A governed knowledge layer for enterprise RAG.
Anvik is built for the hard cases: dense PDFs, tables, fragmented naming, and questions that require relationships. The result is an evidence pipeline you can deploy, evaluate, and defend.
- Multi-hop reasoning (A → B → C)
- Dependency + impact tracing
- Cross-document linking
- Citations to source sections
- Audit trails
- Confidence & gap reporting
- On-prem / VPC
- Private model options
- Data segregation + RBAC
Designed end-to-end — not just retrieval.
Most platforms start at embeddings. We start at document understanding and governance — then build retrieval and agentic workflows on top.
PDFs, scans, tables, annexures. We preserve section hierarchy, references, and page-level citations.
Entity + relation extraction with schemas, validation, confidence, and reprocessing — designed for production observability.
Normalize aliases, merge duplicates, and keep identifiers consistent across corpora so traversal stays reliable.
Hybrid storage for both semantic similarity and deterministic relationship queries; metadata stays first-class.
Multiple retrieval modes: semantic search, graph traversal, constrained retrieval, and evidence pack generation.
RBAC, audit logs, dataset evaluation, change tracking, and safe-by-design agentic workflows.
On-prem, VPC, or managed.
We align with enterprise security: data residency, network controls, private model options, and auditability.
- Corpus onboarding + ingestion report
- Extraction schema definition + coverage
- Graph + vector index with governance
- Retrieval evaluation and failure analysis
- Roadmap to production hardening