Knowledge preparation
Connect approved repositories, preserve useful structure, and track source versions so updates and deletions can reach the index.
Intelligence in practice
Enterprise Search & RAG
Build business knowledge assistants with enterprise search and RAG. Connect service manuals, procedures, and portfolio-company information with access controls.
Discuss your AI project ↗RAG: find evidence before composing an answer
Prepare the knowledge
- 01Approved sourcesDocuments and records
- 02IndexStructure, versions, permissions
Answer a question
- 01Question + identityWhat this user may see
- 02Retrieve evidenceSelect relevant passages
Compose with selected evidenceThe model receives the question and permitted context
Evidence supports an answerRespond with source references
Evidence is insufficientSay so or request clarification
Search quality and answer quality are different.
Retrieval-augmented generation, or RAG, supplies selected information to a model when a question is asked. Blake Technologies builds the ingestion, indexing, retrieval, and user experience around that process. The design must handle permissions, document changes, conflicting sources, and questions that the available material cannot answer. Search quality and answer quality are evaluated separately.
A service business can use a knowledge assistant to help employees find equipment guidance, operating procedures, or approved service information. A private equity firm may need similar capabilities across portfolio companies, with access that respects company boundaries. Our RAG development work connects retrieval to the employee’s task and makes supporting sources available for review.
Maintain the connection to the source
Relevant retrieval
Evaluate search, embeddings, and ranking against realistic questions rather than assuming every retrieved passage is useful.
Source-aware answers
Show supporting references, enforce access boundaries, and provide a clear response when evidence is insufficient.
A useful distinction
Does RAG replace training a custom model?
It addresses a different need: supplying relevant information at request time. Fine-tuning changes model behavior and may be useful for selected tasks. We evaluate retrieval first when the main problem is access to changing business knowledge.
What the engagement leaves behind
Your engagement outputs
- A source ingestion and indexing pipeline
- A search or question-answering interface with references
- Retrieval evaluations and source maintenance procedures
Scope, schedule, team, and support are agreed for the engagement.