AI systems built to be accountable, not just impressive.
RAG pipelines, agentic workflows, and applied ML engineered with evaluation harnesses, cost discipline, and a human in the loop where the decision actually matters.
A demo that works once isn't a production system.
Generative AI is unusually easy to make look finished and unusually hard to make actually reliable. A RAG pipeline that nails three test queries in a demo can still hallucinate on the fourth, and an agent that handles a happy path gracefully can spiral when a tool call fails. The gap between an impressive prototype and a system you'd trust with a real customer interaction or a regulated workflow is where most AI initiatives quietly stall after the initial excitement.
We close that gap with the unglamorous infrastructure most AI demos skip: evaluation harnesses that catch regressions before users do, cost and latency budgets treated as real constraints rather than an afterthought, and human-in-the-loop checkpoints designed in wherever the cost of an autonomous mistake is actually high — not removed for the sake of a cleaner demo.
Six things we actually deliver.
LLM application architecture
System design for production LLM features — context management, fallback behavior, and graceful degradation when a model call fails.
RAG pipelines & vector infrastructure
Retrieval systems built around your actual data quality, not a clean benchmark dataset — chunking, re-ranking, and grounding done properly.
Agentic workflows & tool use
Multi-step agent systems with bounded autonomy and clear failure handling — not an open-ended loop hoping for the best.
Evaluation, prompt & cost optimization
Eval suites that catch quality regressions automatically, plus the prompt and model-routing work that keeps inference costs sane at scale.
Classical ML & MLOps
Forecasting, classification, and recommendation systems — not every problem needs an LLM, and we'll tell you when it doesn't.
AI policy & governance
Usage policies, audit trails, and human-in-the-loop checkpoints for AI features operating in regulated or high-stakes workflows.
A model that's right most of the time still needs a plan for the time it isn't — that plan is the actual engineering.
What clients actually ask us.
Grounding generation in retrieved, verifiable sources rather than open-ended model knowledge, evaluation suites that test against known failure cases before every deploy, and — for anything high-stakes — a human-in-the-loop checkpoint rather than full autonomy. We design for the failure case, not just the success case.
We'll tell you honestly if classical ML or even a rules engine solves your problem better, faster, and cheaper than an LLM — it often does. Reaching for the most fashionable tool isn't the same as solving the actual problem.
Model routing (using a smaller model where a larger one isn't needed), prompt optimization, caching repeated queries, and cost monitoring built in from the start — not discovered for the first time on a surprise bill at the end of the month.
Building an AI feature that has to actually work?
Whether it's a RAG system, an agentic workflow, or an AI feature that needs guardrails before it touches customers — the first conversation is on us.