AI & Data
Vector Search and Semantic Retrieval for Private Business Knowledge
Vector search helps applications retrieve content based on meaning. MultiLLM AI Platform uses Weaviate to store document embeddings and retrieve semantically relevant chunks before the LLM generates an answer.
What We Build
Document semantic search
RAG retrieval
Knowledge-base assistants
Similarity matching
Hybrid search patterns
Metadata-aware retrieval
How the Architecture Works
- Chunking strategy
- Embedding model
- Vector database (Weaviate)
- Metadata
- Similarity retrieval
- LLM or application result layer
Production Controls
- Collection isolation
- Metadata filtering
- Top-k/chunk configuration
- Evaluation against real questions
- Cost-aware embedding choices
- Monitoring relevance over time
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 knowledge base architecture uses Weaviate for semantic search and RAG
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.