aiiop.ai gives enterprise teams three things: a way to ask questions of their data, a way to build RAG pipelines on documents, and a way to train the models that power them. All run inside your infrastructure. Nothing leaves.
Teams get results in notebooks. Then spend months trying to make those results repeatable, auditable, and safe to run on real data. Most don't get there.
Query your databases. Build RAG pipelines. Train ML models. All on your own infrastructure.
Connect your databases and ask questions in plain English. No SQL, no analyst in the middle, no data sent to an external API.
Build production-ready RAG pipelines with a guided wizard. Bring your own vector DB, LLM, and embedding model — cloud APIs or self-hosted on your GPU servers.
Run ML training on servers you already have. Every experiment is tracked, every result is reproducible, and the trained model is yours to take anywhere.
BizChat lets your team ask questions of structured data today. RAS lets you build RAG pipelines over documents and unstructured content. Fabric lets your data scientists train custom models that power both.
You don't have to use all three. Start with whichever solves your immediate problem. But when you're ready to expand, the rest of the platform is already there — same infrastructure, same control model, same audit trails.
You connect your database, auto-generate API endpoints from your tables, and build AI agents that answer questions in natural language. The LLM generates Smart Payloads — it never sees your actual data.
You register your vector database, LLM, and embedding model. RAS handles the chunking, indexing, retrieval, and generation. You get a production-ready inference endpoint with rate limiting and audit logging.
You register a server, pick a training mode, upload your data, and run. Fabric handles the dispatch, the logging, the result collection, and the model registration. You get back a versioned artifact you can test, compare, and export.
A few examples. The pattern is the same: data that can't go outside, questions that need answers, models that need to stay reproducible.
A team that needs answers from their own data without sending that data to OpenAI or any other external API.
Build RAG pipelines over policy documents, contracts, or technical manuals. Get answers with exact source references.
When the data is spread across five systems and nobody on the business side knows SQL, BizChat becomes the layer that connects questions to answers.
Query thousands of contracts, regulations, or case files. Hybrid search with reranking ensures relevant results even with legal jargon.
Teams who need to be able to answer "what was that model trained on?" — whether for a regulator, a client, or their own QA process.
ITSM ticket routing, contract classification, clinical triage — cases where a generic model doesn't know your terminology and you need to train it on yours.
We're not a software vendor that hands over a licence and moves on. We work with your team from the start — understanding your setup, building on our platform, and making sure what we build actually runs in your environment.
Start with one. Add the others when you're ready.