Hybrid retrieval combining dense vector search, lexical search, and reranking. A multi-tier memory model retains what matters across turns and runs with document-level access control and source attribution.
Pure vector search misses literal terms such as account numbers, regulation IDs, and product SKUs. Pure lexical search misses semantic intent. Nexoraa runs both, then reranks the union with an LLM-based or cross-encoder reranker.
The result is higher precision on enterprise content where exact identifiers and conceptual meaning both matter.
Working memory
In-execution context. Holds intermediate values, prior steps, and current sub-task state. Cleared at workflow completion.
Episodic memory
Retains historical runs and outcomes. Used by agents to reproduce decisions, avoid repeated errors, and learn from prior outcomes under retention policy.
Entity memory
Structured business data such as customers, accounts, contracts, claims, and vendors. Queryable through governed tools, not free-form access.
- Document classification
- Public, internal, confidential, and restricted classifications are applied at ingestion.
- Document-level access control
- Specific documents are restricted by role and enforced at retrieval, not after.
- Secure RAG authentication
- Retrieval requires user authentication; queries are scoped to the requesting user's role.
- Source attribution
- Every retrieved chunk returns its source document, page, and chunk identifier, surfaced in the agent's output.
- Stale data detection
- Sources beyond defined refresh thresholds trigger alerts and may block retrieval.
- Right to be forgotten
- Configurable retention with automated purge of embeddings and source artifacts.
- Multi-format support
- PDF, DOCX, XLSX, PPTX, HTML, Markdown, and scanned images with OCR.
- Preprocessing
- Cleaning, deduplication, language detection, and metadata extraction before embedding.
- Chunking strategy
- Configurable size, overlap, and strategy by document type with A/B testing.
- Incremental re-indexing
- Detect changed, new, and deleted documents; update embeddings incrementally.
- Embedding versioning
- Track which embedding model was used and manage migration when models are upgraded.
- Automated ingestion
- Scheduled or event-driven ingestion from SharePoint, S3, file shares, and databases.