Whitepaper
How the scoring works, and what it costs
The detail that used to sit on the landing page. How the four scores are calculated, the research they draw on, the roadmap for the matching engine, and the full pricing with the success-fee mechanics.
Work we build on
- Fairness in ranking and recommendation
- Explainable AI for high-stakes decisions
- Bias auditing in hiring pipelines (FINDHR and related EU work)
- Graph representation learning for careers
How the four scores are built
- Experience
- Hard skills. Shipped a payments SDK and two internal design systems. Matches 9 of 11 must-haves; the gap is Kubernetes, which the brief marks nice-to-have.
- Soft skills
- Ran a team of 5 for three years. Structured answers in the screen, handled pushback without getting defensive, explains trade-offs clearly.
- Cultural fit
- Prefers async-first, written decisions, low-meeting weeks, which is how your team runs. Flag: wants quarterly in person, you are fully remote.
- AI literacy
- Trained on AI with Lyra. Uses agents in her daily workflow and can scope where automation helps and where a human stays in the loop.
Every score ships with a plain-language explanation and a link to the evidence. The candidate sees the same panel the hiring manager does.
Where we take it
- FAccT
- RecSys
- the EU AI Act technical standards work
Roadmap: from concierge to agentic
The matching engine is built in the open, one phase at a time. A recruiter verifies every match by hand today; each phase adds signal and automation without giving up the plain-language reasoning.
| Phase | Method | Status |
|---|---|---|
| MVP | Manual concierge. A recruiter verifies every match. | Live |
| V1 | Vector embeddings and cosine similarity, hybrid metadata filters | In development |
| V1.5 | Graph theory across skills, roles and trajectories | Planned |
| V2 | Agentic matching. Self-improving, multi-signal, Slack-native. | Research stage |
Pricing in detail
Three plans on the landing, explained here in full. The success fee is charged only after a hire has stayed three months, so the placement guarantee is proven before you pay anything.
Free
€0Post a role and see your first match, at no cost.
- Post free jobs
- Scan 50 people for free
- Get your first match
- 12% success fee, charged only after a hire stays 3 months, so the guarantee is proven before you pay
- Hire a good fit in under 9 days
Growth
€550 per monthMore reach per role and a lower success fee.
- Scan 100 profiles
- 10 matches per job posting
- Unlimited job postings
- Reduced success fee
- Hire a good fit in under 9 days
Enterprise
Usage-based Vega creditsRun Vega, our AI recruiting agent, priced per credit.
- Everything in Growth
- Vega, the Lyra AI agent, metered per credit
- Volume hiring across teams
- Dedicated support and onboarding
How Lyra makes money
| Stream | Detail | Status |
|---|---|---|
| Success fee | 12 to 15% of first-year salary on a hire | Active |
| Founding Partner retainer | €300 to €500 a month, plus a reduced success fee | Selling now |
| Senior and CTO placements | Retained search for leadership roles | Active |
| Forward Deployed Engineers | Embedded AI engineers inside client teams, monthly retainer | Active |
| Workshops | AI enablement for people teams. Ancillary, not the product. | Ancillary |