Most agencies solve it with volume — more calls, more CVs, more noise. We built a system that solves it with precision. Here's how the three layers fit together.
Each intake conversation becomes a node in the graph — connected to skills, stacks, salary bands, work styles, and career goals. Roles enter the same graph. Matching is finding the shortest, strongest path between them.
A keyword search finds people who wrote "Python" on a CV. The model reads the whole graph and asks a better question: who is moving in the direction of this role?
200+ signals per candidate — skills with depth, not just presence; salary trajectory; team-size preference; what they said they'd leave for.
Every role is scored against every candidate across all signals. Strong fits surface with a match score and the reasoning behind it — which we check before anything moves.
The model never contacts anyone, never sends a CV, never decides. It shortlists. People do the rest.
A match score doesn't take a job. The last mile of every placement is human — and it stays that way.
Every candidate in the graph has talked to us. We know the context behind the data — and they know who's representing them.
Salary alignment happens before the first interview, not after the offer. Both sides know the numbers from day one.
One person owns the placement end to end. No handoffs to a "delivery team", no account managers.
AI alone would make us a faster job board. The human layer is why placements stick.
The graph only works if candidates trust it. These commitments are specific on purpose.
Every profile enters the graph through a conversation you chose to have. Nothing is scraped.
Inactive profiles are deleted automatically after 18 months. No silent archives.
Email us and your data is gone within 30 days — confirmed in writing.
All candidate data lives on EU infrastructure, under GDPR jurisdiction.