Wire AI Into the Systems
Already Running Your Business
Most AI projects do not fail at the model. They fail at the integration, the auth boundary, the data that cannot leave the building, the latency budget, the legacy codebase nobody wants to touch. That part we have been doing for over twenty years.
Where we are, plainly: AI delivery is the newest line in our practice. We have built AI features in-house and we have 20+ years of enterprise .NET and Azure integration behind us, but we are not going to show you a wall of AI logos we have not earned. If you want an AI partner with a decade of AI case studies, that is not us. If you want engineers who genuinely understand the system you are putting AI *into*, keep reading.
The Demo Works.
The Integration Is the Hard Part.
A proof of concept on clean sample data proves almost nothing about what happens inside a system that has been accreting business rules since 2009.
The Codebase Cannot Just Be Rewritten
Your line-of-business application encodes years of decisions, edge cases, and regulatory constraints. AI has to be added alongside it, through seams that already exist or seams we create carefully, not by replacing what works.
Your Data Cannot Leave Its Boundary
Who can ask what, about whose records, and where does that text get processed? If the model can see more than the user is entitled to, you have built a data breach with a chat interface.
Token Cost and Latency Are Product Decisions
An unbounded prompt is an unbounded invoice. A four-second response is a feature nobody uses. Both need to be designed for, measured, and capped before go-live, not discovered in the first billing cycle.
Nobody Defined What 'Working' Means
Without an evaluation set, 'the AI is wrong sometimes' is an unfalsifiable complaint. We agree what good output looks like and test against it before the feature reaches a user.
Production Systems First,
Not Greenfield Demos
We do not start with the model. We start with your system: how it authenticates, where its data actually lives, what its performance envelope is, and which workflows a language model could genuinely improve. Then we add the smallest thing that delivers value, instrument it, and prove it works before extending it. The result is a feature your team can own, audit, and cost-forecast, rather than a prototype that never survives contact with real users.
The interesting question is never 'can the model do this'. It is 'can we put this into your system without breaking it, leaking anything, or costing you a fortune to run'.
— Anil Channa, Founder, Softwiz InfotechAI Integration, End to End
Scoped to your estate, the models, the plumbing, the guardrails, and the evidence that it works.
Azure OpenAI Integration
Model deployment in your own Azure tenant, so prompts and completions stay inside your subscription and your compliance perimeter. Managed identity, private networking, and per-feature quota.
Semantic Kernel & Agent Framework
Orchestration in C#, native to the stack your team already maintains: plugins over your existing services, function calling into real business logic, and Microsoft Agent Framework where multi-step work justifies it.
Integration With Existing .NET Code
From .NET Framework 4.x through .NET 10. We work through the seams your application already has: service layers, message queues, APIs, rather than demanding a rewrite as the price of entry.
Auth & Data Boundaries
The model inherits the user's permissions, never more. Retrieval is filtered by identity before it reaches the prompt, so a language interface cannot become a privilege-escalation path.
Cost & Latency Control
Prompt budgets, caching, model tiering, streaming responses, and hard ceilings. You get a per-feature run-cost estimate before we build, and telemetry to check it afterwards.
Audit Trails & Evaluation
Every AI-assisted decision logged with its inputs, retrieved context, model version, and output, the record a regulated business needs. Plus an evaluation set agreed with you before anything ships.
What We Have Actually Built
Two AI features we built ourselves, and the integration track record underneath them. Labelled so you can tell which is which because a firm that blurs that line will blur others.
Natural-Language Programme Finder
Clients on a fitness platform could be invited to buy a coach's programme, but had no way to find a programme matching their own goals. We added a conversational assistant: a client describes what they want, and it returns matching programmes with the studio that offers them, so they can go and buy it.
Conversational retrieval over an existing product catalogue, integrated into a live .NET API.
Computer-Vision Attendance Tracking
We replaced manual attendance marking with a system that reads our existing office CCTV feeds, recognises entries and exits per staff member, and reports the counts on a dashboard. Built and running on our own premises, on our own staff, before we would consider offering it to anyone else.
Computer-vision inference over existing camera infrastructure, with a .NET backend and web dashboard.
Integration Into a System That Cannot Fail
Carrier data feeds in PNRGOV and PAXLST message formats, plus watchlist connectors, integrated into a border-security platform used by member states. Not an AI project, but precisely the discipline that AI integration into a regulated system demands.
.NET · PNRGOV / PAXLST message formats · watchlist connectors
The Platform Layer Under an AI Product
Vocean is a Microsoft-listed platform for structured innovation. Anil built and maintained its .NET backend APIs and collaboration engine, the sessions, participants, and data handling an AI product runs on top of. Vocean's own team owned the AI analysis features; we are not claiming them.
.NET backend APIs · collaboration engine · multi-tenant data model
From Assessment to Production
A paid assessment first, because scoping AI work properly is real analysis, not a sales call.
Readiness Assessment
A fixed-scope, fixed-price 2 weeks engagement. We map viable use cases against your data and integration reality, and tell you which ones are worth doing, including any that are not.
Thin Slice
One workflow, end to end, in a non-production environment: real integration, real data boundaries, real cost measurement. Enough to prove the approach before you commit further budget.
Harden & Evaluate
Evaluation against the agreed set, prompt and retrieval tuning, failure-mode handling, cost ceilings, and audit logging. This is where a demo becomes something you can defend.
Ship & Support
Phased rollout behind feature flags, telemetry your team can read, and a handover your engineers can maintain. We stay accountable after go-live, as we do on every system we run.
What We Build With
Microsoft-native by design, the AI layer sits in the same ecosystem as the application it serves, and the same team can maintain both.
Find Out What Is Actually Viable, In Two Weeks
Before anyone writes a prompt, get a written assessment of which AI use cases in your estate are worth building, what your data and integration constraints are, and what each would cost to run. Fixed scope, fixed price: $3,500.
Have a System That Should Be Doing More?
Tell us what it does today and where you think intelligence would help. We will tell you honestly whether AI is the right answer and if it is not, what is.