alternatives-to-bidstream-intent-data): Four kinds of source that can stand in for bidstream intent data, what each observes and documents, the questions to ask on cookies, and a test to run first.
- [Alternatives to building your own company data crawlers](https://www.fokals.com/alternatives/alternatives-to-in-house-web-scraping): What an in-house crawler on Scrapy and Playwright takes to run, and what a managed scraping service, an open dataset or a licensed feed takes off your hands.
- [Bombora alternatives for B2B intent data](https://www.fokals.com/alternatives/bombora-alternatives): How Bombora's co-operative intent data compares with weekly intent scores that come with their evidence, what each is built for, and how to run both.
- [BuiltWith alternatives for technographic data](https://www.fokals.com/alternatives/builtwith-alternatives): BuiltWith profiles websites for the technologies they run. This guide compares its published scope, delivery and licence with company-level alternatives.
- [Clearbit alternatives after the move to HubSpot](https://www.fokals.com/alternatives/clearbit-alternatives): Where Clearbit stands under HubSpot according to both companies' own pages, which vendors enrich company records by API, and how to rebuild a record yourself.
- [Coresignal alternatives for company and jobs data](https://www.fokals.com/alternatives/coresignal-alternatives): What Coresignal covers across company, employee and jobs data, which sources fit each part of a workload, and how its company and jobs data map to Fokals datasets.
- [Crunchbase alternatives for company data licensing](https://www.fokals.com/alternatives/crunchbase-alternatives): What Crunchbase licenses and on what terms, four other private-market databases, and where dated company signals and reported capital raises fit beside them.
- [Demandbase alternatives for account intelligence data](https://www.fokals.com/alternatives/demandbase-alternatives): Demandbase is an account-based platform with its own intent and identification data. For teams that run their own stack, here is how the alternatives compare.
- [Dun & Bradstreet alternatives for company signals](https://www.fokals.com/alternatives/dun-and-bradstreet-alternatives): What Dun & Bradstreet covers according to its own pages, which sources fit each part of it, and how to add dated company signals beside D&B records.
- [HG Insights alternatives for technology intelligence](https://www.fokals.com/alternatives/hg-insights-alternatives): HG Insights covers installed technology behind the firewall, IT spend and buyer intent. This guide sets its published scope beside dated, company-level alternatives.
- [LinkUp alternatives for job market data](https://www.fokals.com/alternatives/linkup-alternatives): What LinkUp's job market data covers, how its job records line up with Fokals postings field by field, and which other sources fit which use.
- [People Data Labs alternatives for company data](https://www.fokals.com/alternatives/people-data-labs-alternatives): What People Data Labs documents for its company dataset, what each product is built for, and how its company fields map to company-level datasets on one index.
- [PitchBook alternatives for tracking private company momentum](https://www.fokals.com/alternatives/pitchbook-alternatives): What PitchBook covers, which other databases cover private deals and investors, and how to track a private company's momentum from dated operating signals.
- [PredictLeads alternatives for company signals](https://www.fokals.com/alternatives/predictleads-alternatives): PredictLeads and Fokals compared dataset by dataset as each documents it: what each is built for, the records, delivery and licensing, and one alert built on both.
- [Revelio Labs alternatives for workforce data](https://www.fokals.com/alternatives/revelio-labs-alternatives): What Revelio Labs models from professional profiles and job postings, four other workforce sources, and what daily data on the roles companies publish answers.
- [TheirStack alternatives for jobs and technographic data](https://www.fokals.com/alternatives/theirstack-alternatives): TheirStack infers technology use from job postings. How that differs from website detection, what each method shows, and a query that tests one against the other.
- [Thinknum alternatives for web-sourced alternative data](https://www.fokals.com/alternatives/thinknum-alternatives): What Thinknum's own site says it offers, what it is built for, four other sources of web-sourced company data, and a test for running two feeds together.
- [Veridion alternatives for web-sourced company data](https://www.fokals.com/alternatives/veridion-alternatives): What Veridion covers according to its own pages, what it is built for, and how other sources of web-sourced company data differ in scope and licence.
