AI Implementation for the Systems You Already Run
Adding AI to a greenfield product is easy. Adding it to a platform with ten years of data, integrations, and users is the real work. That is the work I do.
The gap is not the model
Most organizations are past the "should we use AI" question. The models are commodities, the APIs are a credit card away, and every team has run a demo that impressed someone. What they do not have is AI running inside the product their customers actually use, behaving predictably, on their data, at a cost they can forecast.
The gap is not the model. The gap is everything around it: a legacy schema nobody wants to touch, permissions that must be respected in every answer, workflows that cannot fail silently, and a codebase that was never designed to call a probabilistic service. Pilots stall at exactly that point.
I take AI across that gap and into production.
What I do
AI feature delivery inside existing products
I design and build the AI capability your roadmap calls for, integrated into your current platform rather than bolted on beside it: document understanding, guided intake and decision support, summarization and drafting, search that understands intent, and structured extraction from unstructured data. .NET and Azure first, but I work in the stack you have.
Retrieval over your own data
Retrieval-augmented generation on Azure AI Search, grounded in your documents, SharePoint, SQL Server, and line-of-business systems, with the access controls your users already have carried through to every answer. No data leaves your tenant.
Reliable AI workflows, not fragile prompts
Orchestration with Semantic Kernel and deterministic pipelines on Azure Durable Functions: retries, checkpoints, human approval steps, and a full audit trail of what the model saw and what it decided. Built so that compliance can sign off.
MCP servers that expose your systems to AI
The Model Context Protocol is how AI assistants and agents connect to real tools and data. I build MCP servers over your APIs, databases, and internal services so that Claude, Copilot, and your own agents can act on your platform safely, with authentication, scoping, and logging that your security team will accept.
Legacy platform readiness
Before AI can be added, the platform often has to be prepared: API surfaces where there were none, data cleaned and indexed, secrets and identity done properly with Entra ID, and cost controls in place. I do that groundwork as part of the engagement, not as a separate project you have to fund first.
How I work
Practical over experimental
I do not run science projects. Every engagement targets a feature in production, with success defined in terms your business already measures.
Deterministic by default
Language models are placed where they add value and wrapped in code where predictability matters. The result is a system you can test, monitor, and explain.
Your data stays yours
Everything runs inside your Azure tenant or infrastructure. Private endpoints, managed identities, no external calls you have not approved.
Cost you can see
Token usage traced per feature and per customer from day one, so AI does not become the line item nobody can explain.
Senior-led, start to finish
Designed, built and handed over with documentation and a team that understands it, senior-led throughout.
How an engagement runs
AI Implementation Assessment
Fixed scope, the entry point. Two to three weeks. I review your platform, data, and integration constraints, identify the highest-value use cases that are actually buildable in your environment, and deliver a prioritized implementation plan with architecture, cost estimate, and risks. You can execute it with me or with your own team.
Implementation sprints
I build the capability in staged deliveries, each one shippable, so the product keeps moving while the AI layer matures. Corp-to-corp, hourly, or fixed scope, under a defined statement of work.
Ongoing architecture oversight
Optional. A monthly fractional-architect retainer covering model and cost governance, evaluation, new use cases, and keeping pace with a platform landscape that changes every quarter.
Proof
TheraDesk.AI: an AI-native practice management platform, built end to end
As CTO I designed and built the full platform on Azure in about a year: architecture, backend, frontends, infrastructure as code, billing, compliance, and the AI layer itself, with Azure OpenAI and Semantic Kernel orchestrating regulated health data inside a single tenant. It is the reference for what production-grade AI on Azure looks like when one architect owns the whole stack.
→read the case studyAI intake agent for a change-management consultancy
A conversational agent that guides users through a structured intake, gathers complete project data, and generates a change management plan using the firm's proprietary methodology. Runs serverless on Azure Functions with Semantic Kernel, fully auditable, no external APIs.
Enterprise knowledge interfaces
Semantic search and question answering over SharePoint, SQL Server, and document repositories, with existing permissions enforced on every result.
Ready to put AI inside your product, not next to it?
Start with a fixed-scope AI Implementation Assessment. In three weeks you will know what is worth building, what it will cost, and how to ship it.
We’re on top of things and aim to respond to all inquiries within 24 hours.
Imhauser Technologies
McLean, VA
United States
Available on-site across the DC metro, Tysons, Reston, Arlington, and Washington, DC, as well as hybrid and remote worldwide.