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Tracking Demand Signals with AI: Building a Market Intelligence Platform

Every product team I've worked with has the same question: "Is there actually demand for what we're building?"

Usually the answer involves guesswork, a few Google Trends searches, and someone's gut feeling. I wanted something better. So I built Demand Signal — an AI-powered platform that tracks and interprets demand signals across multiple data sources.

What Are Demand Signals?

Demand signals are data points that indicate market interest in a product, technology, or trend. They come from everywhere:

  • Search volume — what people are actively looking for
  • Social mentions — what people are talking about
  • Job postings — what companies are hiring for
  • Competitor activity — what's being built in adjacent spaces
  • Community discussions — what developers and users are asking for

The challenge isn't finding these signals. It's making sense of them at scale.

The Architecture Behind Demand Signal

Demand Signal is built on a modern stack:

  • Next.js + TypeScript for the frontend — fast, SEO-friendly, and easy to iterate on
  • AI pipeline for signal extraction and classification
  • Real-time data ingestion from multiple source APIs
  • Dashboard with trend visualization using D3.js

The AI layer does the heavy lifting: it classifies raw data into structured demand signals, scores them by confidence, and surfaces actionable insights.

Why I Built It

During my time in AI engineering — from working with brands like Samsung and JLR to building at a YC startup — I saw teams make product decisions based on incomplete information.

The tools that existed were either too expensive (enterprise market research platforms), too generic (Google Trends), or too manual (building custom scrapers).

Demand Signal sits in the middle: powerful enough for real analysis, accessible enough for indie builders and small teams.

Lessons from Building a SaaS Product

A few things I learned shipping this:

  1. Start with one signal source, not five. My first version tried to aggregate everything. It was buggy and slow. Focusing on one high-quality source first and expanding later made all the difference.

  2. AI confidence scores need calibration. The model would confidently classify irrelevant data as a "strong signal." Adding human-in-the-loop feedback loops dramatically improved accuracy.

  3. Speed of insight matters more than depth. Users don't want a 30-page report. They want a dashboard that tells them in 10 seconds whether demand is trending up or down.

Use Cases

Demand Signal is useful for:

  • Product managers validating feature ideas before committing engineering resources
  • Founders gauging market interest before launching
  • Content creators identifying trending topics to write about
  • Investors tracking emerging technologies and market shifts

Check It Out

If you're building products and want data-driven insight into market demand, try Demand Signal. It's the tool I wish I'd had years ago.

You can also explore my other projects including Clawbench for AI evaluation, or follow my building in public updates.