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.
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
- 1IngestCSV / pasted notes, validated and normalized.
- 2Chunk + embedOpenAI text-embedding-3-small (real) or deterministic local vectors (mock).
- 3ClusterGroup feedback into themes; score opportunity size + confidence.
- 4Retrievepgvector similarity search fetches top evidence snippets.
- 5Generate (RAG)claude-opus-4-8 drafts PRD / roadmap / experiments grounded in citations.
- 6VerifyGuardrail 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.
- 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.
- 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.