Retrieval-augmented generation systems that ground AI outputs in your documents and databases - accurate answers, with citations back to source.
Retrieval-augmented generation (RAG) is what turns a general-purpose language model into one that actually knows your business - your documentation, policies, product data, and institutional knowledge.
Advatech designs and builds RAG systems from ingestion through retrieval to generation, tuned for accuracy against your specific document types and query patterns - not a generic off-the-shelf pipeline.
Pipelines that process and chunk your documents - PDFs, wikis, databases - for reliable retrieval.
Vector indexing tuned for your content types and query patterns, not a default configuration.
Hybrid search and re-ranking tuned to surface the actually relevant passages, not just similar ones.
Generated answers linked back to their source documents, so users can verify accuracy.
Pipelines that keep the knowledge base current as source documents change, without full re-indexing.
Retrieval and generation quality measured against real queries, not assumed.
Document sources and formats catalogued, and quality gaps identified.
Ingestion, chunking, and embedding pipeline built for your specific content.
Search and re-ranking tuned against real queries for retrieval accuracy.
Accuracy measured before rollout, with monitoring for ongoing quality.
RAG retrieves relevant information at query time rather than baking it into model weights - it's faster to update, cheaper to maintain, and keeps answers traceable to a source.
PDFs, Word documents, wikis, databases, and most structured or semi-structured content - the ingestion pipeline is built around your actual sources.
Retrieval and end-to-end answer quality are tested against a curated set of real queries with known correct answers, before and after any tuning changes.