AI that works on your data, in your environment

Winmill designs, builds, and runs AI and data systems inside your own Azure tenancy: document intelligence, search that answers with sources, and machine learning that survives contact with production. Built by the engineers who also secure it.

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  • Enterprise software since 1994
  • Microsoft Cloud Solution Provider
  • CISSP and CISA certified security leadership
  • Fortune 500 clients across North America
The problem

Your answers are trapped in documents

The overwhelming majority of organizational data is unstructured: contracts, reports, emails, PDFs, images. It’s exactly the material modern AI is good at reading, and exactly the material your current systems can’t search. The gap between the two is where decisions slow down.

AI initiatives also stall for a second reason: the data underneath them isn’t clean, connected, or governed. Most organizations discover this after they’ve bought the AI. We start with the data, so the AI has something true to say.

The flagship use case

Not a chat bubble. A research tool.

The most requested thing we build is a retrieval augmented generation (RAG) implementation: AI that answers questions from your organization’s own documents. Ours is built as a dedicated research tool, not a widget in the corner. Every answer cites the document it came from, and your users can open that document from the answer itself.

Access is controlled the way the rest of your systems are controlled, restricted to authorized users, and the whole thing runs on your documents whether that’s 800 papers or 80,000 contracts.

The offer, the pilot scope, and the fit check live on AI on Your Data.

What we build

Six ways organizations put us to work

Document intelligence

Turn contracts, invoices, and claims into structured data your systems can act on. Pipelines that cut manual entry and keep an audit trail of what was read and when.

Search that answers with sources

RAG implementations across your documents, databases, and systems. Natural language in, cited answers out, with the source one click away.

Prediction and forecasting

Machine learning models that forecast demand, flag anomalies, and power personalization, trained on your data and measured against outcomes you care about.

Data pipelines and platforms

The engineering underneath: ingestion, transformation, and serving on Azure, including Microsoft Fabric estates with a semantic layer your analysts can actually use.

Models that stay accurate

Deployment is the start, not the finish. We operate, monitor, retrain, and version models over time, so the system you launch is still right a year later.

Automation with a person in charge

AI agents that take real work off your team’s plate, built with the oversight, limits, and monitoring that let you trust them inside business rules.

Trust

Why regulated organizations trust us in their environment

Your tenancy, your controls

We build inside your own Azure environment, under your identity, your access rules, and your compliance requirements. The system is yours from day one.

Security is in-house

The cybersecurity practice that penetration tests and audits code is a Winmill practice, not a subcontractor. AI work here is reviewed by the same people who break systems for a living.

Real data stays in production

Development happens against fictitious data of identical structure. Your real data lives only in your production environment, where it belongs.

Start small

Pilot first, with the price stated up front

You shouldn’t have to fund a transformation program to find out whether AI fits. Our engagements start with a fixed price pilot that runs against your own data and ends with documents you keep: a working system, a written architecture, and a cost model. When the results prove out, the same foundation scales to production.

If your data estate is the starting point, the two minute Fabric fit check will tell you whether Microsoft Fabric makes sense for your environment.

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Our work

AI and data projects you can read

Three engagements that show the range: an AI platform MVP (minimum viable product) delivered in 12 weeks, a legal education platform with AI study tools, and a national benefit fund’s member services rebuilt on native Azure.

Common questions

Frequently asked questions

What is a RAG implementation?

Retrieval-augmented generation: AI that looks up relevant passages from your own documents before it answers, instead of answering from general knowledge. Our fixed price pilot for this is AI on Your Data. It’s how you get answers grounded in your contracts, policies, and research, with citations your users can check.

Does our data have to leave our environment?

No. We build in your Azure tenancy under your controls. Development happens against fictitious data with the same structure as yours; your real data stays in your production environment.

How do you keep the answers trustworthy?

Mechanically, not aspirationally. Every answer cites its source document, users can open the source from the answer, and access is restricted to authorized people. Where the stakes justify it, we add human review steps before an answer drives an action.

What does an AI project cost?

Pilots are fixed price and we tell you the number before any commitment. Larger builds are estimated with a stated not to exceed ceiling and conditional dated milestones, so the document you take to your leadership describes a bounded engagement.

Why Fabric?

Because AI is only as good as the data underneath it. Microsoft Fabric unifies pipelines, warehousing, and Power BI on shared storage, which is the shortest path to clean, governed data if you already run on Microsoft 365 and Azure. Our Fabric pilot puts it to work on your own data at a fixed price.

See what your data can answer

Run the two minute fit check, or book a 30-minute architecture walkthrough with Eddie Hudson and leave with a clear next step.

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