RAG systems that answer from evidence, not model memory.

A language model cannot answer accurately from evidence it never received. Production RAG begins with ingestion that understands pages, sections, tables, figures, metadata, and document relationships—not indiscriminate chunks of extracted text.

We combine semantic and exact retrieval so meaning-based questions work without sacrificing names, identifiers, clauses, or numbers. Ranking, context assembly, and citation generation are evaluated as separate stages so failure can be located and improved.

• Connectors and synchronization for the repositories that hold authoritative content
• Layout-aware parsing for long documents, tables, figures, and section hierarchy
• Hybrid semantic and keyword retrieval with query-aware ranking
• Permission filtering before evidence reaches the model
• Page- and passage-level citations that users can verify
• Evaluation sets for retrieval recall, answer support, refusal, and citation accuracy

The system should distinguish between an answer supported by the available corpus and one the evidence cannot establish. We design thresholds, validation, and response behavior so “the sources do not answer this” remains an acceptable outcome.

For regulated or high-stakes work, retrieved passages, answer claims, model activity, and user access can remain linked in the run record. Reviewers can inspect not only the final response but the evidence path that produced it.

We can improve an existing retrieval system or build the full path from ingestion to application. The same foundation can support enterprise search, grounded agents, document analysis, and knowledge graph extraction, which prevents every AI project from creating a separate copy of the company corpus.