Built on 30 years in the systems that run underneath everything
AzureForge AI exists because most cloud transformations get sold by people who've never had to keep one running at 2am. Ours is led by someone who has — and who has also built and sold companies of his own, so he knows what the business actually needs from the technology.
From Cloud based development fabrication to generative AI
Founder has spent over 30 years working in fast-paced, high-stakes environments — the last decade focused squarely on cloud infrastructure and complex data services. As a senior-level architect, he's designed and built end-to-end systems for organizations ranging from national logistics companies to regional insurers to nonprofit platforms. End-to-end means exactly that: the hybrid network back to the data centre, the Azure landing zone, the data platform, the models that run on it, and the applications the business actually touches.
The projects that go wrong rarely fail on the architecture diagram — they fail in the seams between teams, in the migration weekend, in the on-call rotation six months after go-live. Three decades of being the person holding the pager is what shapes how he scopes work now: designs that a small team can actually operate, cut-overs planned backwards from the rollback, and documentation written before the knowledge walks out the door.
What sets his approach apart is the other half of his background. He's also founded and sold three companies, including a medical practice management and EMR company that grew to $350M in annual sales and 250 employees, raising $35M in private investment capital along the way. He's sat on both sides of the table — as the technologist building the system, and as the CEO whose payroll depended on it working.
That combination is why AzureForge AI is built around a single idea: architecture, data, and AI aren't three separate vendors to hire. They're one pipeline, and it should be led by someone who's owned the outcome, not just the ticket.
Where AzureForge AI stands today
AzureForge AI is the current chapter of that work — four years in, running Azure analytics with generative design capabilities. The practice uses k-Nearest Neighbors and unsupervised learning to surface insight from data clients already have, layered on a hybrid Azure/on-prem network built with ExpressRoute and site-to-site VPN.
Recent engagements have added retrieval-augmented generation on top of that foundation, so teams can ask plain-language questions of their own contracts, tickets, and reports and get answers grounded in the source documents, with citations back to them. The through-line hasn't changed in 30 years: build the system the business needs, make it something a real team can run, and stay accountable for it after the invoice is paid.
What the team works on
Thirty years in production systems, the last decade concentrated on Azure. This is the working toolkit — technologies used on live engagements, not a certifications wall. Scroll across the areas.
Cloud & infrastructure
Designing and operating Azure estates end to end — the landing zone, the network back to on-prem, identity and secrets, and the CI/CD pipelines that ship to them. The emphasis is on environments a small team can run without heroics: reproducible through Terraform, observable by default, and cut over with a tested rollback.
Data engineering
Moving data from source systems into a modelled, queryable platform — ingestion with Data Factory, storage in Data Lake and Cosmos DB, transformation in Databricks and Synapse, and a governed reporting layer in Power BI on top. Dimensional modelling keeps the warehouse answerable to the business rather than a mirror of whatever the source app happened to store.
AI & machine learning
Applied models that earn their place: retrieval-augmented generation over a client's own documents through Azure OpenAI, and classical methods — k-Nearest Neighbors, unsupervised clustering — where they're the right tool for the pattern. Every model is grounded in first-party data and evaluated against it, so answers can be traced back to a source rather than trusted on faith.
Delivery & leadership
Running the engagement as well as the architecture — scoping the work against a budget, leading mixed onshore and offshore teams through delivery, and owning the result. Having carried a P&L and sold three companies, the trade-offs between cost, speed, and risk get made the way an operator makes them, not the way a billed hour does.
Here is the team of super heros behind AzureForge AI
Founder / CEO
Thirty years in production systems and three companies founded and sold. Sets the architecture direction, scopes the work against the business, and owns the outcome.
Marcus Ellison
Designs the service boundaries, API contracts, and database schemas the site runs on. Works consultatively — gathering requirements and weighing trade-offs between consistency, latency, and cost before committing to a structure that's hard to change later.
Priya Raman
Owns the Azure hosting, networking, and deployment path, with security and running cost weighed on every call. Brings Infrastructure-as-Code discipline so every environment is reproducible from a repo rather than hand-tuned and undocumented.
Leo Vance
Builds the interface against the design system — component structure, styling, state, and interaction. Owns the numbers that users feel: bundle size, render performance, keyboard paths, and WCAG-level colour contrast in both light and dark themes.
Sofia Marchetti
Carries a feature the whole way, from the UI down through the server logic to the database. Lives in the seam where frontend and backend meet — request shapes, error states, auth — so nothing falls between two specialists.
Deshawn Carter
Leads the cross-platform mobile side — React Native and Flutter, native integrations, and offline data sync. Plans for app-store review, push notifications, and staged rollout from the first sprint rather than bolting them on at the end.
Hannah Whitfield
Designs the test strategy and risk model, then works the build methodically to find defects before a release does. Every finding comes with the reproduction steps and the data to justify a fix, not just a bug number in a queue.
Amara Okafor
Writes and runs the unit and integration suites, starting with the code that carries the most risk if it breaks. Reports pass or fail with a suggested fix, and never edits application code to force a test green.
Julian Reyes
Turns a rough idea into a structured PRD and a starter project brief. Pins down scope, success criteria, and the interfaces between components up front, so the build teams are implementing a decision rather than guessing at one.
Elena Kowalski
Renders every page in a real browser and critiques it — accessibility, visual hierarchy, typography, colour, spacing, and responsive behaviour down to mobile widths. Reports problems in priority order and hands them back; she reviews the work, she doesn't rewrite it.
Nadia Brooks
Turns the technical detail into documentation that reads clearly for developers, end-users, and stakeholders alike. Holds the style and information architecture together as the site grows, so page fifty still matches page one.
Want to talk through your systems with us directly?
No account managers, no hand-offs — you'll be talking to the person who's actually going to architect the work.