Heabsy
The Engine

Graph-ReAct
Architecture

Traditional RAG retrieves paragraphs. ARGUS reasons across your entire knowledge base, connecting facts like a senior analyst would.

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Naive RAG vs ARGUS

A fundamental difference in how AI understands your documents

Naive RAG

Single-Query Vector Search

Query
Vector DB
Top-K Chunks
Answer

Linear retrieval — no reasoning, no connections

  • Retrieves isolated paragraphs
  • Misses cross-document connections
  • Cannot synthesize multiple sources
  • Fails on multi-hop questions
  • No audit trail for answers

ARGUS

Graph-ReAct Architecture

Query
Doc A
Fact 1
Doc B
Fact 2
Doc C
Fact 3
Reasoned Answer

Graph traversal with iterative reasoning

  • Builds knowledge graph in real-time
  • Connects facts across documents
  • Multi-step reasoning (ReAct loop)
  • Handles 5+ hop complex queries
  • Full evidence trail for audit
Traceability

The Evidence Ledger

Every answer ARGUS provides comes with a complete audit trail. See exactly which documents were consulted, which facts were extracted, and how they were synthesized into the final answer.

  • Source documents with page numbers
  • Extracted facts with confidence scores
  • Reasoning chain visualization
  • Export-ready for compliance audits

"For regulated industries, traceability isn't a feature — it's a requirement."

Evidence Ledger
Query
"What are the liability clauses in Contract A that conflict with Policy B?"
Step 1: Document Analysis
→ Identified 3 liability clauses in Contract_A.pdf (pp. 12, 15, 23)
→ Confidence: 0.94
Step 2: Cross-Reference
→ Compared with Policy_B.pdf sections 4.1, 4.3
→ Found 2 contradictions
Final Answer
Clause 5.2 in Contract A limits liability to €50,000, while Policy B Section 4.3 requires minimum €100,000 coverage...
Knowledge Graph

Built for Real Scale

ARGUS doesn't just store documents — it extracts semantic relationships and builds a queryable knowledge graph that connects facts across your entire corpus.

260K+
Semantic Triplets
Entities and relationships extracted via LLM
19K+
Documents Indexed
Cross-referenced and linked
46.4%
Graph Connectivity
Up from 1% via entity resolution
94%
Island Reduction
Isolated clusters merged into connected graph

How It Works

1
Triplet Extraction: LLM extracts Subject → Relation → Object from every document chunk
2
Entity Resolution: Embedding-based deduplication merges "CEO", "chief executive", and the person's name into one node
3
RRF Fusion: Vector similarity + graph traversal scores combined via Reciprocal Rank Fusion for hybrid retrieval
Zero-Training Deployment

Start in Days, Not Months

Unlike traditional ML solutions, ARGUS requires no training on your data. Connect your document sources and start querying immediately.

Day 1

Connect Sources

Integrate with your existing document stores, SharePoint, S3, or databases.

Day 2-3

Index & Configure

Automatic knowledge graph construction. Fine-tune relevance settings.

Day 4+

Production Ready

Start answering complex queries. Monitor and iterate on results.

Ready to See the Technical Deep-Dive?

Request our whitepaper for architecture diagrams, benchmark methodology, and integration specifications.

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