Graph‑native RAG platform

Long‑form answers your experts can trust.

AIAM Graph RAG weaves together pages, tables, and entities across your PDFs to answer complex questions with context – not copy‑paste snippets.

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Neo4j graph backend Multi‑turn agentic chat PDF & scanned document support
Designed for deep questions
Multi‑document research copilot
Agent‑powered

Our retrieval agent iteratively expands its search across your graph, summarizes relevant pages, and composes long‑form answers grounded in your documents – complete with page‑level citations.

  • Hybrid vector + full‑text search over pages, chunks, entities, questions, and tables.
  • Coverage‑aware agent that knows when to expand the search or stop.
  • Table‑aware RAG that can reason over complex financials and reports.
Legal & compliance
Financial research
Insurance & underwriting
Healthcare & life sciences
Vertical playbooks

Where AIAM Graph RAG shines today.

Deep, cross‑document questions where answers live across long PDFs, tables, and scanned attachments.

Graph‑native retrieval across documents, pages, tables, and entities.
Legal
Contracts

In‑house contract copilot

Compare clauses across counterparties, surface obligations, and answer policy questions from your entire contract stack in one place.

  • Clause‑level comparisons across multiple agreements.
  • Key obligations with page‑level citations.
  • Multi‑turn agent that stays on the same deal.
Financial services
Research

Cross‑filing research assistant

Ask questions that span prospectuses, investor letters, and regulatory guidance – and get long‑form answers grounded in numbers and tables.

  • Table‑aware analysis across multiple reports.
  • Change‑over‑time questions across filings.
  • Coverage‑aware agent for broad topics.
Insurance
Coverage

Coverage & claims knowledge hub

Turn policy wordings, endorsements, and claim files into an interactive knowledge layer your adjusters can actually query.

  • Policy comparisons across years and carriers.
  • Scanned PDF support via Textract.
  • Job tracking for large ingestion runs.
Under the hood

Why graph‑native RAG matters.

  • Documents are ingested into Neo4j as a rich graph of directories, documents, pages, chunks, entities, questions, and tables.
  • Hybrid vector and full‑text indexes power high‑recall search over text, embeddings, and table structure.
  • An agent orchestrator plans search expansions, collects page‑level notes, and stops when coverage targets are met.

The result: fewer hallucinations, more complete answers, and clear traceability back to the exact pages and tables used.

How it works

From messy PDFs to answer‑ready graphs.

  1. Upload PDFs – including scanned images. Files flow through our Textract pipeline, turning scanned pages into structured text and tables.
  2. We build a rich knowledge graph. Pages, chunks, entities, questions, and tables are linked in Neo4j with vector and full‑text indexes.
  3. The agent orchestrates retrieval. It plans targeted searches, takes page‑level notes, and composes long‑form answers with citations.
Try it with your documents