- [Wappalyzer alternatives for technology detection data](https://www.fokals.com/alternatives/wappalyzer-alternatives): Wappalyzer looks up the technologies behind a website. This guide compares its published scope, delivery and licence with datasets built for products and research.
- [ZoomInfo alternatives for company-level data](https://www.fokals.com/alternatives/zoominfo-alternatives): ZoomInfo is a go-to-market platform with contact and company data. For teams that want company-level signals as a data feed, this guide compares the options.

## Glossary

The terms of company data, defined: firmographics, technographics, intent, identifiers, licensing and delivery.

- [Account scoring](https://www.fokals.com/glossary/account-scoring): What account scoring is, how fit and timing combine into a ranking of companies, how to test a score against outcomes, and where intent scores fit in.
- [Account-based marketing (ABM)](https://www.fokals.com/glossary/account-based-marketing): What account-based marketing is, how a programme runs from target list to measured accounts, and which company-level data it needs.
- [Alternative data](https://www.fokals.com/glossary/alternative-data): Alternative data is information from outside financial statements and market prices that investors use in research. What it is used for and what to check first.
- [Applicant tracking system (ATS)](https://www.fokals.com/glossary/applicant-tracking-system): What an applicant tracking system is, why its public job board is the source of job posting data, and how a change of system can look like a hiring freeze.
- [Bulk export](https://www.fokals.com/glossary/bulk-export): What a bulk export is, when files serve better than API calls, what to check on arrival, and how Fokals delivers its datasets as bulk files.
- [Central Index Key (CIK)](https://www.fokals.com/glossary/central-index-key): A CIK is the number the SEC assigns to each EDGAR filer. What it identifies, why it is not a security code, how to format it and how to join filings on it.
- [Company identifier](https://www.fokals.com/glossary/company-identifier): What a company identifier is, what LEI, ISIN, FIGI, MIC and CIK each identify, how to choose a join key, and the identifiers that Fokals carries.
- [Corporate hierarchy](https://www.fokals.com/glossary/corporate-hierarchy): What a corporate hierarchy is, how parents, subsidiaries and brands link, how to roll data up to a group without errors, and how Fokals carries the link.
- [Cursor pagination](https://www.fokals.com/glossary/cursor-pagination): How cursor pagination returns long API results in pages, why it is steadier than offset paging, and how to resume a download from a saved cursor.
- [Data coverage](https://www.fokals.com/glossary/data-coverage): What data coverage is, how to measure it as a match rate and a fill rate on your own list, and how Fokals states coverage dataset by dataset.
- [Data dictionary](https://www.fokals.com/glossary/data-dictionary): What a data dictionary holds, how to read one before loading a dataset, and the checks that show whether the files you receive match it.
- [Data enrichment](https://www.fokals.com/glossary/data-enrichment): What data enrichment is, how a run works from match to refresh, which company fields to add and how often to refresh them, and the mistakes to avoid.
- [Data freshness](https://www.fokals.com/glossary/data-freshness): What data freshness is, how it differs from refresh frequency and latency, how to measure the age of an observation, and how often each Fokals dataset is refreshed.
- [Data lakehouse](https://www.fokals.com/glossary/data-lakehouse): What a data lakehouse is, how its table layer differs from a warehouse and a lake, how to land vendor files in one, and why time travel is not point-in-time data.
- [Data licence](https://www.fokals.com/glossary/data-licence): A data licence is the contract that sets what a buyer may do with a dataset. The three uses, the terms to settle and how licence terms travel with the data.
- [Data manifest](https://www.fokals.com/glossary/data-manifest): What a data manifest lists, how to check a delivery against it before loading, and why to keep it afterwards as the record of what you received.
- [Data marketplace](https://www.fokals.com/glossary/data-marketplace): What a data marketplace is, the two kinds a buyer meets, what a listing does not settle, and how licensing and delivery work when data is bought directly.
- [Data provenance](https://www.fokals.com/glossary/data-provenance): What data provenance is, what a provenance record should hold, why due diligence and licences depend on it, and how Fokals records source and observation time.
