Every organization has valuable internal knowledge trapped in documents, wikis, emails, and databases. AI can unlock this knowledge, but many companies hesitate because they do not want sensitive information flowing to external AI services.
The Promise and Problem of Internal AI
Imagine an AI assistant that knows your company's policies, products, and procedures. Employees could ask questions and get accurate answers instantly. Customer service could access relevant information without searching through documentation. New hires could onboard faster.
The problem: achieving this with public AI APIs means sending your internal documents to external servers. For many organizations, that is a non-starter. Trade secrets, personnel information, strategic plans, and customer data should not leave your environment.
RAG: The Key Pattern
Retrieval Augmented Generation (RAG) is the architecture pattern that makes private AI knowledge systems work. Instead of training an AI model on your data (expensive and complex), RAG retrieves relevant documents and includes them as context for the AI.
When a user asks a question, the system searches your document repository for relevant content, then passes that content along with the question to an LLM. The LLM generates an answer based on the retrieved context. Your documents inform the response without being used for model training.
Keeping Data Internal
For true data privacy, both the retrieval system and the LLM should run within your environment.
Vector Database
Documents are converted to embeddings (numerical representations) and stored in a vector database. When queries arrive, the system finds documents with similar embeddings. Options like Pinecone offer cloud hosting, but for privacy, self-hosted alternatives like Milvus, Weaviate, or pgvector work well.
Private LLM
The language model that generates responses should run internally. Open-source models like Llama 3, Mistral, and others perform well for RAG applications. Since RAG provides relevant context, you do not need the largest models; focused retrieval compensates for smaller model size.
Document Processing Pipeline
Internal documents need processing before RAG can use them. This includes extraction (pulling text from PDFs, Word docs, etc.), chunking (splitting documents into searchable segments), and embedding (converting text to vectors). This entire pipeline runs internally.
Implementation Considerations
- Start small: pilot with a specific document set and user group before expanding
- Chunk wisely: document chunking strategy affects retrieval quality significantly
- Test retrieval: poor retrieval leads to poor answers regardless of LLM quality
- Maintain freshness: documents change; your RAG system needs update mechanisms
- Add metadata: document dates, sources, and categories improve retrieval and user trust
Security Controls
Internal AI tools need the same security rigor as any system handling sensitive data.
- Authentication: Users must be authenticated before accessing AI systems
- Authorization: Not all users should access all documents; preserve existing access controls
- Logging: Record queries and responses for security monitoring and audit
- Data classification: Some documents may be too sensitive even for internal AI
We help organizations design and implement private RAG systems that unlock internal knowledge while maintaining data privacy. Contact us to discuss your internal AI use cases.