When the use case goes beyond what a standard agent can do, you have to build. That means choosing a model, preparing a corpus, and agreeing to measure quality rather than assume it.
Chunking, enrichment, vector indexing. Corpus quality determines answer quality far more than the choice of model.
By language, acceptable latency, cost per request and required residency. The most capable model is rarely the right economic choice.
A reference question set, measured before and after every change. Without it nobody can say whether a change improved or degraded the system.
Quotas, caching, routing to a lighter model when the question is simple. Cost per request is designed, not discovered on the invoice.
We build with your subject experts a set of questions whose correct answers are known. It is the first deliverable, and it serves you long after the mandate.
A simple pipeline, measured against that set. Sometimes it is enough — in which case we say so and the mandate ends early.
Chunking, query rewriting, reranking, model. Every change is kept or rejected on the measurement, not on impression.
Quality and cost monitored over time, with thresholds that alert your team.
Model, index and data stay in the regions you chose. Residency is an architectural decision, documented.
It is what lets you change model in six months without starting over, and refuse a change that would degrade quality.
Measured, not estimated. It is what makes extending to other use cases a decision rather than a gamble.
Tell us which questions you want answered. We will tell you what your data supports today.
See your external surface →