AI & Data
RAG Systems That Answer from Your Private Knowledge
MultiLLM AI Platform uses Retrieval-Augmented Generation to combine private documents with LLM responses. Documents are split into chunks, converted into embeddings, stored in Weaviate and retrieved semantically at chat time so the model can answer with relevant company context.
What We Build
Enterprise document Q&A
Policy and procedure assistants
Legal knowledge search
Product and support knowledge assistants
Internal research tools
Document-based copilots
How the Architecture Works
- Document ingestion
- Chunking and embedding
- Weaviate vector storage
- Semantic retrieval
- LLM answer generation
- Application and access layer
Production Controls
- Private collections per bot/group
- Configurable retrieved chunk count
- Role-based permissions
- Model and prompt configuration
- Evaluation with representative questions
- Cost and token monitoring
How We Deliver
Use-case and data assessment
Prototype or proof of concept
Evaluation against representative questions/documents/data
Integration with applications, databases or workflows
Access controls, logging and cost controls
Production rollout, monitoring and iterative improvement
Relevant Proof
- MultiLLM AI Platform General LLM Bot knowledge base
- Legal platform integrations
- Company document and workflow scenarios
FAQ
Frequently asked questions
Do we need to choose one AI model?
No. Our MultiLLM AI Platform architecture supports multiple LLM providers, allowing model choice by use case. A project can also be designed with an abstraction layer so the application is not unnecessarily tied to one provider.
Can the AI use our private documents?
Yes. Our MultiLLM AI Platform supports private knowledge bases using document chunking, embeddings and Weaviate-based semantic retrieval.
Can AI query our existing database?
Yes. The DB Link Bot pattern sends the question and allowed table structure to the model, executes generated SQL against the database, and returns query results for summarisation rather than sending the entire dataset.
How do you reduce AI risk?
Use-case-specific controls can include role permissions, restricted data access, human review, token limits, validation, logging and evaluation with representative examples.
Build an AI Solution Around Your Workflow
Bring us a real workflow, dataset or document process. We can help identify where AI adds value, what should stay deterministic, and how to move from prototype to a governed production solution.