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RAG / Product StrategyMock demo

SignalDesk AI

An AI product-intelligence workspace that ingests user feedback, clusters pain points, finds evidence, and generates PRDs, roadmap bets, and experiment plans, every claim cited.

Next.jsTypeScriptAnthropic APIOpenAI embeddingsSupabase pgvectorVercel
Live demo ↗GitHub
SignalDesk AI screenshot

At a glance

ICP
Early-stage SaaS teams and founders with 50-500 feedback items from CSVs, app reviews, or notes.
Features
  • Upload feedback CSV or paste notes
  • Embed + semantically cluster themes
  • Evidence-backed insights with citations
  • Generate a PRD from validated opportunities
  • Generate roadmap + experiment plans
  • Track product-metric assumptions

AI architecture

  1. 1

    Ingest

    CSV / pasted notes, validated and normalized.

  2. 2

    Chunk + embed

    OpenAI text-embedding-3-small (real) or deterministic local vectors (mock).

  3. 3

    Cluster

    Group feedback into themes; score opportunity size + confidence.

  4. 4

    Retrieve

    pgvector similarity search fetches top evidence snippets.

  5. 5

    Generate (RAG)

    claude-opus-4-8 drafts PRD / roadmap / experiments grounded in citations.

  6. 6

    Verify

    Guardrail requires ≥2 evidence links per recommendation.

Case study

Product problem

PMs need to move from a pile of feedback to a defensible roadmap. SignalDesk makes the evidence trail first-class: every opportunity, PRD line, and experiment links back to real user quotes.

ICP & MVP scope

ICP: early-stage SaaS / founder with 50-500 feedback items. MVP: import, cluster, ask-over-feedback, and one-click PRD + roadmap + experiment generation with citations. Out of scope: integrations with live ticketing tools and multi-user review workflows.

Metrics & guardrails

North star: validated opportunities converted into PRDs. The key guardrail, unsupported recommendation rate, directly protects trust, which is the whole value proposition of an evidence-first tool.

Resume bullets · AI Engineering

  • Built a RAG pipeline (ingest → chunk → embed → cluster → retrieve → generate) over user feedback with pgvector and source-grounded generation.
  • Enforced a citation guardrail (≥2 evidence links per recommendation) to eliminate unsupported AI claims.

Resume bullets · AI PM

  • Designed a research-to-roadmap workflow that converts raw feedback into cited PRDs, roadmap bets, and experiment plans.
  • Defined an evidence-first metric framework where the core guardrail (unsupported recommendation rate) protects user trust.
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