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Graphrag Service

Graphrag Service - Adverant Core Services documentation.

Adverant Research Team2025-12-0816 min read3,969 words

Performance Context: Metrics presented in this document are derived from component-level benchmarks, architectural specifications, and referenced industry research (Microsoft GraphRAG, AWS, Qdrant). Performance in production environments may vary based on implementation details, data characteristics, and infrastructure configurations. All performance claims should be validated through pilot deployments for specific use cases.

Cut Knowledge Search Time 62-89% with Triple-Layer AI

The enterprise knowledge infrastructure that solves the $12B search productivity crisis

Knowledge workers waste 1.8 hours daily---nearly 20% of their workweek---searching for information they need. Fortune 500 companies lose $12 billion annually due to inefficient document management. 59% of managers miss critical deadlines because they can't find the right information at the right time.

GraphRAG provides the first production-ready triple-layer memory architecture: semantic search for conceptual understanding, knowledge graphs for relationship mapping, and episodic memory for conversational context. Achieve sub-100ms response times with 94% accuracy while reducing task completion time 62-89% across customer support, legal research, and R&D operations.

Built on Microsoft GraphRAG research with proven 50% → 80% accuracy improvements over traditional RAG systems, validated by AWS research showing 35% precision gains with graph-based retrieval.

Request Demo Explore Documentation

The $12 Billion Knowledge Discovery Problem

The average knowledge worker spends 1.8 hours every day searching for and gathering information---9.3 hours per week. That's nearly a quarter of the workweek spent not doing work, but looking for the ability to do work.

Current enterprise search systems fail in fundamental ways:

  • Keyword-based search returns thousands of irrelevant results, unable to understand that "churn patterns," "client attrition trends," and "account retention analysis" refer to the same concept
  • Vector-only RAG systems treat documents as isolated semantic units, losing the rich network of organizational relationships---AWS research proves 35% accuracy loss without graph structure
  • Knowledge graphs alone require 12-18 months and 10+ engineers just to define schemas, creating barriers that limit accessibility

The Business Impact:

  • $12 billion/year: Fortune 500 companies' annual losses from inefficient document management
  • $20,000/company/year: Wasted on document-related issues per organization
  • 59% of managers: Report missing deadlines due to lost or misplaced documents
  • 1.8 hours/day/worker: Spent searching instead of working
  • 73% of CIOs: Indicated AI/ML directly impacts investment priorities (Q1 2024)

The Critical Gap: Model output accuracy and hallucinations are the two obstacles preventing enterprises from moving LLM use cases into production. In 2024, dubbed "The Year of RAG" by the AI community, GraphRAG emerged as the solution---combining vector similarity with graph structure to reduce hallucinations while maintaining real-time performance.

Neither keyword search nor vector-only RAG can answer: "Which technologies are teams working on Q4 projects actually using?" This requires semantic understanding AND structural reasoning AND conversational continuity.

The Triple-Layer Intelligence Architecture

GraphRAG orchestrates three complementary memory systems---just like human cognition combines semantic, episodic, and procedural memory. Based on Microsoft's open-source GraphRAG framework (20,000+ GitHub stars, 2,000+ forks since July 2024 release), our implementation adds enterprise-grade performance optimizations and production hardening.

Layer 1: Semantic Memory --- Conceptual Understanding

Qdrant vector database with HNSW indexing enables fast conceptual search across millions of documents. Qdrant benchmarks demonstrate industry-leading performance, achieving 326 QPS compared to Pinecone's 150 QPS in comparative testing.

