AI Integration
Productionize AI without the science fair. We help you pick the use cases that justify the investment, ship the first one, and put the governance and evaluation rails in place so the second and third do not become a mess.
Three things we focus on.
Use-case pick
Half the value is choosing the right problem. We help you screen ideas against feasibility, data readiness, and what changes for the business if it works.
Production-grade plumbing
Azure OpenAI, retrieval (RAG), evaluation harness, prompt regression tests. The parts that turn a demo into something you can rely on.
Governance and cost
Token budgets, model selection, content policy, audit trails. Boring on day one, mandatory by month three.
Whatever shape fits the work.
Two to four weeks. Map candidate use cases against feasibility and data readiness; produce a sequenced recommendation.
Take one prioritized use case from PoC to production with eval rigor and a path to ownership.
Build the shared AI platform (RAG, evals, observability) that subsequent use cases plug into.
What we get asked to do.
- Build a RAG-based internal knowledge assistant on Azure OpenAI and Azure AI Search
- Automate document extraction and classification from unstructured PDF or email inputs
- Add AI-assisted triage or routing to a customer service or support workflow
- Build an evaluation harness and prompt-regression test suite for an existing AI feature
- Implement token budget management and cost observability for Azure OpenAI usage
- Design a content policy and audit trail layer for a production AI application
- Conduct an AI use-case readiness assessment across a business unit
- Move a notebook-based proof of concept to a governed production deployment
What we bring to ai integration.
We ship AI products, not demos
Every AI engagement includes an evaluation harness, prompt regression tests, and production monitoring. A demo that works most of the time is a liability. A product works consistently or tells you clearly when it does not.
Use-case selection first
The most valuable thing we do in an AI engagement is help you pick the right problem. Half the AI projects that fail do so because the use case was never going to change anything meaningful. We screen before we build.
Built on Azure OpenAI
Enterprise security, data residency, and compliance controls that your procurement and legal teams already understand. No consumer API workarounds, no shadow IT risk, no debate about where your data goes.
Governance from the first sprint
Content policy, token budgets, audit trails, and access controls are part of the architecture. They are cheaper to build in than to retrofit after a legal review flags a problem three months into production.
What clients typically see.
Ready to talk about ai integration?
Tell us what you are trying to change. We will either be useful, or point you to who would be.