LLM-powered products, autonomous agents, and applied ML — from prototype to a system your team can actually operate.
Most "AI features" shipped today are a thin wrapper around a single API call — impressive in a demo, brittle in production. We build the whole system: the prompt engineering and model selection, yes, but also the data pipelines, evaluation harness, guardrails, and monitoring that make AI dependable enough to put in front of real customers.
This is our deepest specialization as a studio, not a side offering — see our full AI Practice for how we think about responsible, production-grade AI.
We start with a narrow, high-value use case rather than boiling the ocean — prove the system works on real data with real evaluation criteria, then expand scope. Every AI feature we ship includes guardrails and a human-in-the-loop checkpoint appropriate to its risk level.
1stack = {
2 "models": ["OpenAI", "Claude", "Vertex AI"],
3 "orchestration": ["LangChain"],
4 "retrieval": ["Pinecone", "pgvector"],
5 "ml": ["PyTorch", "TensorFlow", "Hugging Face"],
6}The classic gap between a prompt that impresses in a meeting and a system that holds up under real, messy input.
Without an evaluation set, "it seems fine" is the best anyone can say — we fix that first.
Guardrails, confidence thresholds, and human-in-the-loop handoffs are designed in from the start.
Most AI projects stall here — we start from the data you actually have, not a hypothetical dataset.
No — we handle the full stack, from data pipeline to model integration to the product interface around it.
We're provider-agnostic — OpenAI, Anthropic, Google, and open-source models, chosen based on your requirements, not our preference.
See our full AI Practice page — evaluation and guardrails are built into every engagement, not an afterthought.