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SignalRoom

Live product · Independent Noerong product

An evidence-first customer intelligence workspace that turns research into traceable themes, prioritized opportunities, and decision-ready reports.

View live product Watch walkthrough Source kit · $29 Setup service · from $249

The $29 source kit includes the application code, sample data, commercial licence, setup guidance, security notes, and Railway configuration. The $249 customization service covers one brand, one focused workflow, a buyer-owned deployment, DeepSeek configuration, handoff, and one revision. Hosting, domains, API usage, data migration, custom integrations, and ongoing maintenance are separate.

IndustryCustomer intelligence and research AI
AudienceSaaS product teams, research consultancies, and customer-led agencies
Studio roleProduct strategy, UX design, full-stack engineering, AI integration, deployment
Released2026

The product

Built around the
actual workflow.

SignalRoom began with a trust problem in customer research. Evidence was scattered across interviews, support tickets, surveys, and reviews, while the reasoning behind product priorities was difficult to audit. I designed and built one connected workspace that keeps every theme, opportunity, report, and AI answer anchored to its underlying evidence.

Product walkthrough

See the complete
decision workflow.

A narrated tour from customer evidence to themes, prioritized opportunities, cited AI answers, and stakeholder-ready reports.

Full HD · 1 minute · Voice-over included

Product approach

Less friction.
More clarity.

The system follows the real research journey from capture to decision. Structured evidence can be imported by CSV, reviewed, grouped into themes, and connected to opportunities. Prioritization makes reach, urgency, confidence, and commercial relevance visible. The AI layer is deliberately constrained to the workspace evidence and cites supporting records instead of inventing unsupported conclusions.

Core system

What it
brings together.

01

Structured evidence library

Interviews, support conversations, survey responses, and public reviews share a consistent record with source, segment, company, quote, and review status.

02

Traceable themes

Recurring signals are organized into searchable themes with coverage and confidence indicators, keeping patterns connected to the records that support them.

03

Opportunity scoring

Reach, urgency, confidence, and commercial relevance create a visible prioritization model instead of a hidden or arbitrary score.

04

Evidence-grounded AI

Ask SignalRoom answers from the available research, cites evidence such as E1 and E2, and communicates uncertainty when the workspace does not support a strong conclusion.

05

Decision-ready reporting

Reports are assembled from the same evidence and opportunity model, giving stakeholders a clear view of findings, support, and next steps.

Inside the product

The work, in detail.

The research overview brings evidence volume, themes, opportunities, coverage, and emerging signals into one decision surface.
The research overview brings evidence volume, themes, opportunities, coverage, and emerging signals into one decision surface.
The evidence workspace keeps source, segment, company, quote, and review status visible for every record.
The evidence workspace keeps source, segment, company, quote, and review status visible for every record.
Opportunity scoring makes reach, urgency, confidence, and commercial relevance explicit and reviewable.
Opportunity scoring makes reach, urgency, confidence, and commercial relevance explicit and reviewable.
Decision-ready reports preserve the connection between findings, supporting evidence, and recommended next steps.
Decision-ready reports preserve the connection between findings, supporting evidence, and recommended next steps.

Open any image for its full-size view. Sample records illustrate product behavior, not client performance.

Architecture

Why these
decisions.

Next.jsReactTypeScriptDeepSeek

Next.js, React, TypeScript, and Tailwind CSS provide the product shell and five responsive workspaces. SQLite/D1-compatible persistence stores evidence and workflow state. DeepSeek runs only through a protected server route with input validation, request-size limits, rate limiting, same-origin checks, provider timeouts, and structured diagnostics. Docker and Railway configuration support a repeatable deployment with persistent storage.

Outcome and boundaries

What is built.
What comes next.

Delivered product

A complete working product from evidence import to cited AI analysis and stakeholder reporting. The live Railway deployment passed laptop, tablet, and mobile checks, CSV import and review-state tests, valid and invalid AI request tests, cross-origin rejection, and a browser-console review without application errors. These are product verification results, not client-performance claims.

Before a business launch

A business deployment requires a buyer-owned hosting account, persistent storage, an exact production origin, and a funded DeepSeek API key stored only as a server environment value. The $249 service covers one brand and one focused workflow. Historical data migration, enterprise integrations, multi-tenant architecture, hosting, API fees, and ongoing maintenance require a separate scope.

Independently designed and built by Rongali Chaitanya at Noerong. Scope and acceptance criteria are agreed before a client implementation.

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