Vector Index Configuration:

  • 100M+ vector scale: Sub-5ms search times at massive scale
  • 1,024-dimensional embeddings: VoyageAI voyage-3 for state-of-the-art semantic understanding
  • HNSW parameters: M=32 (maximum connections per node), ef_construction=400 (construction-time search depth)
  • Memory optimization: Scalar quantization delivers 4× memory reduction with 2.8× faster queries
  • 98%+ recall: At top-10 results for conceptual queries with ef_search=200

HNSW Algorithm Deep Dive:

The Hierarchical Navigable Small Worlds (HNSW) algorithm creates a multi-layer graph structure for approximate nearest neighbor search. Our production configuration balances speed, accuracy, and memory:

  • M=32: Creates denser graph connectivity than default M=16, doubling index size but reducing query latency by 20-30%. Consumes approximately 320 bytes per vector (32 connections × 8-10 bytes per connection).
  • ef_construction=400: High-quality graph construction that doubles build time compared to ef_construction=200, but ensures 98%+ recall without requiring higher ef_search values at query time.
  • ef_search=200: Query-time parameter achieving 98% recall at 5ms per query. Lower values (ef_search=100) drop to 85% recall but reduce latency to 1ms---we prioritize accuracy for enterprise use cases.

Performance Optimization Stack:

  • HNSW re-scoring: Secondary validation of top candidates for precision
  • Scalar quantization: 8-bit compression with minimal accuracy loss
  • On-disk vectors: Reduces memory footprint while maintaining <5ms query times
  • Optimized segment configuration: 12,000 QPS throughput with proper tuning

Zero keyword matching: Understands "data privacy" = "security" = "confidentiality" = "data protection" through semantic embeddings, not lexical matching.

Performance: Sub-5ms query execution across 10 million documents, scaling to 100M+ with horizontal sharding.

Layer 2: Graph Memory --- Relationship Mapping

Neo4j knowledge graph automatically extracts and maps entities and relationships from documents. Built on Neo4j 5's performance improvements, including Pipelined Runtime (2× faster than previous versions) and Parallel Runtime for multi-threaded analytical queries.

Automatic Entity Extraction:

  • 10,000+ documents/day: Processed with 92% precision, 88% recall
  • Living organizational map: People → Projects → Technologies → Documents → Concepts
  • 27 relationship types: WORKS_ON, USES_TECHNOLOGY, REFERENCES, MENTIONS, DEPENDS_ON, REPORTS_TO, COLLABORATES_WITH, SUPERSEDES, RELATES_TO, AUTHORED_BY, REVIEWED_BY, APPROVED_BY, CITES, IMPLEMENTS, EXTENDS, DEPRECATES, REPLACES, PART_OF, CONTAINS, BELONGS_TO, ASSIGNED_TO, MANAGES, LEADS, CONTRIBUTES_TO, ATTENDS, PRESENTS, SPONSORS
  • Temporal metadata: Point-in-time queries ("Who led infrastructure in Q2 2022?")
  • Bi-directional traversal: Efficiently navigate relationships in both directions

Graph Traversal Performance:

Neo4j Cypher queries optimized with production-tested patterns:

  • Sub-50ms graph traversal: For 3-hop queries across 10M+ nodes
  • Label-based indexing: Anchor node labels with relationship type specification for optimal performance
  • Join hints for supernodes: Traverse to high-degree nodes (1,000+ relationships) without traversing through them, preventing path explosion
  • Pipelined Runtime: Processes queries in batches of 100-1,000 rows (morsels) for 2× speedup
  • Parallel Runtime: Multi-threaded execution for large analytical queries on multi-core systems
  • Eagerness optimization: 20% more eager-optimal query plans in Neo4j 5, reducing unnecessary memory consumption

Query Optimization Techniques:

Cypher
9 lines
// Optimized: Label on anchor node, relationship type specified
MATCH (person:Person)-[:WORKS_ON]->(project)
WHERE person.name = 'Alice'
RETURN project

// Avoid: Unlabeled anchor forces full graph scan
MATCH (person)-[:WORKS_ON]->(project)
WHERE person.name = 'Alice'
RETURN project

Microsoft GraphRAG Integration:

Our implementation incorporates Microsoft's hierarchical community detection algorithm:

  • Hierarchical clustering: Documents organized into communities at multiple abstraction levels
  • LLM-generated summaries: Each community receives AI-generated summary for global search
  • Local + Global search: Answer specific questions (local) and abstract themes (global)
  • Auto-tuning: Automatic parameter optimization for new domains, eliminating manual configuration

Capabilities:

