All work

Governed AI system

OpportunityOS

A truth-locked opportunity workflow built around governed autonomous agents, provenance and controlled generation.

AI engineering · multi-agent architecture · governance · product systems

Open public evidence
Trace / authorizepublic_safe_derived
01Discover02Ingest03Qualify04Score05Truth-lock06Prepare07Monitor08Learn
Factual authorityTruth GraphEvidenceClaim
DRY_RUNASSISTEDCONTROLLED_SUBMIT
Public architecture diagram — not product UITruth and provenance constrain matching, generation and outbound action authority.

Case study

Autonomy is only useful when the system can explain what it knows, what it is allowed to claim, and exactly where it must stop.

Opportunity automation crosses factual, policy and external-action boundaries. The architecture therefore has to keep generation flexible without letting it invent material founder facts, reinterpret missing evidence as rejection, or silently escalate into an external submission.

Role & scope

Product architecture, truth/provenance authority, evidence-aware matching, truth-locked artifact generation and controlled outbound-action modes, represented only through the allowlisted public documentation mirror.

Approach

How the system earns the result.

01 — Acquire

Discover under source policy, not around it.

Opportunities enter through a central source-policy model that separates measured access behavior from permission and preserves provenance through normalization and deduplication.

02 — Qualify

Treat missing evidence as unknown, not negative.

Matching is evidence-aware: a hard rejection needs an explicit requirement plus a verified conflict or versioned policy. Scoring may rank evidence but may not fabricate fit.

03 — Generate

Keep every material claim attached to truth authority.

The Truth Graph and EvidenceClaim model constrain generated CVs, cover letters and related artifacts. Verified facts may be selected and rewritten, but material claims may not be invented.

04 — Act

Escalate authority deliberately.

DRY_RUN, ASSISTED and CONTROLLED_SUBMIT separate preparation from external mutation. Sensitive ambiguity, CAPTCHA/MFA and uncertain outcomes are stop conditions rather than invitations to guess or retry.

05 — Learn

Improve operations without weakening deterministic truth.

Monitoring, feedback and outcome learning sit downstream of the same authority model so operational autonomy cannot create a weaker parallel path for facts or permissions.

Evidence

What can actually be checked.

Factual authority

Truth Graph

Material claims remain traceable through the public EvidenceClaim authority model.

Action authority

3 modes

DRY_RUN, ASSISTED and CONTROLLED_SUBMIT make external-action escalation explicit rather than implicit.

Decision semantics

Open-world

Unknown or absent evidence is not automatically treated as false, ineligible or a reason to reject.

Limits & boundaries

What the case study does not pretend.

The private OpportunityOS repository is authoritative; the public repository is an allowlisted documentation mirror.

Founder truth, application data, credentials and private implementation details are intentionally excluded from the public case study.

The website uses explanatory architecture, never a synthetic dashboard that pretends to be hidden product UI.

Publication boundary

This case study describes only the public architecture contract and governance model. It deliberately proves the system through invariants and authority boundaries rather than exposing private operational data.

From proof to useful work

Where this project maps to real service work.

These links come from the governed project/service evidence map. They are not generic cross-sells and do not widen the claims made above.

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