Azure AI Foundry Solutions
Enterprise AI that runs in your tenant, cites its sources, and survives contact with your auditors.
What you get
- AI applications deployed inside your Azure subscription and security boundary
- Retrieval that's evaluated and measured — recall and groundedness before vibes
- Citations users can check, which is where enterprise trust actually comes from
- A cost model you understand before the invoice, not after
Most RAG projects fail in the last mile
The pattern is depressingly consistent: a promising demo, a pilot that impresses, and then a slow loss of trust as users catch the system answering fluently from the wrong document, the stale version, or thin air. By the time someone asks “where did that answer come from?” and nobody can say, the initiative is dead — whatever the architecture diagram looked like.
That last mile is grounding, evaluation, and citations. It’s less glamorous than model selection and it’s where we spend most of our effort.
What we do differently
We index what the business trusts, not everything SharePoint contains. We evaluate retrieval separately from generation, because they fail differently and most teams only test the second. We ship the citation UI before the chat UI, because verifiable answers are what enterprise adoption is actually made of. And we build the evaluation harness first, so every change to prompts, chunking, or models gets gated by measurement the same way your code gets gated by tests.
It runs in your tenant, on your identity stack, with costs you can see per use case. Boring, auditable, and in production — which is the point.
How we engage
Assess. Pilot. Build. Enable.
Every engagement follows the same shape — small steps, working software at each one, and an exit where your team owns the result.
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01
Assess
A focused readiness assessment: your data, your tenant, your governance posture, and your first three use cases — ranked by value and feasibility.
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02
Pilot
One scoped use case to working software in weeks, with an evaluation harness from day one so quality is measured, not vibes-checked.
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03
Build
Production hardening: security review, observability, CI/CD, and rollout. The pilot graduates into something your auditors can live with.
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04
Enable
Your team takes the keys — documentation, training, and pairing until you are self-sufficient. We measure success by not being needed.
Deliverables
What we actually hand over
AI readiness assessment
Your data estate, tenant architecture, governance posture, and first three use cases — ranked by value and feasibility, with a recommendation on what to build first and what to skip.
Production RAG pipeline
Ingestion, chunking, indexing in Azure AI Search, retrieval tuning, and a citation-first UX — with an evaluation harness that gates changes the way tests gate deployments.
Model deployment & evaluation
Model selection from the Foundry catalog against your task and budget, deployment in your subscription, and continuous evaluation for groundedness, relevance, and safety.
Operations handover
Observability, cost monitoring, and runbooks — plus the enablement for your platform team to own it.
Stack: Azure AI Foundry · Azure OpenAI in Foundry Models · Azure AI Search · Foundry Agent Service · Azure Functions · Microsoft Entra
FAQ
Questions we actually get
Does our data leave our tenant?
No. Foundry deployments, your indexes, and your application all run inside your Azure subscription under your Entra identity and network controls. Prompts and completions aren't used to train foundation models. That's most of the reason enterprises pick this stack.
Which models can we use?
The Foundry model catalog spans Azure OpenAI models, open-weight models, and specialized ones. We pick per task — the expensive frontier model where reasoning matters, cheaper or smaller models where it doesn't. Model choice is a cost-and-quality decision, not a loyalty program.
How do you measure whether the AI is any good?
With an evaluation harness from day one — golden question sets, retrieval metrics (does the right document come back?), groundedness scoring (does the answer come from the document?), and regression runs on every change. If quality isn't measured, it doesn't exist.
How is this different from Microsoft 365 Copilot?
M365 Copilot is a product you adopt; Foundry is a platform you build on. When the workflow, data, or UX needs to be yours — custom applications, your line-of-business data, your interface — you build on Foundry. Many clients run both.
Book an AI readiness assessment
Your data, your tenant, your first three use cases — and a straight answer on what to build first.
Or reach us directly at contact@eightbot.com
Let's talk
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