SignalRoom: Evidence-First Customer Intelligence
Rongali Chaitanya
From scattered research to traceable decisions
SignalRoom is an independent Noerong product for SaaS product teams, research consultancies, and customer-led agencies. It turns interviews, support conversations, surveys, and reviews into a decision system where every recommendation can be traced back to customer evidence.
The problem
Research is often spread across call notes, spreadsheets, support tools, and slide decks. Teams may have plenty of information, but they lose the chain between the original customer language, the themes they identify, and the product decisions they make.
My role
I led the complete product from concept to deployment, including product strategy, UX design, information architecture, full-stack engineering, AI integration, testing, documentation, and release packaging.
Design principles
Keep every insight connected to a source.
Separate observations from recommendations.
Make confidence depend on evidence coverage.
Keep important decisions under human control.
Treat customer text as untrusted content, never as system instructions.
The workflow
1. Capture the evidence
A searchable repository brings customer interviews, support notes, surveys, and reviews into one structured workspace. Every record keeps its source, segment, company, quote, and review status.
2. Connect repeated signals
Related evidence becomes themes with visible supporting mentions and confidence. The team can move from a summary back to the exact customer language behind it.
3. Prioritize opportunities
Opportunity scoring makes reach, urgency, confidence, and commercial relevance explicit. Product bets become comparable and reviewable instead of depending on the loudest opinion.
4. Package the decision
SignalRoom turns findings, recommendations, supporting evidence, and unresolved questions into concise stakeholder reports.
5. Ask with citations
The DeepSeek assistant answers from the same research workspace and cites the evidence records used in each response.
Engineering and trust
The application uses Next.js, React, TypeScript, Tailwind CSS, a SQLite and D1-compatible data layer, protected server-side AI routes, CSV import controls, request size limits, same-origin checks, timeouts, and usage controls. Railway configuration and buyer setup documentation are included.
Outcome
The result is a complete working path from evidence import to themes, opportunity scoring, cited AI answers, and decision-ready reports. The responsive interface and reduced-motion support were checked across desktop, tablet, and mobile layouts.
The figures and records shown in the product are fictional sample data. Verification statements describe product tests, not client performance.
Explore the product
Evidence repository: every record keeps its source, segment, company, quote, and review status visible.
Opportunity scoring: reach, urgency, confidence, and commercial relevance become explicit and reviewable.
Decision-ready reports: findings, evidence, recommendations, and open questions stay connected in one stakeholder brief.
SignalRoom walkthrough: a narrated tour from customer evidence to themes, prioritized opportunities, cited AI answers, and stakeholder reports.
Like this project
Posted Sep 11, 2026
Full-stack research workspace turning customer evidence into traceable themes, prioritized opportunities, cited AI answers, and decision-ready reports.
