AI & Data
Semantic Search Across Documents and Business Knowledge
Enterprise search should help users find meaning, not just keywords. We combine structured search, vector retrieval and conversational interfaces so teams can search private content, databases and selected business records in a way that matches their permissions.
What We Build
Semantic document search
Legal information retrieval
Internal knowledge search
Cross-document discovery
Search over structured and unstructured data
Conversational result exploration
How the Architecture Works
- Search interface
- Embedding model
- Vector database (Weaviate)
- Metadata and permissions
- LLM response layer where needed
- Database/API integrations
Production Controls
- Permission-aware access
- Isolated data collections
- Metadata filters
- Result relevance evaluation
- Logging and monitoring
- Fallback to structured search where appropriate
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 Weaviate-based knowledge search
- D-Law legal data search and natural-language querying
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.