This guide is for a product or data lead who builds on Veridion, or has it on a shortlist, and wants to know what else supplies company data read from the web. It sets out what Veridion covers according to its own pages, what it is built for, how Fokals differs in coverage and in the shape of a record, and how to test any of these sources on your own records.
Start with what each is built for. Veridion's coverage page counts more than 500 million registered legal entities and more than 180 million operational companies across 250 countries and territories. Its pages describe legal-entity records, physical locations, products and services, sustainability data and corporate groups, delivered into Snowflake, BigQuery and Databricks as well as by API. It is built for resolving a company of any size in any country and for finding companies by what they make. Fokals is built for what a company is doing now: technographic, hiring, intent and announcement data, every observation dated, on listed companies worldwide, the brands they own and verified private companies.
What Veridion offers, as its pages state
Veridion's methodology page describes a pipeline it runs itself. Its crawlers gather pages from the open and deep web, registry ingestion and public data assets are added, a language model turns pages into structured company records, and entities are resolved and refreshed without pause. Its API page says the graph is recrawled all the time and that each value is stamped with its own last-updated time.
The knowledge graph page lists the attribute groups. Among them are company information, locations, company size, industry and insurance classifications, legal information, business contact information, corporate groups, technology insights, products and services, sustainability scores and key people. This is firmographic data in the broad sense, with much besides.
Veridion also describes signals. Its revenue operations page lists hiring velocity changes, technographic adoption, product launches, certification gains, executive moves and operational news, and says they function as buying-trigger signals where appropriate and are not intent data in the search-keyword behavioural sense. Fokals intent scores are likewise built from what a company itself does in public, and every score carries the dated signals behind it.
For delivery, its API page lists Match & Enrich, Digital Footprint, Search, Discovery, Corporate Groups, Location and ESG, and the delivery page lists batch files in CSV, JSON or Parquet as a one-time backfill, on a schedule or as incremental updates, and third-party delivery through partners including Snowflake, BigQuery, Databricks, Datarade and Nomad Data. The Match & Enrich documentation says a request needs the company's name plus at least one further identifier, such as an address, a website, a registry ID or a phone number.
The pricing page publishes no fixed prices and names three packages, Data Foundations, Company Intelligence and Strategic Program, scoped by coverage, usage, delivery method and services. Veridion's terms of service grant use for the customer's internal business purposes and say that commercial use, public-facing use or distribution of its data needs a formal agreement with Veridion.
Why teams look for an alternative
The reasons are about scope and fit. Each is a need to put to any source, Veridion and Fokals included.
- Listed-company keys. A research team joins on ISIN, FIGI or ticker with MIC, and needs the records of a brand to carry the identifiers of its listed parent.
- A record that is never revised. A back-test needs each observation dated and each daily or weekly dataset written once, which is what makes a series point-in-time.
- Postings as records. A product that shows open roles needs one record for each posting, with its job function, seniority and advertised pay.
- A score with its evidence. An account-scoring feature needs a number for each topic and the signals behind it.
- Licence fit. Terms for embedding in a product or for redistribution, agreed directly with the vendor.
- Another kind of data. Credit scores, records from official registers and profiles of people are offered by other vendors, listed below.
Veridion and Fokals side by side
The Veridion column repeats what the pages linked above state. The Fokals column follows the data dictionary.
| Veridion | Fokals | |
|---|---|---|
| Companies covered | More than 500M registered legal entities and more than 180M operational companies, as its coverage page states | Listed companies worldwide, the brands they own and verified private companies, each resolved to one company ID |
| Scope | Company profiles: legal information, locations, classifications, corporate groups, products and services, sustainability, technology, key people | Technographic, hiring, intent and announcement data, with weekly Market Series |
| Sources | Web crawling, registry ingestion and public data assets | First-party company sources and public records, processed in-house |
| How change is kept | A graph recrawled continuously; every value carries its last-updated time | Every observation dated; daily and weekly datasets written once after the period closes and never revised |
| Identifiers | A Veridion ID, with LEI, VAT ID and registry IDs in the match response | A stable company ID, with ticker, MIC, ISIN, LEI, share-class FIGI and CIK on a listed company |
| Delivery | APIs (Match & Enrich, Search, Discovery and others); batch files in CSV, JSON or Parquet; partners including Snowflake, BigQuery, Databricks, Datarade and Nomad Data | REST API of 25 endpoints; bulk exports in JSON, JSON Lines or CSV with a manifest, delivered direct |
| Licensing | No fixed prices published; internal use under its terms of service, other uses by formal agreement | Written agreement for internal use, embedding in a product or redistribution |
Testing a source on your own records
No table settles a choice between sources that read the web. A sample does, and the steps below work for Veridion, for Fokals and for any vendor named further down.
- Draw a stratified sample. Take about 500 of your own records, spread across the kinds of company your product serves: listed companies, their brands and subsidiaries, and the private companies you know well.
- Send each source what it matches on. Veridion's documentation asks for a company name and one more identifier. For Fokals, match on the website domain, or on ISIN, LEI, FIGI or ticker with MIC for a listed company.
- Measure three things in each stratum. The match rate, the share of matched records with a value in each field you would show, and the age of each value.
- Check a subsample by hand. Open each company's own website and count how many values agree, field by field.
