We want our own documents searchable by AI
RAG (retrieval-augmented generation) does not need a dev team. Start with what you have and only then go custom.
Try this first
- 1Inventory first: which documents do you actually want searchable? Fifty PDFs with stale versions: clean up first, AI second.
- 2All in M365? Try Copilot Chat scoped to SharePoint sources or an agent in Copilot Studio. No code, tenant data boundary in place.
- 3Google Workspace? Similar setup with Gemini and NotebookLM for scoped document sets.
- 4Not happy yet, or outside M365/Google? Then a real RAG (LangChain/LlamaIndex on Azure OpenAI or similar) is on the table. That is a project.
- 5Set measurable goals. 'People find the right quote template in 30 seconds' beats 'AI on our data'.
When to bring us in
Once code and self-managed vector stores enter the picture, you want someone maintaining it and keeping hosting bills sane. That is us.
See also
- Can I paste a customer file or email into ChatGPT?Depends on the account and settings. Free ChatGPT and a Team tenant behave very differently from what most people assume.
- I want a one-page AI policy for my teamA real one-pager beats a thick document nobody reads. Four headers and concrete examples.
- How do I tell if an AI answer is made up?Models sound confident even when they are wrong. A few habits catch most mistakes.
None of the above fits?
Describe your situation below. We pass your input plus the steps you already saw to our AI and return tailored next-step advice. If it's too risky to DIY, we'll say so.
Or skip the DIY entirely
Our Managed IT clients do not look these things up. One point of contact, a fixed monthly price, resolved within working hours.