MarketMind

Inspiration

Prediction markets like Polymarket are powerful tools for forecasting, but analyzing them requires deep quantitative skills and constant market monitoring. We wanted to democratize market analysis by building an AI copilot that could reason about prediction markets the way expert traders do—by combining real-time data with strategic thinking—all through a simple chat interface.

What it does

MarketMind is an AI-powered prediction market analysis platform that provides real-time trading signals through conversational AI. Users can ask natural questions like "Should I bet on this market?" and get structured analysis including:

Direction signals (bullish/bearish/neutral) with confidence scores
Data-driven reasoning considering price vs. fair value, liquidity, and time to resolution
Key factors influencing the market that might be underweighted
Historical analysis stored in a queryable dashboard

The system combines a Next.js frontend with a custom MCP (Model Context Protocol) server that performs deep quantitative analysis using Claude Sonnet 4, all orchestrated through Lava's API for enhanced reliability.

How we built it

Frontend: Next.js 14 with TypeScript and Tailwind CSS for a responsive split-view interface (chat + dashboard)
Backend: Custom MCP server written in TypeScript that exposes a generate_signal tool for structured market analysis
AI Layer: Claude Sonnet 4 accessed via Lava API for both orchestration and signal generation
Database: Supabase (PostgreSQL) for storing market analyses with efficient indexing
APIs: Polymarket API integration for live market data The architecture uses a two-layer LLM approach: an outer orchestrator handles user conversations while an inner specialized agent generates quantitative signals with a focused system prompt tuned for prediction market analysis.

Challenges we ran into

Structured output reliability: Getting consistent JSON signal format from LLM responses required careful prompt engineering and regex extraction
MCP integration complexity: Building a custom Model Context Protocol server from scratch meant learning the SDK and debugging stdio communication
Real-time data sync: Coordinating between live market data, AI analysis, and database persistence within tight latency constraints
API rate limiting: Managing Lava forward requests and Polymarket API calls efficiently

Accomplishments that we're proud of

Built a fully functional MCP server that generates structured trading signals with confidence scores
Created a clean two-panel UI that feels like a professional trading terminal
Successfully integrated multiple APIs (Lava, Anthropic, Polymarket) into a cohesive system
Implemented persistent storage with proper indexing for historical signal analysis

What we learned

How to architect agentic systems with specialized tool-calling layers
The power of MCP for building reusable AI components
Prompt engineering techniques for getting structured quantitative outputs
Real-world considerations in building trading analysis tools (calibration, confidence scoring, factor extraction)
The importance of separating orchestration logic from specialized analysis