Product intelligence,
powered by your customers.
Sylvia turns scattered customer feedback into clear, evidence-backed product decisions. Then delivers ready-to-build specs for your team/AI agents.
Connect your entire stack
How Sylvia works
From scattered feedback to shipped features — in six steps.
Unify all feedback
Connect your support, sales, product, and research tools. Sylvia ingests customer signals from every team into one place.
Understand your product
Sylvia learns your product structure, customer segments, roadmap, and personas to exclude — so every signal is interpreted in context, not in a vacuum.
Find real needs
Using that product context, Sylvia deduplicates signals, filters non-ICP noise, and surfaces recurring problems across teams. What remains are validated patterns — not just mention counts.
Generate opportunities
Sylvia synthesizes what to build, who needs it, supporting evidence, confidence level, and trade-offs. When Sales says "PDF export" but Support says "dashboards" — Sylvia finds the real need behind both.
Native reporting feature
98 requests · $240k pipeline · 23 tickets/wk
Evidence
“Can't share reports with my clients”
“We lost the deal because no PDF export”
73% say “reporting” — real need is sharing data externally, not PDF.
Deliver ready-to-build specs
Sylvia translates opportunities into user stories, acceptance criteria, and user flows. Zero translation gap — go straight from insight to implementation.
User Story
As a team lead, I want to export and share reports so clients can review progress.
Acceptance Criteria
User Flow
Push to your workflow & AI agents
Push specs to Linear, Jira, or Slack for team review. Or give your AI agents the context they need — Sylvia sends specs, customer evidence, and business context directly to Cursor, Claude Code, or Copilot via MCP. Agents that understand the why build the right thing.
AI Agent
via MCP
Stop guessing.
Start knowing.
Connect your tools, ask what to build, and get answers grounded in real customer data.