AI and RAG development on Azure
Retrieval and agent systems on Azure AI Foundry — with boundaries, evaluation and observability.
Sound familiar?
What it looks like before we arrive
- The demo worked, and nobody can say how often the production version is wrong.
- A document from one customer could surface in another customer’s answer.
- Every model release raises the question of whether to upgrade, with no way to answer it.
- An agent can reach systems it should never touch.
What you get
What we deliver
- An evaluation set built from your real questions, before any prompt or model is tuned
- Source isolation in the architecture, not in a prompt instruction
- Boundaries for agents: what each can call, and what needs a person to approve
- Observability that shows which step of an answer went wrong
- A written recommendation on model choice, backed by the evaluation
Engagement shapes
How it can be shaped
Timelines are planning estimates for a typical scope. Yours goes in the written scope, with a fixed quote for each option, within two business days of our first conversation.
- 01
AI readiness review
Is AI the right tool for this problem? A short written answer, with the evaluation behind it.
Ask about this - 02
Pilot to production
Take a working prototype to something you can measure, secure and support.
Ask about this - 03
Evaluation harness
The test set and tooling that tell you whether a change made answers better or worse.
Ask about this
Typical stack
- Azure AI Foundry
- Azure AI Search
- Azure Functions
- API Management
- Front Door + WAF
- Azure SQL
- Managed Identity
- Bicep
Track record
The principal’s record, inside SI teams
Kakoriya Cloud opened in October 2026. These figures come from estates our principal worked on before then, each with its scope.
Questions
AI on Azure: questions, answered
Is our data used to train models?
Not by anything we build. We design so your data stays in your Azure tenant, and show you where each piece of it is stored and who can reach it.
Which model will you use?
Whichever the evaluation supports. Sometimes that means not upgrading: a newer model is measured against your questions before it is adopted, not after.
Do you build with Azure OpenAI?
Yes, through Azure AI Foundry, which hosts Azure OpenAI models alongside others. Which model a system uses is decided by an evaluation against your own questions.
We only have an idea, not a prototype. Is that too early?
No — that is when a readiness review is cheapest. Some problems are better solved without AI, and it is worth knowing that before building anything.