AI & Automation

We get asked for AI more often than we get asked for a specific automation problem, which is backwards in a useful way: it means the first real conversation is usually about narrowing down what's actually worth automating, not about which model to use. A lot of manual work isn't worth automating — it's infrequent, low-volume, or already fast enough that engineering effort is better spent elsewhere.

The work that is worth it tends to share a pattern: repeatable, well-defined, and currently done by a person copying information between systems that don't talk to each other. Reporting workflows, data reconciliation, ticket triage, internal search over a knowledge base — these are the boring, high-value cases, not the flashy ones.

These are the boring, high-value cases, not the flashy ones.

LLM and RAG applications get built and evaluated like any other software: a prototype against real data first, an honest look at where the model gets things wrong, and a decision about whether the failure mode is acceptable for the use case before it goes anywhere near production. We're specific about what data leaves your infrastructure and what stays local, because that's usually the actual blocker, not the technology.

Once something works, it gets the same deployment and monitoring as any other system we run — versioned, logged, and owned by a real team, not a notebook someone forgot about after the demo.