AI

We put language models and classical systems where they earn their keep: search over your corpus, extraction over your documents, assistance inside a tool someone already opens every day. We will not sell you a chatbot as a strategy. We will tell you when a rules engine is enough.

When a model is warranted, we treat evaluation as part of the product. There is a baseline before we ship. There is a set you can rerun when the model or the corpus moves. There is a fallback — a queue, a rules path, a human — when the answer is wrong or the vendor is down. A system that only works on the happy path is a demo, however fluent it sounds.

Most of the work is not the model. It is permissions, tenancy, logging, and the last mile into a console a clerk or a dispatcher already trusts. That is why this practice ships with Software and Cybersecurity, not as a standalone lab.

We decline work whose only success metric is “people liked the demo.” If you cannot name the task, the user, and the cost of being wrong, we are not ready to build.