API and Systems Integration
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AI integration services for real business systems
We add AI to software that already runs your business: search over your documents, drafting and summarising, classification, and agents that take actions through controlled tools. Our own product, cMCP, is a governed memory layer for company AI.
What it is
AI and LLM Integration: what you get
AI integration means connecting large language models to your data and workflows so they do something useful and dependable. That could be a support assistant that answers from your own documentation, a tool that extracts fields from messy documents, an internal search that understands meaning as well as keywords, or an agent that can look up an order and draft a reply for a person to approve.
We approach it as software engineering first. A prototype is easy; a feature that behaves predictably, respects permissions, fails safely and can be tested is the real work. We handle retrieval-augmented generation (RAG), prompt and output design, evaluation on your real examples, logging, cost control, and the guardrails around what a model may see and do.
We also build MCP (Model Context Protocol) servers, the open standard that lets AI apps use your tools and data in a controlled way. IT-Labs builds its own governed shared-memory layer for company AI, cMCP, which is currently in a live pilot, so we work on these problems ourselves, not just in client projects.
Looking for shared, governed memory across your people and AI tools? See cMCP, our own product.
Problems we solve
Typical problems this solves
Answers buried in documents
Policies, manuals and past tickets exist but nobody can find them. RAG gives staff a way to ask and get sourced answers.
Repetitive text work
Drafting replies, summarising threads, tagging and routing requests, and extracting data from documents.
AI tools that forget everything
Assistants start cold every session. A governed memory layer keeps shared context current and permission-checked.
Worry about data leaving the company
We can design for models on your own server, or limit what any hosted model sees, and keep an audit trail.
A prototype that cannot go live
We turn a demo into a feature with testing, monitoring, fallbacks and human review where it matters.
Deliverables
What we deliver
- A scoped use case with clear success criteria before building
- The AI feature or agent integrated into your application or workflow
- Retrieval over your documents with permission-aware access where needed
- An MCP server that exposes your tools and data to AI apps safely
- Evaluation on real examples and logging of what the model saw and did
- Documentation, and guidance on running costs and model choice
Technologies we use
- LLM APIs
- Self-hosted models
- Embeddings
- Vector search
- RAG pipelines
- MCP servers
- Python
- TypeScript
- PostgreSQL
- REST APIs
- Docker
We work in any language. See all technologies.
Engagement
How the engagement works
We start with a free consultation and one specific use case. We tell you honestly whether AI is the right tool; for some problems, a rule or a query is more reliable. We then build a small working version against your real examples, so you can judge quality before investing more.
From there we harden it: permissions, logging, evaluation, fallbacks and cost controls. You keep the code and choose where it runs. We make no promises about savings or accuracy in advance, because those depend on your data; we measure on your examples and show you the results. If shared memory for your teams and AI tools is the real need, look at cMCP.
Questions
Common questions
What is RAG and do I need it?
Retrieval-augmented generation lets a model answer using your documents instead of only its training. You need it when answers must come from your own, changing content.
Can the AI run on our own servers?
Often, yes. Open models can run on your infrastructure, which suits sensitive data. Quality and hardware needs vary, so we assess this per project.
What is an MCP server?
MCP is an open standard for connecting AI apps to tools and data. An MCP server exposes specific, permission-checked capabilities of your system so any AI app that speaks MCP can use them.
Will the AI make mistakes?
Models can be wrong. We design for that with sourced answers, human approval for important actions, evaluation on your examples and clear logging.
How is cMCP related to this service?
cMCP is our own product: a governed shared-memory layer for company AI, in a live pilot. See the cMCP section of this site.
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Tell us what you need built
Describe the problem, the system or the idea. We offer a free initial consultation and a written estimate, and you will talk directly to the developer who would build it.