Overview
A purpose-built research tool that turns Reddit into structured market intelligence. Rather than requiring users to know which subreddits to monitor or what queries to run, a conversational onboarding experience builds a detailed user profile and maps relevant communities — then the system generates targeted research across five analysis types with full source citations.
The Problem
Reddit is one of the richest sources of unfiltered market intelligence, but extracting value from it is hard:
- Signal-to-noise ratio is terrible without domain expertise
- Manual monitoring doesn't scale across communities
- Traditional market research costs $10K+ per report
- Existing tools require technical users who already know which subreddits to track
Solution: Chat-First Research
The tool flips the typical approach. Instead of asking users to configure scrapers and pick subreddits, an AI conversation guides the entire setup — building both a user profile and a subreddit map before any research begins.
Conversational Onboarding
The onboarding process does two equally important things:
1. Profile Creation The AI builds a detailed understanding of who the user is and what they need:
- Industry vertical and niche positioning
- Target audience and customer segments
- Specific intelligence goals (competitive, content, opportunity)
- How they'll use the output (content creation, consulting, strategy)
2. Subreddit Discovery Using the profile context, the AI discovers and ranks relevant communities:
- Maps subreddits by relevance score to the user's industry
- Identifies adjacent communities that surface non-obvious insights
- Explains why each subreddit was selected
- Configures monitoring scope automatically
The profile and subreddit map together form the research foundation — every subsequent analysis is shaped by both.
AI-Powered Search Strategy
Rather than scraping entire subreddits, the system generates focused search queries from the user's profile context — reducing noise by 83% and keeping analysis costs under $1 per report.
Five Analysis Types
Comprehensive — Full market overview surfacing dominant themes, recurring patterns, and key takeaways across all mapped communities.
Pain Points — Top challenges and frustrations ranked by frequency and confidence scoring, drawn from real user conversations.
Opportunities — Service and product opportunities matched directly to the user's profile, with evidence from community discussions.
Dual Perspective — Separate analysis for multiple stakeholders (e.g. buyers vs. sellers, practitioners vs. decision-makers).
Trends — Emerging patterns and shifts in sentiment backed by evidence trails with timestamps.
Profile Modes
Users select how they'll use the intelligence, and the output adapts:
- Content Creator — blog ideas, LinkedIn posts, newsletter topics backed by Reddit evidence
- Consultant — service opportunities, pitch angles, engagement strategies with supporting data
Each mode tailors the output format, tone, and recommendations to match the use case.
Evidence-Based Citations
Every insight links back to its source — Reddit posts with upvote counts, timestamps, and direct URLs. No black-box analysis.
Architecture
Tech Stack
- Frontend: Next.js with React Query
- Backend: FastAPI with SQLAlchemy (async)
- Database: PostgreSQL via Supabase
- Cache: Redis for session and scrape data
- Auth: JWT with bcrypt
- Payments: Stripe subscription billing
- AI: Claude API for analysis and onboarding
Current State
Core infrastructure is deployed and the analysis engine is production-tested:
- Full-stack deployed (Vercel + Railway + Supabase)
- User registration, auth, and session management working
- Database schema (12 tables) with seed data
- Analysis engine validated across multiple verticals
- Payment integration in progress
What's Next
- Complete Stripe subscription flow
- Finalize the chat onboarding UI
- Scheduled monitoring with email delivery
- Historical trend comparison (week-over-week)
This project demonstrates conversational AI product design, full-stack development with Python and Next.js, and building research tools that abstract complexity behind intuitive interfaces.