AI integrated into the systems you already run
A contract software engineer who bridges enterprise .NET, SQL Server, and Azure with modern AI - designing, building, securing, and deploying AI-integrated systems past the demo and into production.
Where enterprise AI projects get stuck
These patterns show up in almost every engagement.
The pilot that never ships
A working demo that never reaches production - because integrating it with real .NET services, SQL Server data, and Azure security is harder than the prototype suggested.
Legacy systems left out of the picture
AI generalists don't know your .NET codebase or your SQL Server schema. The stack most teams actually run is the one most AI contractors won't touch.
No AI-specific security controls
Least-privilege for agents, prompt-injection hardening, and evaluation harnesses aren't built into most AI projects - and security reviews catch that gap late.
AI spend that's impossible to predict
Per-call billing with no cost model means costs surprise you at month-end. Designing to a predictable per-user cost envelope isn't automatic - it has to be built in.
Data locked away, reports still slow
BI teams are a bottleneck. Ad-hoc questions take days. The data exists in SQL Server - but getting answers out still requires a developer or an analyst with calendar availability.
Repetitive multi-system processes still manual
Onboarding flows, compliance checks, data migrations - the kind of work that jumps between C# services, databases, and APIs, and currently requires a person at the keyboard for every iteration.
What I build
Eight AI integration patterns for teams running enterprise .NET, SQL Server, and Azure.
Agentic Workflows & Autonomous Agents
AI that plans and executes multi-step work - calls APIs, queries databases, runs code, loops until done, or fires a human-in-the-loop gate. Built on Microsoft Agent Framework / Semantic Kernel, wired into your C# logic and SQL data through MCP and tool-calling.
Close the loop - agents that take the action, not just suggest it.
Structured Extraction & Classification
Turn unstructured input - emails, PDFs, tickets, contracts -
into structured rows, labels, and routing decisions. Often runs in the database via
SQL Server 2025 EXTERNAL MODEL to classify rows in place, or app-layer
with small, cost-efficient models for high-volume throughput.
Stop paying people to retype what a model can extract in place.
Natural-Language-to-SQL & BI Agents
Business users ask questions in plain language; the system generates safe SQL against your databases, returns answers, and can alert on KPI thresholds. Built on SQL Server / Azure SQL with read-only roles, query validation, parameterisation, and guardrails most generalists get wrong.
Answers in minutes, not a ticket to the BI queue - built safely against your data.
Inline AI Augmentation
Small, fast AI helpers embedded inside tools people already use - suggested replies, field auto-fill, summarise-this, draft-this. Not another chatbot. Built with low-latency calls in existing ASP.NET / Blazor apps using the smallest model that hits the bar, with streaming UX.
High-leverage AI where your team already works - no new app to learn.
Knowledge Assistants (Defensible RAG)
Assistants grounded in your company data with citations.
Built on SQL Server 2025 native VECTOR type, DiskANN vector index, and
VECTOR_SEARCH - no separate vector database required.
On-prem/hybrid capable for data residency. Permission-aware retrieval so users only
see what they're authorised to see.
A copilot grounded in your data - on your infrastructure, respecting your permissions.
Custom Copilots for Microsoft-First Shops
Copilot Studio and M365 copilots wired into real line-of-business systems. Out-of-the-box copilots are fast but shallow - I fill the integration gap, connecting copilots to the legacy .NET and SQL systems they don't reach natively.
Make your copilot actually do the work - wired into the systems that run your business.
AI Governance, Security & Evaluation
Make AI systems safe, auditable, and measurably correct - agent registries, least-privilege credentials, prompt-injection and adversarial testing, and evaluation harnesses. Backed by a Security Engineer certification and IaC delivery. Available as a standalone audit on systems someone else built.
Ship AI you can defend in a security review - and prove it works.
Cost-Disciplined Delivery
Every engagement is designed to a cost envelope: cost-per-user-action rather than cost-per-call, smallest model that meets accuracy, aggressive caching. If a design pattern can't hit the budget, I change the pattern - not the model.
AI that fits a budget you can predict.
How we'll work together
From first conversation to live, production AI - in clear, practical steps.
1) Assess
I review your systems, data, and workflows to find where AI integration will deliver the clearest, most measurable return.
2) Plan
We agree on scope, expected outcomes, cost envelope, and the smallest first step that proves value in your real environment.
3) Build & secure
I integrate, harden, test, and iterate with short feedback loops - security and evaluation built in from the start, not bolted on at the end.
4) Ship & handover
Production deployment via IaC, full documentation, and the confidence your team needs to extend and operate the system independently.
Why this matters for enterprise teams
Most AI contractors are Python-first and cloud-native. The combination of enterprise .NET, SQL Server, Azure, security, and IaC with hands-on AI integration is genuinely rare.
Rare stack intersection
Enterprise .NET, SQL Server, Azure, security, and IaC - plus hands-on AI integration. That combination is what enterprise teams need and rarely find in one contractor.
Ships past the demo
Every engagement is focused on production impact, not pilots that stall. The gap between a working demo and a deployed, governed, cost-predictable system is where most AI projects fail - and where I work.
Security-first by default
Permission-aware retrieval, least-privilege agents, and prompt-injection testing - backed by a Security Engineer certification. AI governance is built in from day one, not added when a security review flags it.
Data stays put
On-prem and hybrid RAG using SQL Server's native vector capabilities - no separate vector database, no data leaving the boundary. Right for regulated environments and data-residency-sensitive clients.
Cost-disciplined from the start
Designs to a predictable per-user cost envelope. Smallest model that meets accuracy, aggressive caching, and cost-per-action thinking baked into every architecture decision - not discovered at month-end billing.
Engaged as a partner
Remote, on contract, with IaC-delivered infrastructure and full documentation on exit. Your team owns the outcome - not a black box or a platform subscription with no off-ramp.
Built on a certified enterprise foundation
The AI integration work sits on top of a decade of enterprise software engineering - C#/.NET, SQL Server, Azure, and security.
- Azure DevOps Engineer Expert - delivery pipelines, IaC, CI/CD
- Security Engineer - hardening, least-privilege, threat modelling
- Azure Developer Associate - cloud-native .NET development on Azure
- Hands-on day-to-day with agentic AI, and MCPs
From pilot to production - securely
Tell me what you're trying to integrate. I'll identify the right pattern, the right stack, and the right way to ship it.
Book a free call