Case Studies
Deep-dive case studies from our research partnerships with production-grade AI
See how we've helped partners solve complex AI challenges
Markdown-Driven Browser QA with Evidence-Rich Reports
We productized and open-sourced a browser QA runner after proving it across TheBlueOne and InterviewLM: 480 markdown specs, 289 archived run directories, release CSV sync, HTML reports, and Gemini 3.1 Flash Lite as the best-fit operating model for this workflow.
Tech Stack
Key Results
Production Specs
480
Archived Runs
289
Largest Sweep
107 tests
Multilingual Entity Resolution at Scale
Multi-agent LLM classification system for Indian grocery items across 12+ languages, achieving 93% category accuracy at $0.03 per batch of 255 items using a self-correcting ReAct validation agent.
Category Accuracy
93%
Cost per Batch
$0.03
Languages
12+
Let Your Agent Choose Its Own Brain
Build Cursor-style Auto Mode where your agent intelligently switches between Haiku, Sonnet, and Opus based on task complexity. Complete implementation with tool-based state updates and middleware routing.
Cost Savings
10x
Implementation
3 files
Complexity
Low
AI-Native Technical Assessment Platform
Implementation of a production-grade multi-agent system using LangGraph 1.0 and LangChain to power real-time AI-assisted technical interviews with adaptive difficulty and comprehensive evaluation.
Agents Deployed
8
Cost Reduction
40%
Response Latency
<2s
Architecture Modernization
Modernizing a legacy TypeScript-based agentic system to a modern Python LangChain v1 architecture, achieving 5-10x throughput improvements, 90%+ cache hit rates, and 50-70% reduction in token costs.
Throughput
5-10x
Cache Hit Rate
90%+
50-70%
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