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AI & automation

Intelligent workflow automation

LLM features and internal automation built like software, not demos — evaluated against a dataset, bounded in cost, and with a defined answer for what happens when the model is wrong.

What you get

Deliverables

  • 01Use-case assessment with a go / no-go recommendation
  • 02Retrieval and prompt architecture with an evaluation suite
  • 03Model integration with cost and latency budgets
  • 04Human-in-the-loop and fallback paths for low-confidence output

How it runs

Process

  1. 01

    Qualify

    Which parts of the workflow actually benefit — and which are cheaper as ordinary code.

  2. 02

    Prototype

    A narrow slice measured against real examples before scope grows.

  3. 03

    Evaluate

    A scored test set, so changes are improvements rather than vibes.

  4. 04

    Integrate

    Shipped behind the same monitoring and guardrails as the rest of the product.

Tooling

What we reach for

  • Claude API
  • Python
  • TypeScript
  • Vector search
  • Evaluation harnesses

Proof

Where we’ve done this