AI integrations and connections
AI securely connected to your own systems and data
Generic AI knows nothing about your business. We connect a model safely to your own documents and systems, through RAG, embeddings and function-calling, so it answers from your data and always shows its source.
- Answers with source citations
- ISO 27001-level security
- Native integration, no iframe
In short
AI on your own data means connecting an AI model to your own documents and systems, instead of letting it guess from general knowledge. A plain API or system integration without AI falls under our Systeemkoppelingen en integraties service, under Automation. Here we connect an AI model safely to your own data and systems, through RAG, embeddings and function-calling, with source citations and access control. At Planit Consulting a RAG pipeline makes 18,429 documents searchable with a source per answer; at Simply the AI runs native in Salesforce, built in Apex at ISO 27001 level.
What gets in the way
Ask a generic AI model something about your business and it makes an answer up. It doesn't know your archive, your clients, or your systems. And without a source citation you can never be sure it's right. That's exactly the gap we close here: we connect an AI model safely to your own data and systems. Mind the difference. A plain API or system integration without AI falls under our Systeemkoppelingen en integraties service, under Automation. This page is about AI on your own data: through RAG, embeddings and function-calling, with source citations and access control. At Planit Consulting we built a full RAG pipeline over 18,429 .docx documents, good for 95,592 searchable chunks with a citation back to the original file. At Simply the AI runs native in Salesforce, in Apex, at ISO 27001 level. No separate tab, but a real part of the system. We think along with you: we listen first to what you want to achieve now and where you are heading, then choose the technology to match, for the most scalable and powerful result that is cost-efficient at the same time.
Generic AI can't see your own data
ChatGPT and the rest know the whole internet, but nothing about your archive, contracts or CRM. Ask something specific and it invents a plausible-sounding answer. At Planit Consulting we fixed that with a RAG pipeline that can actually read 18,429 of their own documents.
Getting sensitive data to a model safely
You want AI on your data, but your candidate, client or case data can't just end up anywhere. The question isn't whether AI can, but whether it can safely. At Simply we built everything at ISO 27001 level: controlled access, strict 2FA and full logging.
An LLM that answers without a verifiable source
An AI that confidently claims something without saying where it came from can't be trusted in your work. You need to click back to the source file. At Planit every answer points back to the original document, so consultants can always verify.
AI in a separate tab instead of in your stack
Plenty of AI tools sit alongside in a limited iframe. You copy data back and forth and the time saved evaporates. At Simply we built the AI native in Salesforce, in Apex, so the work happens where your users already are.
What we build
How we help
Full RAG over your own corpus
We build a complete RAG pipeline over your documents: classify, semantically chunk, embed and make searchable. With embeddings, a vector store and a RAG model that points each answer back to the original file, so you get answers based on your own data.
Function-calling plus vector search
We let the model not just read but act: a prompt that drives functions and automations. A native agentic AI chat runs functions and flows from a prompt, while every summary is vectorised so you can search it later.
Native, secure integration, no iframe
We wire the AI into your system instead of beside it. A native app inside your platform, with permissions, certificates, oAuth and external credentials, at ISO 27001 level with its own VPN, strict 2FA and full logging. No iframe, but a part of the system.
Source citations and access control built in
Every answer comes with a pointer to the source file, so you can check it. And the model only sees what it's allowed to see. From the answer you click back to the original document, and access control decides, down to permission level, who can reach which data.
Embeddings that make your archive searchable
We turn your documents and summaries into vectors, so searching on meaning works instead of on exact words. Everything sits in a persistent vector store that returns the right passage in seconds, even across a large archive.
Proof
We have built this
Real projects we can name.
Planit Consulting
Planning consultancy
An AI chat that makes 18,429 planning documents searchable, with source citations.
18,429
Documents
95,592
Chunks
Simply
Recruitment SaaS
An AI recruitment suite, a native Salesforce app, a secure back-office and a marketing site. The full Simply stack, built end to end.
4-6 hrs/week
Recruiter time saved
Native Salesforce (Apex)
Integration
Our approach
From idea to working software in four phases
Every project runs through the same four phases, so you always know what's happening, what comes next and what it costs — from first call to live software, usually in weeks rather than months.
- 01 Phase 1 Free intake
Understand & analyse
Everything starts with a good conversation. We map your goals, processes and the bottlenecks worth solving — no sales pitch, just an honest read on where the biggest win sits.
