Model card
| System | Ros / Stability Engine retention-risk scoring |
| Outputs | Stability Score, survival-analysis-based 12-month retention curve, critical-risk month, confidence band, and a Logic Trace showing what the score is reacting to |
| Intended use | Directional pre-hire diligence and post-hire stability monitoring — calibrating onboarding investment, monitoring cadence, and early intervention |
| Intended users | Talent leaders, operators, PE operating partners — humans who retain decision authority |
| Inputs used | Structural career-history signals only (prior tenure patterns, transition density, history/role alignment, environmental-fit signals) |
| Inputs not used | Protected characteristics; interview performance; personality assessments; self-reported preferences |
| Prohibited uses | Sole basis for any employment decision; automated hire/no-hire; any use implying certainty of who will leave |
Known limitations (stated plainly)
- —Directional, not deterministic. The score is a probability signal, not a verdict. A high score does not guarantee retention; a low score does not mean a hire will fail.
- —Career-pattern risk only. It captures structural history signals — not management quality, team environment, or post-hire conditions that also drive retention.
- —Disclosed methodology ceiling. On candidates whose visible prior history is genuinely ambiguous, the system can be too pessimistic (pattern-break false-negatives). These cases are tracked and disclosed, not hidden.
- —Outcome cohort is currently building. The forward, real-world outcome-learning loop is architected and live but has not yet accrued a large outcome corpus. We say this out loud — see the methodology page.
Responsible-use statement
Ros informs human action; humans decide. Every output is a directional signal intended to help a manager or operator act earlier and more rigorously — not to replace the judgment that belongs in any serious hiring or retention decision.
Ros must not be used as the sole basis for any employment decision (hiring, termination, promotion, or compensation). This is stated in proposals and contracts, not buried.
Bias & adverse-impact posture
- —No protected-class inputs. Scoring uses structural career-history signals only.
- —Human-in-the-loop by design. The system surfaces signal; a human makes the call. This is the single most important control against automated adverse impact.
- —AI-disclosure attestation (NYC LL144-style). An AI-disclosure attestation is logged durably before any scan starts — the scan refuses to run if that record cannot be written.
- —Evidence-gated outputs. When the product shows "clear," it means checked-and-clear, not decorative. No synthetic proof, no fabricated case studies.
- —Planned, not yet claimed: an independent bias / adverse-impact audit and SOC 2 are on the roadmap. We do not claim either is complete today.
Data governance & security
- —Tenant isolation. Reads and writes are server-scoped by company; default-deny database rules are deployed and verified; access is gated on active billing status; cross-tenant data enrichment is rejected even when a foreign record is referenced by direct ID.
- —Encryption. Candidate resume content is encrypted at rest.
- —Access model. Enterprise tenants share data only within their own company scope; a canceled or inactive tenant is denied at the auth layer by design.
Honesty architecture
Why the claims are defensible:
- —Predictions logged before outcomes. Prediction snapshots are persisted at scan time; the system cannot retroactively grade itself.
- —Proof chain. Each monitored hire has a visible timeline: risk flag → intervention scheduled/completed → employment outcome captured → learning eligibility, with honest complete / in-progress / insufficient states.
- —Financial claims are proof-gated. Prevented-attrition value is only claimed when flag → recorded intervention → retained outcome exists, in that order.
What we do not claim
- •We do not claim the model tells you whom to hire.
- •We do not claim certainty about who will leave.
- •We do not claim a single headline accuracy number.
- •We do not claim a completed independent bias audit or SOC 2 (roadmap, not done).
- •We do not claim the learning loop has already learned from a large outcome corpus.