Signal
An autonomous AI agent that investigates markets in real time — turning scattered evidence into a scored, source-traceable intelligence report.
- Agentic AI
- LLM Orchestration
- Market Intelligence
- Product-Market Fit
Signal
Market Intelligence
Awaiting investigation
Investigation
Topic
Industry
Known competitors
Up to 120 seconds to map the market.
What should Signal investigate?
Signal will map competitors, opportunities, demand shifts, market signals and strategic risks.
Try it live
The real product, running live — give the first investigation a moment to spin up.
Overview
Founders spend months building before they validate demand. Signal is an autonomous agent that investigates a market continuously — pulling evidence from search, social, trend, and launch data — and synthesizes it into a structured, evidence-backed intelligence report. Built in a one-week sprint for the Agentic & Autonomous Systems track at Zuup Hackathon 2026.
- Role
- Full-Stack Builder
- Team
- Team of 3 — Manya Mankad (full-stack build), Ananya Rajhans, Aisha Monsar
- Timeline
- 1-week hackathon sprint
- 1 wk
- Build sprint
- 6
- Pipeline stages
- 4
- Live data sources
The problem
Markets now move faster than organizations — a trend can emerge, explode, and disappear before most teams even notice.
Founder behavior
- Reddit browsing
- Random customer interviews
- Competitor stalking
- Trend hunting
- Gut-feel decisions
Consequences
- False positives
- Confirmation bias
- Missed opportunities
- Slow validation cycles
- Wasted development time
The problem isn't a lack of information. It's turning scattered information into actionable intelligence.
What I built
Signal is an autonomous market-intelligence agent: give it a topic, an industry, and (optionally) known competitors, and it investigates continuously rather than answering a single query. It maps competitors, demand shifts, opportunities, and strategic risks, then returns a scored, evidence-backed report.
As the team's full-stack builder, I designed and built the product end-to-end — the investigation UI, the FastAPI backend, and the multi-stage AI pipeline behind it — working alongside teammates Ananya Rajhans and Aisha Monsar over the one-week sprint.
Automated Debt Management Solutions
Address a major gap with a high-demand feature and strong differentiator.
Score
65/100
Confidence
60/100
Source · Signal Network
Integration with Budgeting Tools
Enhance user experience and data synergy by connecting with current tools.
Score
65/100
Confidence
60/100
Source · Signal Network
Investor-Grade Financial Forecasting
Serve sophisticated users with advanced financial planning capabilities.
Score
65/100
Confidence
60/100
Source · Signal Network
How it works
Evidence Collection
Signals gathered from across the internet
Signal Extraction
Noise filtered into measurable signals
Intelligence Engine
Signals analyzed in depth
AI Synthesis
Patterns connected into a narrative
Build Recommendation
The case for and against, scored
Technical architecture
Frontend
Lovable + React
REST client, custom components
Backend
FastAPI + Pydantic
Typed request/response models across customer, market, and competitive extraction
Data sources
Live evidence
AI layer
Gemini 2.5 Flash
Executive synthesis · JSON-enforced prompts
Output
Structured JSON report
Returned to the frontend over REST · deployed on Render
What it produces
Signal doesn't hand back a yes or a no. It builds the case in both directions — the strongest reason to build alongside the biggest risk, a recommended customer and positioning, the most defensible moat, and a confidence score for how well the evidence supports any of it. The decision stays with the founder; what changes is how much they know before making it.
Opening the full brief shows the evidence underneath — the trend, discussion, and competition numbers each score was derived from — so any claim can be audited back to its source.
Signal Verdict
Confidence
88/100
Comprehensive intelligence on pain points, desired outcomes, competitive landscape, and white space provides a clear strategic direction. Lack of explicit market size data introduces some uncertainty.
Top reason to build
Deliver an authentic cookie taste and texture with optimized macro ratios.
Biggest risk
Intense competition from established brands, highlighted by a competition score of 100.
Recommended customer
Fitness-conscious snackers
Recommended positioning
A premium protein cookie delivering authentic, delicious taste and texture, optimized for high protein and low sugar, crafted with all-natural ingredients.
Best moat
Proprietary recipes and natural ingredient formulations for superior taste.
Open full brief →
Market Brief
protein cookie
Why Signal found this
The protein cookie market offers a strong opportunity to innovate, addressing critical consumer pain points regarding taste and ingredients. While competition is high, clear white space exists for a differentiated product.
Evidence
Signal context
Growth
26
Competition
100
Discussion
114
Momentum
13
Signal strength
What I'd focus on next
The one-week build proved the core loop: evidence in, structured decision out. The natural next layer is distribution-side intelligence — tracking influencer and community activity as an additional signal source, rather than just founder-facing market analysis.
Takeaways
Building the full stack inside a team sprint
Owning the investigation UI, the FastAPI backend, and the AI orchestration layer myself meant every technical decision — how prompts were structured, how concurrency was handled, where evidence got normalized — was also a product decision.
Structured output over prose
Enforcing JSON schemas on every LLM call, rather than parsing free text, was the single decision that made the pipeline reliable enough to demo end-to-end in a week.
Evidence-first, not confidence-first
Every insight had to trace back to a collected source. That constraint — inherited from the product's own positioning — shaped the backend as much as it shaped the UI.
Still here?
If this is the kind of work you want on your team, let's talk.
I'm always up for a conversation about design, emerging tech, or building things that don't exist yet.
Currently in Bengaluru (IST) · open to remote-first roles, and to being somewhere else
See more work