Type what you do. JobID translates every job, CV and search into one shared model of skills — and shows you roles that fit, with the exact reason why. The data layer that makes work computable.
Eighteen pages of results. By page three, half of them have nothing to do with what you asked. So you give up — not because the job isn't out there, but because you can't find it, and you can't trust the search.
The same job is posted as “Growth Hacker,” “Demand Gen Manager,” and “Brand Lead.” Search one, miss the other two.
A “Solution Manager” — meaningless on its own. The perfect candidate searches “Product Owner” and never sees it.
A nurse who also runs the ward and trains juniors searches “nursing” — and never sees “Clinical Operations Coordinator.”
This isn't an annoyance. Skills mismatch costs around 6% of GDP and up to $15 trillion by 2030 (BCG). The most important matching problem in the economy runs on guessing job titles.
Twenty years. Dozens of funded startups. The biggest platforms on earth. All chasing semantic job matching.
And all of them work top-down: take the job, let AI break it into data, then guess how similar two things are. Guessing has no floor. Change one word and the results move. It can't be exact, and it can't explain itself.
Everyone built on sand. The fix is to start from solid ground.
Underneath everything sits one shared model of work — skills, tasks, context. Every job, CV, training and search gets translated into that model. We don't guess whether two things are similar. We compute it against the same ground truth.
Think Nutrition Facts: once every product is described in the same standard units, you can finally compare and rank them. JobID does that for work.
An ontology at exactly the right depth — not the bottomless trees that make other systems unusable at scale.
Queries that used to take minutes, answered in milliseconds.
100% precise — plus fuzzy matching that surfaces the close-but-worth-it roles you'd never have found.
They guess. We compute.
Same search. Indeed breaks by page three. Eightfold makes errors out of the gate. JobID holds the fit — and shows the reason for every single result.
Here's what surprised even us: the search is the profile. Explore a few roles you like, lift a task here, rule one out there — and you've built your JobID. Save it once, and the right work finds you. You never search again.
Two numbers on every match: how well you fit the role, and how much of the role you cover. Search only “Java”? You'll see 100% matches — that cover a sliver of the job. JobID tells you the truth instead of flattering you.
The wedge. Talent comes for the experience and builds a living JobID. Jobs we aggregate for free. The better the experience, the faster it spreads — word of mouth and press, not bought traffic.
The money. Companies pay to reach that structured talent graph — and to run the same matching inside their own walls. It's not another job ad competing with Indeed. It's access to data that exists nowhere else.
The expansion. The same engine that matches a candidate to a role matches 2,000 employees to 150 new ones — internal mobility, workforce optimization, reskilling. Then the engine itself, licensed to the boards and HR platforms that need it.
Every search makes the graph richer. The graph is the moat.
Work is being rewritten by AI, titles inflate, skills shift monthly — and AI needs structured, computable work data that simply isn't there. Whoever owns the shared model of work owns the layer LinkedIn, SAP and Indeed all need and none of them built.
For twenty years the giants didn't crush semantic matching — they bought it:
We're building the open standard none of them owns.
The invention risk is behind us — four years of R&D, a working prototype, the engine proven. What your investment buys is execution, not a science experiment.
17 years placing executives and key roles. Lived this pain firsthand — the reason JobID exists.
Built the tech, data and AI. The engine, the ontology, the millisecond retrieval.
We're the rare team that felt the problem and cracked the research.
This round takes JobID from proven engine to live product: the first thousands of JobIDs, the first paying customers, the data flywheel turning.