AI Agent Case Studies — Production Results
Real-world AI agent implementations with verified outcomes. Every case study includes the problem, constraints, approach, measurable results, and lessons learned.
Polymarket Flow Capture Bot
Outcome: 1-3% daily returns via liquidity provision + momentum exits
Problem: Prediction market bot was losing money chasing volatility. Needed strategy shift from speculation to market-making.
Approach: Redesigned as flow capture system: widen bid-ask spread, provide liquidity during volume spikes, exit on momentum confirmation. Added position reconciliation to prevent phantom trades. Structured logging on every decision for post-hoc analysis.
Stack: Python, PostgreSQL, Docker, Prometheus
Results
- Daily Returns: 1-3% — Consistent over 30-day window
- Phantom Position Bugs: 0 — Eliminated via chain sync reconciliation
- False Signal Reduction: -60% — Relaxed thresholds, tight exits
- Uptime: 14 days — Zero downtime continuous operation
TakeOff Pro: Multi-Model Consensus
Outcome: 95% auto-approved, quote turnaround 48h→4h
Problem: Construction takeoff process required expert review of every LLM-generated quote. Client needed automation without sacrificing accuracy.
Approach: Implemented "Automated Consensus" pattern: 3+ models vote on each line item, flag disagreements for human review. Added confidence scoring and audit trail for every decision. Domain experts validate strategy, not every output.
Stack: TypeScript, Redis, PostgreSQL, OpenAI, Anthropic
Results
- Auto-Approval Rate: 95% — 5% flagged for human review
- Quote Turnaround: 48h → 4h — 92% faster end-to-end
- Billing Errors: 0 — 90-day production trial
- Audit Compliance: 100% — Insurance requirements satisfied
Veil: Institutional Flow Intelligence
Outcome: Detected 12 pre-earnings accumulations, 70% mobile engagement
Problem: Retail investors lack access to institutional order flow data. Existing tools either too complex or actively misleading with predictions.
Approach: Built signal fusion platform: congressional trading disclosures, options flow, volume anomalies. Focused on "what happened" not "what will happen." No predictions, just evidence. Mobile-first design drove adoption.
Stack: Next.js, Python, PostgreSQL, Tailwind, Vercel
Results
- Accumulation Patterns Detected: 12 — Pre-earnings, verified post-hoc
- Avg User-Reported Gain: 8% — On tracked positions
- False-Positive Insider Alerts: 0 — Zero in production
- Mobile Engagement: 70% — Of total sessions