Local AI
RAG Systems for Business: AI That Answers From Your Corporate Data
We build on-premise RAG systems for business: private AI that answers your teams' questions from your own documents, policies and systems, cites its sources, and can be trained on your corporate data. Your data never leaves your building.
Why it matters
What is a RAG system, and why does your business need one?
Your corporate knowledge is spread across policies, procedures, manuals, contracts, tickets and code. People spend time searching, or ask a colleague who is just as busy, and general-purpose AI can't help because it has never seen your data.
A RAG system changes that. RAG stands for retrieval-augmented generation: before the AI answers a question, it retrieves the relevant passages from your own documents and data, then answers from them and shows its sources. The result is a private AI assistant that knows your business, built on your own hardware so your data stays in your building.
What we offer
Our RAG development services
- Assessment of your documents, data sources and use cases
- Design and build of an on-premise RAG system
- Connection to your documents, knowledge bases and business systems
- Answers that cite their sources
- Access controls and security
- Optional AI model trained on your corporate data
- Testing of answer quality before rollout
- Ongoing improvement and support
Use cases
Where teams use it
Internal AI knowledge base
One place to ask about policies, procedures and how things work.
RAG chatbot for company documents
Plain-language answers from manuals, contracts and reports, with sources.
Help desk and customer service
Fast, consistent answers for the staff who support customers and colleagues.
Code and technical documentation
Engineers ask questions about systems, code and technical documents.
RAG plus custom training
Enterprise RAG with AI trained on your corporate data
Answers from your data
The RAG system retrieves from your current documents and data, so its answers keep pace as your information changes.
A model trained on your data
For specialized work, we also train the AI on your corporate data, so it understands your terminology, products and way of working.
Measured, not assumed
We test answer quality against your own questions and sources, so you can see the results before you rely on them.
RAG for your COBOL code
The same approach works on source code. Our AI for COBOL lets your developers ask questions about your legacy systems, on your own hardware.
How a project runs
From first conversation to support
- Discover
We identify your use cases and the documents and systems that hold the answers.
- Connect
Your documents and data sources are prepared and connected to the RAG system.
- Secure
Access controls are set up so the system respects who may see what.
- Test
Answers are checked against your sources and your teams' real questions.
- Improve
We tune the system, train the model on your data where it helps, and support it over time.
Who it's for
Built for organizations like yours
- Organizations with large document, policy and procedure libraries
- Banks and other regulated organizations that can't send data to cloud AI
- Teams that want an enterprise RAG solution on their own hardware
- Engineering teams that need answers about systems and code
FAQ
RAG systems: FAQ
What is a RAG system?
RAG stands for retrieval-augmented generation. The AI retrieves the relevant passages from your own documents and data, then writes its answer from them and shows its sources.
Does our data leave our building?
No. We build the RAG system on your own hardware, on your premises, and your documents are never sent to outside AI services.
Is the AI trained on our corporate data?
It can be. A RAG system answers from your data without changing the model. For specialized work, we also train a custom AI model on your corporate data so it understands your terminology. You can use both together.
Can we see where an answer came from?
Yes. Answers cite their sources, so your team can check the original document.
Can we control who sees what?
Yes. Access controls and security are part of every RAG system we build.
Is a private LLM the same as a RAG system?
A private LLM is the language model running on your own hardware. A RAG system connects that model to your documents and data, with access controls and answers that cite their sources.
Local AI only
Everything runs on hardware you own, on your premises. Nothing is sent to outside AI services.
Plan your local AI.
We design and build the hardware, or deploy on the hardware you already own. Your data stays in your building.