AI features that work in production, not just demos

We integrate AI into existing products with proper guardrails. LLM features, semantic search, recommendation engines, and NLP pipelines. Production-grade reliability, not science projects.

The Challenge

Demo-quality AI

AI features that impress in a demo but hallucinate in production. No guardrails, no fallbacks, no monitoring. The gap between prototype and production is enormous.

Cost spiraling out of control

LLM API costs that scale linearly with usage. No caching strategy, no model tiering, no way to predict next month's bill. What started as $500/month becomes $50,000.

Integration, not replacement

You have a working product. You need AI features added to it, not a ground-up rebuild. The integration has to work with your existing architecture, data, and deployment.

Our Approach

Guardrails and structured output

Schema-validated LLM outputs, content filtering, and fallback paths for when the model fails. Users get reliable results even when the AI is uncertain.

Cost optimization from day one

Semantic caching, model tiering (use GPT-4 only where quality demands it), and batch processing where latency allows. We design for sustainable unit economics.

RAG done right

Retrieval-augmented generation with proper chunking strategies, re-ranking, and citation tracking. AI responses grounded in your actual data, not hallucinated from training data.