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AI Product Proof

Grounded AI Workflow Reference Demo

Document based AI workflows can sound convincing even when a source is outdated, a capability is only planned or evidence is insufficient.

Product problem

A reviewer needs to know whether an AI workflow can separate implemented release facts from planned capabilities, conflicting evidence and missing evidence.

Users or reviewers

Hiring managers, AI Product Leads, data platform Product Managers, Engineering Leads and technical evaluators.

Product decision

The milestone isolates retrieval, answer context, claim classification, uncertainty, blocked claims, static validation and human review.

Scope and explicit exclusions

  • Included: approved corpus, lexical retrieval, structured answer context and validation.
  • Included: six cases with supported answer, blocked overclaim, source conflict and safe refusal.
  • Excluded: LLM integration, embeddings, external data, production security and autonomous generation.

Implementation evidence

  • The validator reports 32 passing scenarios and preserves the older 14 scenario baseline.
  • The interview path shows three primary cases in a five minute sequence.
  • The roadmap ticket export mention is blocked from becoming a current capability claim.

My role

Implementation was accelerated with AI coding agents. Product framing, scope, acceptance criteria, review and release decisions remained my responsibility.

What this proves

  • Product risk framing for grounded AI workflows.
  • Acceptance criteria and validation design.
  • Clear separation of release behavior and future platform ideas.
  • Readable communication for product and technical audiences.

What this does not prove

  • Production scale.
  • Enterprise security.
  • Model quality with an integrated LLM.
  • Customer adoption.

Inspection path

  • Run npm run -s jarvis:reference-demo for the six case output.
  • Run npm run -s jarvis:reference-demo -- --interview for the five minute path.
  • Run npm run -s jarvis:reference-demo:validate to check the 32 validation scenarios.

Human review boundary

Every final decision remains review required. The system can classify and block claims, but it does not publish or act autonomously.

Current maturity

Interview ready local reference demo with synthetic data and validated product boundaries.

Next evidence needed

A real reviewer or product team should use the demo in an interview, design review or controlled evaluation and record what was clear, unclear or missing.

Technical verification

The demo can be run locally without network, database or LLM access.

Inspection commands
npm run -s jarvis:reference-demo
npm run -s jarvis:reference-demo -- --interview
npm run -s jarvis:reference-demo -- --json
npm run -s jarvis:reference-demo:validate

Contact

Professional contact

Start a no-obligation case conversation.

Emailhello@wateristheholygrail.com
SubjectProfessional Work inquiry - Product cases
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