For the Fokals side, this query gives the match and the dates of observation in one pass. A matched record comes back with its company ID, so the share of records with one in each stratum is the match rate.
select
s.record_id,
t.company_id,
count(t.technology) as technologies,
min(t.first_seen_at) as earliest_first_seen,
max(t.last_seen_at) as latest_reading
from sample_records s
left join company_technologies t
on t.domain = s.domain
group by s.record_id, t.company_id;Read the result with the design of each source in mind. A profile describes what a company is. Fokals returns dated records of what a company does, and the table maps what a product screen shows to the dataset that delivers it.
| What the screen shows | Fokals dataset | What it delivers |
|---|---|---|
| Technologies in use | Technology Stack | Each technology with its category and its first-seen and last-seen dates |
| A tool adopted or removed | Technology Changes | A dated event for every adoption, removal and platform migration; a first observation sets a baseline and is never counted as a change |
| Open roles by function | Hiring Activity | Daily open, new and closed postings for each company, built on roles labelled by job function and seniority |
| Recent announcements | Company News | Announcements and regulatory disclosures classified into 13 event types, each with its date and its link |
| An account score | Intent Scores | A weekly score from 0 to 100 for each company and topic, with its five strongest signals as evidence |
| Headcount | Employee Headcount | Stated headcount over time, each count with the date it refers to |
To keep matched records current, the feed of website changes runs oldest first from a time you set, so the last cursor is your bookmark.
GET /api/v1/changes?since=2026-09-28&category=technology&limit=200
Authorization: Bearer <your key>Take Acme Robotics, an illustrative company. A profile source tells your user where it is registered, what it makes and who its parent is. The records above tell the same user that it adopted a commerce platform on Tuesday and opened six engineering roles this month. A product can need both, and the guide to enriching company records in your data product covers the join.
Other alternatives to Veridion
- Person and company records. People Data Labs documents a company dataset beside its person dataset, reached through its Company Enrichment and Company Search APIs. The guide to People Data Labs alternatives compares it with Fokals.
- Registry and credit data. Dun & Bradstreet issues the D-U-N-S Number and says its data is sourced from public registries, websites and trusted partners, with business scores and corporate family trees. The guide to Dun & Bradstreet alternatives covers it.
- Company, employee and jobs data. Coresignal offers company, employee and jobs records collected from the public web, through APIs and as datasets in JSONL, CSV or Parquet.
- Legal entities from registers. OpenCorporates offers company records that its site says are drawn from over 140 government registries and other official sources.
Where each fits
Veridion fits when the coverage is wide, small companies in any country included, and the need is a profile: who the legal entity is, where it operates, what it makes and whom it belongs to. Its solutions pages name commercial insurance, supplier sourcing, third-party risk and revenue operations among the uses.
Fokals fits a product or a research process that needs dated company signals on listed companies worldwide, the brands they own and verified private companies. It suits a security master that joins on ISIN or FIGI, a feature that scores accounts by topic with the evidence shown, and a record that stays exactly as it was written. Labels and scores are produced under named, frozen versions, and a breaking change ships as a new version with at least 90 days' notice.
Fokals is delivered direct, by REST API and as bulk files, which you load with your warehouse's own loader. Bulk exports come as JSON, JSON Lines or CSV with a manifest naming the period, label versions and licence. Licensing is by written agreement for internal use, embedding in a product or redistribution, and sample data for the companies you track is sent on request with the methodology.
Frequently asked questions
What is Veridion used for?
Veridion's solutions pages present its data for uses including commercial insurance, third-party risk, supplier sourcing, revenue operations, market intelligence and private equity. Its pages describe a company knowledge graph built from web crawling, registry ingestion and public data assets, reached through APIs for search, matching and enrichment, through batch files and through third-party delivery.
How many companies does Veridion cover?
Veridion's coverage page counts more than 500 million registered legal entities and more than 180 million operational companies across 250 countries and territories. A total says little about the companies you track, so test a sample of your records before you rely on it. The Fokals index is built on listed companies worldwide, the brands they own and verified private companies, each resolved to one company ID that carries its security identifiers.
How do Veridion and Fokals collect company data from the web?
Veridion's methodology page describes crawlers that collect content from the open and deep web, with registry ingestion, public data assets and a language model that structures each page. Fokals collects from first-party company sources and public records, and processes them in-house: what companies publish on their own websites and careers pages, what they announce and what they file. Its sourcing statement holds the detail a due diligence review asks for.
Can I embed web-sourced company data in my own product?
Only under a licence that says so. Veridion's terms of service allow internal business use and require a formal agreement for commercial use, public-facing use or distribution. Fokals licenses by written agreement for internal use, embedding in a product or redistribution, and the agreement governs. Settle with any vendor what your users may see, export and keep before you build.
How should I compare two company data providers?
On a sample of your own records. Draw a few hundred that cover the kinds of company you hold, send each provider what it matches on, and measure the match rate, the share of fields filled and the age of each value in every group. Then check a subsample by hand against the companies' own websites. A comparison of totals or field lists does not show how a source behaves on your accounts.
Can web-sourced company data be used in a back-test?
Yes, when each observation carries the date it was first seen and earlier values are kept as they were. Fokals writes daily and weekly datasets once, after the period closes, and never revises them, so the record is point-in-time by construction. Veridion's pages describe a continuously refreshed graph in which each value carries a last-updated time. Ask any vendor, Veridion included, how earlier values are kept before you test on them.
The queries and code on this page are examples to adapt. Test them in your own environment before you rely on them.
What this page says about the products it names was checked against their public documentation on 4 October 2026. Product and company names are trademarks of their owners. Fokals is not affiliated with them or endorsed by them.