AI Agents & LLM Integration

Data readiness is usually the first problem we work on, not a reason to wait. Most organizations have data that's messier than they'd like. The question is whether it's good enough for the task at hand, and that's something you find out in an assessment, not by assuming either way. We run a readiness pass before any RAG or agent build starts.

Cost is part of the architecture. We size models to the task, cache where caching pays off, and route simpler queries to cheaper inference rather than sending everything to the most capable model. An AI feature that works but costs several times its value doesn't survive the next budget review, so we watch the bill as closely as the accuracy numbers.

Usually one of three things: the workflow the AI was meant to live in wasn't owned by anyone; the evaluation criteria were vague, so nobody could agree whether it worked; or guardrails were an afterthought, and the risk review killed it before launch. We build evaluation harnesses and human-in-the-loop checkpoints from the start, and we set explicit kill/scale gates so the pilot has a clear decision point rather than a slow fade.

It rarely requires a rewrite. We're equally comfortable adding AI features to existing software as building new. RAG systems can sit alongside your current codebase. The integration points need to be clean, but that's scoping work, not a reason to start over.