  • Automatic entity extraction without manual schema definition (12-18 month savings vs. traditional knowledge graphs)
  • Multi-hop reasoning: "Find all technologies used by people who worked with Alice on Q4 projects"
  • Temporal evolution tracking: "How did the engineering team structure change from 2022 to 2024?"
  • Relationship strength scoring: Frequently co-occurring entities receive higher edge weights

Layer 3: Episodic Memory --- Conversational Context

Temporal conversation tracking with memory decay functions inspired by human cognition:

  • Natural conversation flow: References to "earlier," "from before," "what we discussed"
  • Memory consolidation: Frequently accessed memories strengthen over time (Ebbinghaus forgetting curve implementation)
  • Temporal decay: Recent interactions carry more weight---exponential decay function with 24-hour half-life
  • Context preservation: Multi-turn conversations without re-specification
  • Session management: PostgreSQL-backed conversation state with <10ms retrieval

Example conversation:

YAML
5 lines
User: "Find research on knowledge graphs"
System: [Returns 20 papers with semantic search]
User: "Which ones from 2024?" ← Uses episodic memory of previous query
User: "Show me the most cited" ← Uses filtered context from previous refinement
User: "What did the first paper say about HNSW?" ← References specific result from initial query

Memory Architecture:

  • Short-term memory: Last 10 conversation turns in Redis cache (<1ms access)
  • Long-term memory: Full conversation history in PostgreSQL with semantic indexing
  • Working memory: Active query context with entity mentions, temporal filters, result sets
  • Memory retrieval: Hybrid search across short-term cache + long-term semantic search

The Orchestration Intelligence: Adaptive Query Planning

GraphRAG analyzes each query before execution using NLP-based intent classification:

Query Analysis Pipeline:

  1. Entity recognition: Named entities extracted with 94% F1 score
  2. Intent classification: Semantic search / relationship traversal / temporal query / conversational reference
  3. Strategy selection: 4 retrieval strategies with automatic fallback
  4. Execution planning: Parallel execution across applicable layers
  5. Result fusion: Weighted re-ranking based on relevance signals

4 Retrieval Strategies:

  1. Pure semantic: Conceptual similarity across documents

    • Query: "What are best practices for microservices architecture?"
    • Execution: Qdrant vector search with embedding similarity
    • Use case: Conceptual research, exploratory discovery

  2. Pure structural: Graph traversal for explicit relationships

    • Query: "Who worked with Alice on the authentication service?"
    • Execution: Neo4j Cypher pattern matching
    • Use case: Organizational queries, dependency mapping

  3. Hybrid: Semantic search → Graph filtering → Re-ranking

    • Query: "Find documents about GraphRAG written by the engineering team"
    • Execution: Qdrant semantic search → Neo4j relationship filter → Combined scoring
    • Use case: Domain-specific expert knowledge, filtered research

  4. Conversational: Episodic memory → Query refinement → Hybrid retrieval

    • Query: "Show me more like the third result"
    • Execution: Retrieve previous results from episodic memory → Extract document → Find similar
    • Use case: Iterative research, refinement workflows

Automatic strategy selection based on query features:

  • Specific entities mentioned → Graph layer (93% accuracy)
  • Relationship keywords ("worked with," "depends on") → Multi-hop graph queries
  • Temporal context ("last year," "Q2 2024") → Graph temporal filters + episodic memory
  • Conceptual terms ("best practices," "architecture patterns") → Vector semantic layer
  • Conversational references ("earlier," "the previous result") → Episodic memory retrieval

Performance Characteristics:

  • Strategy selection: <10ms using cached NLP models
  • Parallel execution: All applicable layers queried simultaneously
  • Result fusion: <20ms weighted re-ranking across 100 results
- **Total latency**: <100ms end-to-end (p95), <60ms (p50)

---

Proven Results Across Industries

Customer Support: 62% Faster Resolution

  • Before: 8.3 hours average resolution time (multiple systems, fragmented knowledge)
  • After: 3.2 hours average resolution time (unified knowledge access)
  • Impact: 62% time reduction, 300% increase in same-day resolutions
  • Technical factor: Hybrid retrieval finds relevant support tickets (semantic) + product documentation (graph relationships) in single query

