AI Integration
AI that fits into what you already run.
You don't have to replace your systems to add AI. We build the layer on top - production-grade, not a science project - without changing your infrastructure.
Sounds familiar?
You're here because something needs to work better.
Process mismatch
Your current tools don't fit the process, so people work around them in spreadsheets.
Vague scope
Every vendor quote comes back vague, and you can't tell what you're paying for.
Developer lock-in
The last custom system was abandoned - or only one person understands how it works.
Our approach
From the real problem to reliable software.
01 - Assess
We learn your process and the real problem.
02 - Plan
Architecture and roadmap agreed up front.
03 - Build
Tight iterations, working software early.
04 - Support
Documentation, handover, long-term care.
Chatbots
System-integrated chatbots (WhatsApp / Web / Teams) that read and write to your database.
Document AI
Automatic extraction from invoices, contracts, and receipts, straight into your system.
Semantic Search
Smart search across legacy data: “clients who mentioned a similar issue before.”
AI Co-pilot
A “?” button that explains any screen in plain language, cutting support load.
The tools we build with.
Modern foundations chosen for maintainability, security and speed.
- SQL Server
- .NET Core / C#
- Vue.js
- Access-MCP & AI
- Cloud infrastructure
Why A-Point Systems
- We ship AI to production, not demos
- We built the tools (MCP)
- Your data stays where it is
- Deterministic-first, AI-second
FAQ
Will AI see our sensitive data?
You control exactly what the AI can access. We design for least-privilege, and for many workflows your data never leaves your environment.
Do we need to move to the cloud?
No. AI features can run against your existing on-prem or hybrid setup.
What does an AI project cost?
It depends on scope - a focused chatbot is very different from a full document-AI pipeline. You get a fixed-price proposal after discovery.
How long until it's in production?
A focused first use case can be live in a few weeks. We start with something real, not a demo.
Which AI models do you use?
We're model-flexible through a LiteLLM gateway - we pick the right model per task and can switch as the landscape changes.
Can AI work with our old Access database?
Yes - that's exactly what our Access-MCP tooling is for.
What if the AI gets something wrong?
We design deterministic guardrails around AI outputs, and keep a human in the loop for anything consequential.
Is this just hype?
We ship AI to production for real clients, not demos. If a use case doesn't add real value, we'll tell you.
A logistics client cut invoice data-entry time dramatically with Document AI.
25 minutes → 30 seconds