Cloud First · AI Ready · Data Driven
For your role

For AI Lead

You own the gap between what the model can do in a notebook and what it does for users in production. We help you close it, with the eval rigor and platform structure to repeat it with the next use case.

What you probably have

The shape we recognize.

  • A PoC that works most of the time and a stakeholder who already calls it a product.
  • No real evaluation harness, just vibes and a Slack channel.
  • Cost growth that nobody can predict because nobody is measuring it.
  • Three more use cases lined up behind this one with no shared platform under them.
How we help

What we ship for you.

  • Build the eval harness and prompt-regression suite before the next change ships.
  • Stand up the production stack: Azure OpenAI, retrieval, observability, governance.
  • Lay the platform layer so the next use case is a deployment, not a rebuild.
  • Close the cost / governance / audit gaps before they become a board conversation.
How we engage

What an engagement typically looks like.

Eval harness build

Two to three weeks. A prompt-regression suite and evaluation framework in place before the next change ships. The minimum viable production infrastructure for any AI feature.

Production stack buildout

Azure OpenAI, retrieval, observability, cost instrumentation, and governance controls. The shared infrastructure that makes the next use case a deployment, not a rebuild from scratch.

PoC to production

Take a specific, prioritized proof of concept through to a production deployment with evals, monitoring, and cost controls in place before go-live.

Outcomes

What it looks like when it works.

60%

of enterprise AI projects fail to reach production; the gap is usually evaluation, observability, and governance, not the model

3-5x

faster AI feature iteration when evaluation harnesses and deployment pipelines replace manual notebook workflows

40%

reduction in manual review time when AI-assisted document processing enters high-volume operational workflows

25%

improvement in decision accuracy with ML-assisted scoring on structured classification problems

Sound like the conversation you need to be having?

Tell us what you are trying to change. We will either be useful, or point you to who would be.

Start a conversation