About Inferarc
An inference-first graph memory layer built for enterprises that need auditable, conflict-aware AI reasoning.
The Problem
Traditional AI memory relies on vector search — a flat retrieval mechanism prone to semantic drift and context blindness. When your sales team promises a feature that engineering deprecated three months ago, standard RAG retrieves both documents and gets confused. There is no mechanism to detect that these facts contradict each other, or to trace which one is temporally authoritative.
Our Approach
Inferarc transforms raw organizational data into a Temporal Knowledge Graph where every fact is anchored to its source, timestamp, and department. Instead of similarity-based retrieval, we use multi-hop graph traversal with semantic consistency checks.
Ingest
Extract entity-relationship triplets from documents, emails, and internal tools. Every fact gets a temporal anchor.
Reason
Multi-hop traversal traces dependencies across the graph. The system follows causal chains, not just keyword matches.
Detect
Semantic conflict detection flags contradictions automatically, identifying which source is temporally authoritative.
Enterprise Use Cases
Cross-Department Conflict Detection
Automatically flag when Sales promises contradict Engineering timelines or when procurement terms conflict with legal requirements.
Supply Chain Tracking
Monitor supplier lead times, delivery commitments, and SLA changes across multiple vendors with temporal audit trails.
Compliance & Audit Export
Export full entity timelines as CSV for regulatory audits. Every fact is anchored to its source document and timestamp.
Bulk Document Ingestion
Ingest hundreds of documents in a single batch. The system extracts entities, relationships, and detects conflicts at scale.
Multi-Vendor Management
Track relationships and commitments across multiple vendors. Detect when overlapping contracts introduce conflicting terms.
Knowledge Base Integrity
Maintain a single source of truth across your organization. Semantic conflict detection prevents stale or contradictory facts.
Technical Architecture
The system is built on three layers:
- 01Data Ingestion Layer — Entity resolution with automatic merging, episodic anchoring with mandatory event_time and ingestion_time, and pattern-based triplet extraction.
- 02Reasoning Layer — LangGraph-powered multi-step pipeline with entity extraction, graph traversal, conflict detection, and answer synthesis. Backed by Neo4j for production and in-memory fallback for development.
- 03Temporal Diff Engine — Tracks state changes over time for any entity, producing a full audit trail with conflict markers for regulatory compliance and decision tracing.