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}Most "AI-powered" features never get past a demo because nobody defined what "working" actually means. Here's what we check before anything reaches a customer.
Every prompt or model change runs against a fixed set of real and adversarial examples — so a "fix" can't silently break something that worked yesterday.
For RAG systems: Precision@k and Recall@k on retrieval, plus a faithfulness score checking the answer actually matches the retrieved source — not just "looks right."
P50, P95, and P99 response-time targets are defined before a line of integration code is written, not discovered when users complain.
Input sanitization against prompt injection, output validation before it reaches a user, and an allow-list for any tool or function the model can call.
Token and inference cost is measured per feature from day one — so scaling usage never arrives as a surprise on the cloud bill.
A defined confidence threshold below which the system defers to a human, rather than guessing and hoping it's right.
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.