- [Data sharing](https://www.fokals.com/glossary/data-sharing): What data sharing means on cloud data platforms, how a share differs from files and APIs, and what it changes in a data licence and in how you load data.
- [Data warehouse](https://www.fokals.com/glossary/data-warehouse): What a data warehouse is, how its tables are organised by grain, and how to load licensed company data into one, with a note on what Fokals delivers.
- [Domain matching](https://www.fokals.com/glossary/domain-matching): How domain matching joins company records on a normalised website domain, how to normalise one, where it fails, and how Fokals data carries domains.
- [Due diligence questionnaire (DDQ)](https://www.fokals.com/glossary/due-diligence-questionnaire): A DDQ is the list of questions a buyer sends a vendor before a contract. What a data DDQ asks, which standard forms exist and how a vendor answers with documents.
- [Entity resolution](https://www.fokals.com/glossary/entity-resolution): What entity resolution is, how matching proceeds from identifiers to domains to names, how to measure its errors, and how Fokals ties records to a company.
- [FIGI](https://www.fokals.com/glossary/figi): FIGI, the Financial Instrument Global Identifier, is an open 12-character code with three levels. What each level identifies and how it differs from an ISIN.
- [Firmographic data](https://www.fokals.com/glossary/firmographic-data): Firmographic data describes a company by industry, size, location and ownership. What it holds, how teams use it to segment markets and where it comes from.
- [First-party data](https://www.fokals.com/glossary/first-party-data): What first-party data is, how it differs from third-party data, and what a first-party source means when a vendor describes where its data comes from.
- [Form 8-K](https://www.fokals.com/glossary/form-8-k): Form 8-K is the report a US public company files with the SEC to disclose a significant event. Its items, the four-business-day deadline and how items map to events.
- [Form D](https://www.fokals.com/glossary/form-d): Form D is the notice filed with the SEC when a company sells securities under a Regulation D exemption. What it contains, the 15-day deadline and what it omits.
- [Hiring data](https://www.fokals.com/glossary/hiring-data): Hiring data is information drawn from job postings: roles, places, seniority, pay and dates. How it is collected, the mistakes to avoid and where it is kept.
- [Ideal customer profile (ICP)](https://www.fokals.com/glossary/ideal-customer-profile): What an ideal customer profile is, how to build one from your best customers and company data, and how to test it so that it does not overfit.
- [Incremental sync](https://www.fokals.com/glossary/incremental-sync): How incremental sync keeps a local copy current by fetching only what is new: the bookmark, a repeatable load, and the traps to avoid.
- [Installed base](https://www.fokals.com/glossary/installed-base): What an installed base is, how to estimate a vendor's from website technology data, and why a count of websites is not a count of customers.
- [Intent data](https://www.fokals.com/glossary/intent-data): Intent data is a set of signals that a company is researching or preparing to buy. The kinds of source, how to read a score and what Fokals delivers with each one.
- [Intent surge](https://www.fokals.com/glossary/intent-surge): What an intent surge is, why a company is compared with its own baseline rather than a fixed cut-off, what the Fokals surge flag marks and how to read one.
- [ISIN](https://www.fokals.com/glossary/isin): An ISIN is a 12-character code that identifies a security under ISO 6166. Its structure, what it does and does not identify, how to check it and how to join on it.
- [Job function](https://www.fokals.com/glossary/job-function): What a job function is, how it differs from title, department and seniority, how it appears in Fokals hiring data, and how to read a function share.
- [JSON Lines](https://www.fokals.com/glossary/json-lines): What the JSON Lines format is, how it differs from JSON and CSV, how to read a file safely, and the mistakes that break a load.
- [Label version](https://www.fokals.com/glossary/label-version): What a label version is and why label definitions are published under a name. How to work with two versions in one table, and how Fokals data carries them.
- [Lead scoring](https://www.fokals.com/glossary/lead-scoring): What lead scoring is, how fit points and behaviour points add up, how company data improves the fit side, and why scores need decay and testing.
