Retrieval-Augmented Generation for accurate, source-cited AI responses.
Optimised vector store architecture using Pinecone, Weaviate, or Qdrant for lightning-fast semantic search at scale.
Automated ingestion pipelines that chunk, embed, and index documents from PDFs, databases, wikis, and APIs.
Every AI response includes verifiable source citations with page numbers, links, and confidence scores.
Combine semantic vector search with keyword-based BM25 retrieval and metadata filtering for maximum relevance.
Document-level permissions ensuring users only access information they are authorised to see.
Incremental indexing that keeps your knowledge base current as documents are added, modified, or archived.
Answers grounded in your own documents, with sources cited, rather than answers generated from a model's training data. That makes the output checkable — a reader can follow the citation — and keeps the system current as your content changes, without retraining anything.
Usually retrieval, not generation. If the right passage never reaches the model, no amount of prompt work recovers it. Chunking strategy, embeddings, reranking and evaluation are where the quality actually comes from, which is why we treat them as the substance of the build rather than as configuration.
With an evaluation set built during the project and maintained afterwards. Without one, changes to chunking, embeddings or prompts are guesses. With one, a model upgrade or a retrieval change is verified in an afternoon instead of discovered by a user.
It will surface the problem quickly, which is generally the useful outcome. Retrieval quality is bounded by content quality, so projects often expose that source material is out of date, contradictory or spread across systems. That is worth knowing before it is designed around.