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Service 01

Artificial Intelligence

Most AI programs fail on selection, not execution, and the ones that clear selection fail on everything that surrounds the model. We work on both: finding the handful of cases where the economics, the data and the risk appetite line up, then building them so they hold under real load.

What we deliver

  • Use case discovery and scoring against value, feasibility and risk
  • Retrieval architecture: chunking, embeddings, hybrid and re-ranked search
  • Evaluation harnesses with golden datasets and regression gates in CI
  • Agent design with typed tool contracts, least privilege and human checkpoints
  • Cost, latency and quality instrumentation from the first commit

What you end up with

  • A ranked portfolio instead of a list of ideas
  • Measured answer quality, not vibes
  • Predictable cost per request, understood before launch rather than after
Eval findings01SourcesDocuments and systems02IndexChunk and embed03RetrieveHybrid and re-rank04GenerateGrounded, cited05GuardrailsRefuse and redact06MeasureFaithfulness, cost
A retrieval-augmented system. Retrieval quality, not model choice, is where most of these fail.

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A short conversation is usually enough to tell whether there is a real case here, and we will say so if there is not.

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