- [LEI](https://www.fokals.com/glossary/lei): An LEI is a 20-character code that identifies a legal entity under ISO 17442. What it records, how it is built and checked, and how it links securities to issuers.
- [Look-ahead bias](https://www.fokals.com/glossary/look-ahead-bias): Look-ahead bias is an error in a back-test that uses information not yet available on the simulated date. How it gets in, how to find it and the checks to run.
- [Market identifier code (MIC)](https://www.fokals.com/glossary/market-identifier-code): A market identifier code is the four-character ISO 10383 code of a trading venue. Operating and segment MICs, how to pair a MIC with a ticker and how to join on it.
- [Material non-public information (MNPI)](https://www.fokals.com/glossary/material-non-public-information): MNPI is information about a company that is important to investors and not yet public. The two tests, how data teams check a source and how sourcing is recorded.
- [On-target earnings (OTE)](https://www.fokals.com/glossary/on-target-earnings): What on-target earnings means in a sales job advert, how base and variable pay combine, and how advertised OTE is read in job data without mistaking it for pay.
- [Point-in-time data](https://www.fokals.com/glossary/point-in-time-data): Point-in-time data stores each value with the date it became known, so a past date returns what was available then. How it works and how to query and test it.
- [Rate limit](https://www.fokals.com/glossary/rate-limit): What an API rate limit is, how minute and daily limits work together, how to plan a load against them and how to back off after a 429 response.
- [Redistribution licence](https://www.fokals.com/glossary/redistribution-licence): What a redistribution licence allows, how it differs from internal-use and embedding licences, and what to settle before passing company data to customers.
- [robots.txt](https://www.fokals.com/glossary/robots-txt): What robots.txt is, how its Allow and Disallow groups are matched under RFC 9309, what the file does not do, and how a crawler that obeys it behaves.
- [Same-store cohort](https://www.fokals.com/glossary/same-store-cohort): A same-store cohort is the fixed group of units present throughout a comparison, so growth ignores units joining or leaving. How to calculate it, with an example.
- [Security master](https://www.fokals.com/glossary/security-master): A security master is the reference table of every security a firm trades or analyses, with identifiers and history. What it holds and how to join data to it.
- [Seniority level](https://www.fokals.com/glossary/seniority-level): What seniority level means in job data, how it is read from a posting, the nine levels used to label postings, and the mistakes that distort it.
- [Survivorship bias](https://www.fokals.com/glossary/survivorship-bias): Survivorship bias is the error of studying only the companies that still exist. Where it appears, how to avoid it in a back-test and how delisted names are kept.
- [Tag manager](https://www.fokals.com/glossary/tag-manager): What a tag manager is, how its containers, tags and triggers work, and why reading them matters for complete technology detection and for dating a removal.
- [Technographic data](https://www.fokals.com/glossary/technographic-data): Technographic data records the technologies a company uses, found in public code and records. How detection works, how to read a detection and how changes are dated.
- [Technology churn](https://www.fokals.com/glossary/technology-churn): What technology churn is, how to calculate gross and net churn from website data, and why a removed tag is a signal to investigate, not a cancelled contract.
- [Third-party data](https://www.fokals.com/glossary/third-party-data): What third-party data is, how it is used, which questions to put to a supplier about source, conduct, personal data and licence, and how Fokals data fits the term.
- [Total addressable market (TAM)](https://www.fokals.com/glossary/total-addressable-market): What total addressable market means, how to build it bottom-up from a count of companies and a price, and where company data helps most.
- [Traffic rank](https://www.fokals.com/glossary/traffic-rank): What a traffic rank is, how rank buckets differ from exact positions, why a rank is not traffic volume, and how the monthly traffic tier appears in Fokals data.
- [Trigger event](https://www.fokals.com/glossary/trigger-event): What a trigger event is, how funding, leadership and other company changes create a reason to contact an account, and how to check a trigger before acting.
- [Web crawler](https://www.fokals.com/glossary/web-crawler): What a web crawler is, how it works, how crawling differs from scraping, what responsible conduct looks like and how crawled company data reaches a buyer.