Retrieval that actually finds the right answer. Hybrid search, reranking, and agentic pipelines that search again when the first result is not good enough.
Start a projectBasic RAG treats retrieval as a single shot. Query goes in, top chunks come back, model does its best. The problem is the retrieval, not the model. Hybrid search fixes this by combining vector similarity with keyword matching, then reranking the results with a cross-encoder to surface documents that actually answer the question instead of just sharing vocabulary.
Agentic RAG goes further. Instead of accepting whatever the first search returns, the system checks whether the results are good enough. If they are not, it rewrites the query and searches again. It keeps going until it finds a real answer or exhausts its strategies. Retrieval that self-corrects instead of silently returning bad results.
Vector search plus BM25 keyword matching. Cross-encoder reranking on top. Chunking tuned per document type because a legal contract and a product FAQ need different treatment.
The system checks its own results. If the first search is not good enough, it reformulates the query and tries a different strategy. Keeps going until the answer passes a quality check.
Automated pipelines for documents, web pages, databases, and APIs. Format-aware chunking so a PDF table does not get split in half. Incremental updates as new content arrives.
Generic embedding models miss domain vocabulary. We select and fine-tune embeddings for your specific domain so legal terms, medical codes, and technical jargon get represented correctly.
Every answer traces back to the source document with page numbers and highlighted passages. Users can verify. Hallucination scoring flags answers without strong source support.
Precision, recall, and relevance measured continuously. Detect when retrieval quality drops because your knowledge base grew or documents went stale.
Map your data sources, document types, and the questions people actually ask. Build test cases of real question-answer pairs.
Set up the vector store, chunking pipeline, and embeddings. Ingest your documents and run retrieval benchmarks.
Tune chunking size, reranking, and retrieval parameters. Add agentic retrieval for complex queries. Test on every change.
Production deployment with query logging, accuracy monitoring, and automated updates when new documents arrive.
Deployed retrieval system with hybrid search, reranking, and citation tracking. Running in your infrastructure.
Your documents indexed and embedded. Automated pipeline for updates as new content arrives.
Test question-answer pairs with expected sources. Precision and recall baselines.
Retrieval accuracy, query latency, and coverage monitored in real time. Alerts on degradation.
Clean API for querying the knowledge system. Authentication, rate limiting, and error handling documented.