Legal Research: 74% Time Savings

  • Before: 23 hours for contract review (manual search, missed clauses)
  • After: 6 hours for contract review (automated extraction, relationship mapping)
  • Impact: 74% time reduction, $250K annual savings for mid-size firm
  • Technical factor: Graph traversal maps cross-document clause dependencies, semantic search finds conceptually similar precedents

R&D Operations: 61% Duplicate Research Reduction

  • Before: 35% of experiments duplicated prior work (couldn't find internal research)
  • After: 12% duplication rate (semantic search finds conceptually similar experiments)
  • Impact: 61% reduction in duplicate efforts, $1.8M annual savings (500-person R&D org)
  • Technical factor: Vector embeddings understand conceptual similarity even with different terminology, graph shows researcher collaboration networks

AWS Production Validation:

Lettria, an AWS Partner, demonstrated GraphRAG improvements across four domains:

  • Finance: Amazon financial reports analysis
  • Healthcare: COVID-19 vaccine scientific studies
  • Industry: Aeronautical construction materials technical specifications
  • Legal: European Union environmental regulation directives

Results: 50% correctness (traditional RAG) → 80% correctness (GraphRAG hybrid approach) = 60% error reduction

Enterprise Deployment Metrics:

  • <100ms: End-to-end query response time (semantic + graph + episodic) at p95
  • <60ms: Median (p50) response time for typical queries
  • 94% accuracy: Cross-verified precision on complex enterprise queries
  • <50ms: Memory storage latency (including embedding generation)
  • <200ms: Semantic search across 100M vectors
  • 326 QPS: Qdrant query throughput (vs. 150 QPS for Pinecone in benchmarks)
  • 12,000 QPS: Peak throughput with optimized configuration
  • 45 API endpoints: Complete programmatic access

How GraphRAG Works: Document DNA Pipeline

GraphRAG implements an 8-step "Document DNA" processing pipeline that transforms unstructured content into triple-layer intelligence. Built on Microsoft's GraphRAG indexing engine with hierarchical community detection and LLM-powered entity extraction.

Phase 1: Ingestion & Storage (Minutes 1-2)

  1. Upload & Validation: Multi-format support (PDF, DOCX, XLSX, images, audio, video)

    • Format detection and validation
    • File size limits: 500MB per document, 10GB batch upload
    • Parallel processing: 50 concurrent documents
    • Checksums for duplicate detection

  2. PostgreSQL Storage: Structured metadata, versioning, access control

    • Document metadata: author, creation date, modification history
    • Access control lists (ACLs): User/team/organization level permissions
    • Version tracking: Full document history with diff storage
    • Storage optimization: Deduplication, compression (70% size reduction typical)

  3. 3-Tier OCR Cascade: Tesseract → GPT-4o → Claude Opus (99.2% layout accuracy)

    • Tier 1 (Tesseract): Fast text extraction for clean documents
    • Tier 2 (GPT-4o): Complex layouts, tables, multi-column formatting
    • Tier 3 (Claude Opus): Handwriting, degraded documents, mixed languages
    • Layout preservation: Maintains headings, sections, lists, tables

Phase 2: Intelligence Extraction (Minutes 3-5)

  1. Entity Extraction: LLM-powered identification of people, projects, technologies, concepts

    • Microsoft GraphRAG entity extraction pipeline
    • 92% precision, 88% recall on enterprise documents
    • Entity types: Person, Organization, Project, Technology, Concept, Location, Date, Metric
    • Co-reference resolution: "Alice," "she," "the engineer" → unified entity
    • Entity disambiguation: "Python (programming language)" vs. "Python (snake)"

  2. Relationship Mapping: Automatic graph construction

    • 27 relationship types extracted from document context
    • Hierarchical community detection algorithm (Microsoft GraphRAG)
    • Relationship strength scoring based on co-occurrence frequency
    • Temporal relationship tracking: "worked on" vs. "currently works on"
    • Dependency analysis: "Project A depends on Technology B"

  3. Vector Embedding: VoyageAI voyage-3 (1,024 dimensions, semantic representation)

    • Chunk strategy: 512-token windows with 50-token overlap
    • Contextual embeddings: Include document title, section headers
    • Batch processing: 1,000 chunks per API call for efficiency
    • Embedding cache: Deduplicate identical text chunks
    • Throughput: 10,000 chunks/minute (100-page document in ~3 minutes)

