AI Engineering

Most LLM features die in production. The demo dazzles, the rollout collapses — hallucinations, latency spikes, brittle prompts, and no way to measure whether anything is actually working.

We build LLM-powered features the way you'd build any other production system: with evals, observability, fallback behavior, and a clear theory of what "good" looks like before we ship.

Whether it’s RAG over your own data, a generative product surface, or an internal tool that lets ops move faster, the goal is the same: AI features that hold up after the launch tweet.

What’s Included

• LLM integrations (Claude, GPT, open-source models)
• RAG pipelines with proper retrieval evaluation
• Prompt engineering and prompt management
• Eval frameworks and regression testing
• Embeddings and vector search (pgvector, Pinecone, etc.)
• AI-powered product features end-to-end
• Fine-tuning and adapter training when it actually helps

Key Benefits

Features that survive contact with real users
Built with evals, fallbacks, and observability from day one.

Quality you can measure
Eval pipelines so you know when a prompt change made things worse.

Right model for the job
Honest tradeoffs between cost, latency, and capability — not “use the biggest model.”

AI you can actually ship
Not a Jupyter notebook — a production feature with logging, retries, and rollout controls.