Pathway Vector

Vocation Intelligence · 2 demonstrations · Built for Workforce Routing in Disciplined Services & Career Programs

Traditional posting decisions weigh seniority and supervisor preference. Pathway Vector reads a different signal — what people gravitate toward across their career, and where that trajectory is heading.

Trust Vector matches reviewers to readers — both sides external to your platform.

Resonance Vector matches your offerings to your members — your offerings, your members.

Pathway Vector matches your own pathways to your own people — both sides inside your organisation.

Bring your HR records and engagement history. We model the match your senior officers have been doing by gut.

These two demonstrations walk through the engine on HKPF Pilot v1.0 — 200 synthetic officers, 10 vocation dimensions, 6 career archetypes. Demo data is synthetic; your deployment would model your own pathways and people.

Seniority Baseline · P@3
26.7%
Rank by years of service
Pathway Vector · P@3
86.7%
Cosine similarity routing
Improvement
+60pp
Gate ≥ 30pp · PASS
1
Signal

Individual officer career profile — radar on the mean dim vector, alignment-score sparkline, posting timeline with rationale, dim-score table

Open →
2
Routing

Posting slot allocation — Seniority baseline vs Pathway top-3 candidates side-by-side, with Precision@3 by posting type and archetype performance

Open →
Where Pathway Vector does not go
Voluntary preference signals only. No performance ratings. No disciplinary records. No behavioural monitoring. Pathway Vector is decision augmentation — not decision replacement, and not surveillance.
Engine config: 10 vocation dims · cosine_sim routing on empirical posting-type profile vectors · 1,257 postings Haiku-decomposed · 6 archetypes · HKPF Pilot v1.0 · 200 synthetic officers