AI that runs in Azure, on your data, with answers you can cite
Winmill designs, builds, and runs AI and data systems inside your own Azure tenancy: document intelligence that cites its sources, Microsoft Fabric data estates, and machine learning that keeps working after launch. Fixed price first steps, documents you keep, and the same engineers who secure the software they build.
Four ways to put AI and Azure to work
Most organizations arrive with one question: can you build this, and what does it cost? Each service below answers that with a scoped first step, a stated price, and a written deliverable your team keeps whether or not you continue.
AI on Your Data
A retrieval-augmented generation (RAG) implementation that puts your organization’s documents one question away. Every answer cites the document it came from, and your people can open it. Not a chat bubble. A research tool.
Explore AI on your dataMicrosoft Fabric and Foundry
AI initiatives stall without clean, unified data. We stand up Fabric on your own data in a fixed price pilot that ends with a working report, a target architecture, and a capacity and cost model, then scope the full build from what the pilot proved.
Explore Fabric and FoundryData and Intelligence
Document intelligence, search that answers with sources, and machine learning that survives contact with production, all designed and built inside your Azure environment by the engineers who also secure it.
Explore data and intelligenceMachine Learning Operations
Machine learning operations (MLOps) keeps a model accurate once it meets real data: versioned deployment, monitoring, retraining, and measurement against the business outcome the model was bought to move.
Explore MLOpsYour tenancy, your data, your documents
Every AI and Azure engagement follows the same three rules. They exist because buyers of AI work fear the same three things: unbounded cost, a stalled project, and a system nobody in their organization can explain afterward.
Built in your Azure tenancy, not ours
We deploy into your own Azure tenancy under your controls. Your data never leaves your environment, existing Azure credit applies, and when we’re done the system is yours to run.
Small first step, price stated up front
Each service starts with a fixed price pilot on one real source or one document library. You see it working on your own data before you fund the full build.
Documents you keep either way
Pilots end with a written architecture, a cost model, and a fixed price proposal for the next step. If you stop there, you still have the documents and the working environment.
Why organizations trust Winmill inside their environment
Buyers of AI work aren’t shopping for an education in the technology. They’re deciding who to trust with data that matters. This is what that decision rests on.
Secured by the same firm
Winmill’s cybersecurity practice is in house, not subcontracted. The team that builds your AI system is accountable to the team that tests it.
Answers with provenance
Our RAG implementations cite the source document for every answer and let users open it. Your people can check the work, and so can an auditor.
We run production systems
Winmill has operated its own infrastructure in a SOC 1 and SOC 2 audited facility for three decades. Our AI advice comes from running systems, not only from designing them.
Engineers in your time zone
Nearshore delivery from teams in the United States and Mexico, working in your hours, in English and Spanish, with source code in repositories under your own account.
Frequently asked questions
What does Winmill’s AI and Azure practice cover?
Four connected services: AI on your data (RAG implementations that answer from your documents with citations), Microsoft Fabric and Foundry data estates, data and intelligence solutions including document AI and search, and machine learning operations that keep models accurate in production. All of it is built and run inside your own Azure tenancy.
Where should we start?
With one real problem and a fixed price pilot. If your answers are trapped in documents, start with a RAG pilot on one document library. If your data is scattered across systems, start with a Fabric pilot on one source. Both end in a few weeks with a working environment and written documents you keep.
Where does our data live?
In your own Azure tenancy, in your region, under your controls. Winmill deploys into your environment rather than hosting your data. Existing Azure credit applies, and we architect so that it does.
Can you build this and what does it cost?
Yes, and we tell you before you commit. Every engagement starts with a scoped pilot at a stated fixed price. The pilot produces a written cost model and a fixed price proposal for the full build, so the second decision is made with real numbers from your own data.
How do you handle security in AI projects?
Winmill’s cybersecurity practice is in house and led by CISSP and CISA certified staff. When we build inside your environment we build to your controls, restrict access to authorized users, and cite sources so answers can be checked. Our own infrastructure is SOC 1 and SOC 2 audited, and we can share reports under NDA.
Ready to see AI working on your own data?
Tell us what you’re working on, and we’ll bring the right engineers to the conversation. We respond within one business day.