EngineeringMost AI demos never make it to production. Here is how we scope, evaluate, and deploy agents that teams can actually rely on.
The best AI projects begin with a clear operational job: deflect support tickets, extract contract terms, or triage leads. The model is a tool for that job, not the starting point. We write the success criteria first, then choose the smallest model that can meet them reliably.
Generic prompts produce generic results. We connect agents to the client's own documents, tickets, and APIs so answers are specific, traceable, and safe. Retrieval architecture, chunking strategy, and fallback behavior matter more than the headline model name.
Before tuning latency or cost, we build an evaluation harness that measures accuracy against real examples. If the agent fails silently, it does not ship. Human-in-the-loop review is the default until the numbers prove the system can fly solo.
Agents live in messy environments. We design escalation paths, rate limits, retries, and logging from day one. Production deployment is the midpoint of the project, not the finish line.
Tell us what you're building and we'll map the shortest path to production.
