The trust problem in AI
Integrating artificial intelligence into a B2B product can feel like a double-edged sword. On one hand, LLMs can automate manual document processing and answer queries in seconds. On the other hand, unstructured model outputs can hallucinate facts, violate data privacy regulations, or leak sensitive client documents.
To ship AI features that corporate legal teams will sign off on, you must build safety, auditability, and verification directly into your engineering pipeline.
Grounding AI with RAG (Retrieval-Augmented Generation)
An LLM should not answer queries based purely on its public training weights. Instead, use a Retrieval-Augmented Generation (RAG) architecture.
RAG limits the AI's response space to a specific, verified set of reference documents:
- Embedding: When a document is uploaded, it is split into chunks and converted into vector embeddings.
- Indexing: Vector representations are stored in a secure vector database (like Pinecone).
- Retrieval: When a user inputs a query, we retrieve the top semantic match chunks from the database.
- Generation: We send the retrieved text as context to the LLM, instructing it to only answer using the provided facts and cite its sources.
This reduces hallucinations to virtually zero and provides a transparent audit trail.
Data privacy and enterprise constraints
When building AI integrations for the UK market:
- Data Sovereignty: Use enterprise-grade APIs where data is hosted locally (e.g. UK cloud instances) and is never used to train the base model.
- Access Controls: Ensure your retrieval step honors user permission levels. If a user does not have permission to view a document in the CRM, the vector search must exclude that document chunk.
- Rate Limiting: Implement strict tokens-per-minute rate limits to protect your API budget from runaway queries.