- 02 Phase 2 Free blueprint
Blueprint & quote
We turn the analysis into a blueprint: a clear plan showing exactly which steps we'll take and why, paired with one fixed quote. You know up front precisely what we build, what it costs and when it's done — no open ends, no hourly billing, no surprises afterwards. And getting that blueprint is completely free, no obligation.
- 03 Phase 3 Prototype in days
Build, test & deploy
Then we build, in short iterations. You see a working prototype in days and a finished product in weeks. We work on a modern, AI-native and secure stack — with data protection and GDPR in mind from day one.
- 04 Phase 4 100% your code
Implement & optimise
We launch, hand over the full codebase and keep improving on your terms. You get 100% ownership of the code we write — no lock-in, no licensing games. Hosting and maintenance are optional, never required.
What you always get
Fixed price
Agreed up front, never an open end.
100% code ownership
Your code, fully yours, no lock-in.
Live in weeks
Prototypes in days, finished in weeks.
Modern & secure
A scalable stack that always integrates with the latest tech. Secured by our in-house cybersecurity experts.
Tools & tech
We're not tied to one model or platform. We pick the AI approach that fits your data, budget and goal — an off-the-shelf model, a custom pipeline, or a mix — and we're honest when simpler tech does the job just as well. No hype, no lock-in.
What you get
- A RAG pipeline over your corpus: classification, chunking, embedding and vector store
- An AI model that answers from your data, with a source per answer
- Function-calling so the AI can drive your own functions and automations
- A native, secure integration with your system, not an iframe beside it
- Access control and logging, so the model only sees what it's allowed to
- Vector search over your documents and summaries, searching on meaning
- Clear documentation and a clean handover to your own team
- A fixed price agreed upfront, with no open ends
Our promise
Your software stays yours
- Full code ownership The full source code is and stays yours, documented and ready to hand over.
- No vendor lock-in Stop working with us and your system keeps running. Any developer can pick it up.
- Fixed price up front You get a no-obligation blueprint and quote up front: no open-ended billing and no surprises afterwards.
- Security in every layer We build security-by-design, with a specialist in-house.
Frequently asked questions
What does it cost to get AI built on my own data?
You always get a fixed price agreed upfront. The scope depends mostly on your corpus and your systems: how many documents, how clean the data is, and whether the model only needs to read or also needs to act. A scoped RAG chat over a tidy set of documents is faster and cheaper than a full pipeline over a heterogeneous archive like Planit's, with 14 document types across 3 tiers. We map it first and then price exactly what you need.
How long before an AI integration like this is live?
That depends on the state of your data and the number of systems. We work in short iterations, so you see a working version quickly instead of waiting months. At Planit, classification was the first step: without reliable typing, retrieval never gets relevant, so that's where it begins. The cleaner and better-ordered your archive, the faster it goes live. We agree the exact timeline upfront.
Does the integration stay mine, and who maintains it?
The integration runs on your own environment and data, and the code and flows are yours, with documentation and a handover. No vendor lock-in: your own team or another party can pick it up. If you want us to maintain it, for instance as your corpus grows or a system changes, we can, on clear terms, but you're never tied to us.
Will this AI replace my people?
No, it takes the search work off their plate. At Planit Consulting the consultant still decides what makes a good precedent and how the advice reads; the AI just finds the right passages faster, with the source attached so they can check it. At Simply, recruiters still run the conversations; the AI takes over the admin around them. Your people get more time for the work that really needs their judgement.
Is AI really needed here, or would a plain integration do?
Fair question, and the answer is often: a plain integration. If you only need to move data from A to B, you don't need AI, and you fall under our Systeemkoppelingen en integraties service, under Automation. AI only adds value when a step calls for understanding text: searching by meaning through a large archive, matching a transcript to a profile, building a branded document. We'll tell you honestly when an integration without AI serves you better and cheaper.
How do I know the AI isn't making things up?
Because we tie every answer to a source. At Planit Consulting the RAG chat points each answer back to the original document, so a consultant can always verify where something came from. The model draws from your own corpus, not general internet knowledge, and what it can't find in your data, it doesn't invent. Verifiability isn't an extra, it's the starting point.
Ready to build?
Book a no-obligation call — we'd love to think through the best approach with you.
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