Not a chat bubble. A research tool your people can cite.
Your organization already has the answers, scattered across documents nobody has time to read. A RAG implementation puts them one question away, with every answer traceable back to the document it came from.
What a RAG implementation does
Retrieval augmented generation, usually shortened to RAG, grounds an AI system in your own documents instead of a model’s training data. A question goes in, the system retrieves the passages that actually matter, and the model answers from those passages rather than from memory.
Two layers make it work. Azure AI Search handles retrieval, which is where most RAG projects quietly fail. Microsoft Foundry handles the model, the routing, and the safety controls. Get the retrieval layer right and the answers stop feeling speculative.
If you want the architecture in detail, our engineers wrote it up: Building Enterprise-Ready RAG with Azure AI Search.
Three things we do differently
A full page, not a widget in the corner
Serious research does not happen in a chat bubble floating over something else. We build a dedicated interface your people can actually work in.
Every answer cites its source, and the source opens
Users see which document an answer came from and can open the original. This is the difference between a tool people trust and a tool people quietly stop using.
Only the people you authorize
Access restricted by email domain or your own identity provider, so a tool built for staff, members, or institutional users does not end up open to the public.
Start with one library and one interface
Fixed price. Dates set at kickoff. You keep the environment either way.
In scope
- 500 to 5,000 of your documents ingested and indexed with Azure AI Search.
- A full screen query interface with citations back to the source document.
- Access restricted to the users you authorize.
- Model selection and configuration in Microsoft Foundry.
- A cost model at your projected monthly question volume, including how existing Azure credits apply.
What you keep
- The working environment, in your own Azure tenancy.
- The interface, running on your documents.
- A written target architecture.
- A written cost model at your volume.
- A fixed price proposal for the full build.
Out of scope, stated plainly: automated ingestion pipelines, production hardening and go live, and replacing any content system you already run. Those get scoped from the pilot rather than guessed at beforehand.
The pilot is step two of four
Fit Check
Free. Three questions, under two minutes.
You get a read on screen and a short written summary by email.
AI on Your Data Pilot
Fixed price. Dates set at kickoff.
One document library, one working query interface with citations, and a written architecture and cost model. The full scope is above, and you keep the environment either way.
Full Build
Scoped from the pilot.
More sources, automated ingestion, production hardening, and go live.
Managed and Optimize
Monthly retainer.
Cost and quality monitoring, retrieval tuning, and a written monthly report.
Building a broader data platform rather than a document assistant? That is the Microsoft Fabric and Foundry track. Same four steps, different scope.
Holding unspent Azure credit?
A surprising number of these conversations start with credits quietly expiring. We architect so your existing credit actually applies, and we confirm which models are and are not available in Foundry before anyone commits to a design that assumes otherwise.
Quick fit check
Answer three questions and you will get a read on screen, then a short written summary by email.
Rather talk first? Book a 30-minute architecture walkthrough with Eddie Hudson, who leads our Emerging Technology practice.
Common questions
Will it make things up?
That is what the retrieval layer is for. The model answers from passages pulled out of your documents, and every answer shows which document it used so anyone can check it.
Do our documents leave our environment?
No. The pilot runs in your own Azure tenancy, in the region your policies require.
What formats do you support?
PDFs are the common case. We confirm the rest against your library during the fit check.
How is this different from a chatbot on our website?
A website chatbot answers from a script or a public page. This answers from your document library and shows its work.
What if we already have Microsoft Fabric?
Better. Fabric gives the retrieval layer a governed foundation to draw from. See Microsoft Fabric and Foundry.
Put Winmill’s AI and Azure team to work
Book a 30-minute architecture walkthrough with Eddie Hudson to talk through your documents and leave with a clear next step.