Governed AI product workflow
A governed AI workflow for turning approved career evidence into recruiter-ready artifacts.
The system connects portfolio proof, source-audited evidence retrieval, role-aware resume generation, authenticated APIs, evals, and human review, showing how AI can improve high-stakes knowledge work without hiding claim boundaries.
Personal operating context with authenticated boundaries, source-audited retrieval, eval-backed grounding, and human approval. No external-adoption claim.
Product problem
Hiring teams often see polished claims without knowing what evidence supports them. This system separates authored proof, source-audited evidence, role-specific tailoring, AI-assisted critique, and final human approval so career materials can be stronger without becoming less trustworthy.
Hands-on AI product leadership
Daniel defined the product architecture, evidence boundaries, claim-safety rules, workflow stages, evaluation criteria, and portfolio positioning. He used AI-assisted development to build and iterate across the Portfolio Guide and ResumeCustomizer while preserving human review as the final approval layer.
The products share an operating context and authenticated contracts, but each retains a distinct responsibility. Human review remains the final accountability layer.
01
Recruiter UX, authored page grounding, Portfolio Guide interactions, download, and optional email delivery.
02
Public-safe evidence retrieval, role mapping, generation, specialized reviews, rendering, and temporary artifacts.
03
Final accountability for what is approved, represented publicly, and submitted to a hiring team.
Get a grounded read on responsibility, evidence, impact, or what to read next.
Approved evidence becomes portfolio proof, role-specific mapping, a generated draft, specialized review, and finally a human-approved artifact.
Recruiter-facing evidence discovery with explicit source priority and claim boundaries.
What does this system prove about Daniel's AI product judgment?
Server-to-server boundary
Role-aware evidence mapping, specialized critique, and validated document rendering through six configured advisory reviewers.
Models assist with mapping, drafting, and critique; Daniel remains responsible for what is approved and submitted.
This is the core governed-product mechanism: it lets the Portfolio Guide answer deeper hiring-manager questions without inventing claims or exposing unsafe or private details. Retrieval reuses the existing bearer-authenticated server boundary rather than adding a browser credential.
01 / Question
A deeper hiring-manager question triggers source-audited career evidence retrieval.
02 / Governance
publicSafeOnly and sourceAuditedOnly default to true before evidence can be returned.
03 / Response
The API returns claims, metrics, source status, and answerability without exposing raw resume bullets.
An implemented public-safe evidence search contract with structured claims, metrics, source status, answerability, and graceful degradation. Raw resume bullets remain private.
A complete shared evidence graph. Durable page-to-evidence identifiers and unified publishing remain future work.
Launch condition: the Portfolio tool and ResumeCustomizer evidence endpoint must be merged and deployed together.
Transferable AI product judgment
This system shows Daniel can design AI products where models are useful but constrained: retrieval is source-aware, claims are bounded, unsafe gaps are surfaced instead of hidden, outputs are reviewed, and quality is measured through evals before being represented publicly.
In a stored 12-case Portfolio Guide eval set, grounding and source-separation changes improved acceptable responses from 5/12 to 11/12. This is a historical comparison from comparable runs, not a current full-suite quality claim.
Six configured reviewers
The baseline-template pass uses four strategic reviewers before drafting. The final-resume pass runs all six against the completed content and rendering evidence. Their findings are advisory; structural checks can block output, and Daniel retains final approval.
Top-third fit and supported JD keyword coverage.
Buried qualifications, weak skim-read clarity, and exact terms that are supported but missing.
Role-specific proof, seniority calibration, and likely hiring questions.
True gaps, adjacent evidence that needs a safe bridge, and strong proof the draft failed to surface.
AI product leadership positioning and story coherence.
Stale positioning, credibility risks, and language that undersells or overstates supported experience.
Exact claim verification against approved or current-run evidence.
Missing sources, contradictions, overclaims, credential inflation, and supported claims weakened by over-hedging.
Parseability, density, section clarity, and document hygiene.
Template leakage, unusual characters, dense bullets, ambiguous labels, and page length padded with filler.
Candidate archetype, bridge classification, and the highest-leverage positioning change.
Off-archetype framing, unsafe domain leaps, underplayed proof, and bridges that need evidence instead of invention.
Structured output
Findings, 1–5 scores, issue severity, source status, and recommendations labeled source-backed, needs-source, or reject.
Credibility boundary
Review artifacts expose tradeoffs and gaps. They do not prove universal uplift or replace final human judgment.
Portfolio Guide
31 cases
Authored evaluation inventory
Resume review
6 agents
Configured advisory perspectives
Role strategy
3 lanes
Configured positioning lanes
The credible product story includes its limits. Current capabilities are visible on the left; the investments required for stronger reliability and measurement stay visible on the right.
Use the generator when a role-specific artifact would help a hiring team evaluate the same evidence in context.
The generator is an action surface, not evidence of role fit.