Firmographics are the floor. The file also carries funding, filings, validated leadership and headcount by department — and every row joins to the People Dataset, so companies and the people inside them arrive as one graph, not two files.
Platforms shipping their own products on these records today.
A firmographic file tells you what a company is. Three of these four tell you what it is doing, which is the part that dates.
Industry, employee size, location and headquarters — plus headcount by department, so the shape of a company is visible and not just its size.
Validated leadership, and the changes when someone arrives or leaves. Not a title scraped once.
Funding history and IPO filings, so a record says what stage a company is actually at.
What moved since the last drop — leadership, funding, news — rather than a fresh copy of the same thing.
The join back to people is the field that decides whether two files are one graph or two matching problems.
Legal and trading name, domain, headquarters and the identifiers that let you dedupe against your own records.
Industry, employee size band, location and headcount by department, normalised so two sources do not become two companies.
Who runs it, validated — and the arrivals and departures since the last file.
Rounds and dates, so a growth signal is a fact rather than an inference from headcount.
IPO filings and company news, attached to the company rather than left in a feed.
Every company joins to its people in the People Dataset, including its decision makers.
Same arrangement as the People Dataset, and most teams take both on one cadence so the two files stay in step.
Each of these is a combination of groups plus a cadence — nothing here needs a product we have not already listed.
of 60M+ — refreshed monthly
Your copy, your filters
Filter on size, industry and stage from your own copy, then refresh the movers by API. For sales-intelligence platforms.
Segment · payments infrastructure
Shortlist built on stage
Funding and filings across a segment, so a shortlist is built on stage rather than on who has a website. For investment and diligence.
Portfolio · 9 holdings
Noticed, not missed
Validated leadership plus the changes, so a new CFO is an alert rather than something noticed late. For customer success and PE.
Three spellings, one company
One canonical company row to reconcile a CRM, a billing system and a product database against. For data teams.
Meridian Partners
From the fund, not a list
Start from a fund rather than a filter, and get its holdings from funding history rather than a hand-kept list. For deal sourcing and competitive research.
tell me about Vertex Labs
StageSeries B · 2024
LeadershipS. Okafor, CFO
A lookup, not a call
Firmographics, funding and leadership held locally, so retrieval is a lookup rather than a live call per turn. For AI and agent teams.
We resolve and validate our own graph rather than reselling someone else’s, and matching stays hashed — so the raw identifiers you send are never exposed.
Security & Trust Centre
EU & UK
California
Audited
Yes. The two files are scoped separately. Most teams take both because the join is the point, but neither depends on the other.
Every company row carries the identifier its people resolve to, so the join is a key rather than a fuzzy name match you have to build.
Industry, employee size band, location and headquarters. That is the floor — funding, filings, leadership and change are what the file is actually for.
Validated rather than scraped once, with arrivals and departures carried as changes. Signals tell you between drops.
The one your stack already uses, delivered to your warehouse or object storage. Format and destination are part of the delivery agreement.
No. We resolve and validate our own graph across 18+ data sources and reconcile them into a single answer.
Fifteen minutes to scope the groups, the regions and the cadence — and to see what the rows look like against your own account list.