A retrieval architecture designed for enterprise context.
Anvik treats enterprise AI as a system design problem. The goal is not just to generate an answer, but to return the right answer with the right evidence and the right controls around it.
The system begins with document understanding: sections, tables, references, and page-level evidence links. This is the layer that prevents enterprise content from collapsing into generic chunks.
Entities, relationships, metadata, and document references are modeled so the system can answer questions about ownership, dependency, exception flow, and change impact.
Search can combine semantic retrieval, graph traversal, and rules depending on the question. The same foundation supports analysts, assistants, and workflows.
Every output can carry citations, trace paths, and operating controls such as access checks, logging, and evaluation signals.
How data moves through the system.
These are the core operating steps behind search, copilots, and graph-aware AI workflows.
Bring in PDFs, scans, tables, SOPs, policies, tickets, and operational documents without losing document structure.
Turn raw documents into sections, tables, entities, relationships, and metadata using controlled schemas.
Normalize aliases, merge duplicates, and connect references across the corpus into a durable context graph.
Store vectors, graph edges, metadata, and evidence references so search and agents operate over the same foundation.
Use the right retrieval mode for the task: semantic search, traversal, constrained evidence search, or agentic multi-step workflows.
Return answers, citations, trace paths, and operational signals that teams can review, trust, and act on.
The details that separate a demo from a durable platform.
Parse PDFs, scans, tables, images, and long documents while preserving hierarchy, sections, and evidence references.
Extract entities, relationships, and metadata into explicit schemas with validation, confidence, and review loops.
Unify aliases, merge duplicates, and maintain stable identifiers so knowledge graphs and assistants stay coherent over time.
Combine semantic retrieval, section-aware ranking, graph traversal, and rules to answer multi-step enterprise questions.
Support workflows that verify, plan, cross-check, summarize, escalate, and cite the exact evidence used in the answer.
Use the model stack that fits your latency, privacy, and cost constraints without redesigning the entire pipeline.
Role-based access, audit logs, retrieval evaluation, and change tracking make the system usable in real enterprise settings.