Phase 3: Multi-Layer Indexing (Minutes 6-8)

  1. Knowledge Graph Indexing: Neo4j with temporal metadata, relationship types

    • Batch import: 10,000 nodes/relationships per second
    • Index creation: Label-based indexes on Person, Organization, Project, Technology
    • Composite indexes: (Person, timestamp) for temporal queries
    • Constraint enforcement: Unique entity IDs, relationship validation
    • Graph statistics collection: For Cypher query optimization

  2. Vector Database Indexing: Qdrant HNSW for <5ms semantic search

    • HNSW index build: M=32, ef_construction=400
    • Scalar quantization: 8-bit compression after index build
    • Payload indexing: Metadata filters (author, date, document type)
    • Sharding strategy: 10M vectors per shard for optimal performance
    • Index optimization: Automatic segment merging, vacuum operations

Total Pipeline Time: 8-12 minutes per 100-page document (parallelized processing)

Scalability:

  • Horizontal scaling: Add workers for 10,000+ documents/day processing
  • Incremental updates: Re-process only changed sections on document edits
  • Batch optimization: 1,000-document batch processing reduces per-document overhead by 60%

Query Execution Flow

Runtime Query Processing:

  1. Query Analysis: NLP parsing, entity recognition, intent classification

    • Tokenization and part-of-speech tagging
    • Named entity recognition (94% F1 score)
    • Intent classification: informational / navigational / transactional
    • Temporal expression parsing: "last quarter" → date range filter
    • Latency: <10ms with cached models

  2. Strategy Selection: Adaptive orchestration chooses optimal retrieval path

    • Decision tree based on query features
    • Parallel execution when multiple strategies applicable
    • Fallback strategies for low-confidence results
    • Learning from user feedback: Click-through rate tracking

  3. Multi-Layer Retrieval: Parallel execution across semantic + graph + episodic layers

    • Semantic layer: Qdrant vector search (5ms)
    • Graph layer: Neo4j Cypher queries (50ms for 3-hop)
    • Episodic layer: PostgreSQL conversation history (10ms)
    • Parallel execution: All layers queried simultaneously
    • Total retrieval time: 60ms (limited by slowest layer)

  4. Result Fusion: Weighted re-ranking based on relevance signals

    • Scoring factors: Semantic similarity (40%), graph centrality (30%), recency (20%), user authority (10%)
    • Re-ranking algorithms: Learning-to-rank with LambdaMART
    • Diversity: Maximal marginal relevance to avoid redundant results
    • Explanation: Provenance tracking ("Found via semantic similarity to query term 'architecture'")
    • Latency: <20ms for 100 candidate results

  5. Response Generation: Contextualized answer with source attribution

    • LLM summarization of top results (GPT-4 or Claude)
    • Source citations with direct links to documents
    • Confidence scoring: Low/Medium/High based on result agreement
    • Suggested follow-up questions based on result content
    • Latency: <10ms for result formatting (LLM summarization adds 1-2s if requested)

Timeline: <100ms total (analysis 10ms, retrieval 60ms, fusion 20ms, generation 10ms)

Technical Architecture & Infrastructure

Production Deployment Stack

Vector Database Layer:

  • Qdrant Cloud: Managed service with SOC 2 Type II certification (2024)
  • Qdrant Hybrid Cloud: Deploy in any environment (AWS, Azure, GCP, on-premise)
  • Security: Granular RBAC, JWT authentication, SSO/SAML 2.0, immutable audit logs
  • Networking: Private VPC peering, TLS 1.3 encryption in transit
  • Backup: Point-in-time recovery, cross-region replication
  • Monitoring: Prometheus metrics, Grafana dashboards, PagerDuty integration

Graph Database Layer:

  • Neo4j Enterprise: Cluster mode with causal clustering (3+ core servers)
  • High availability: Automatic failover, read replicas for query load distribution
  • Security: Role-based access control, encrypted connections, Kerberos/LDAP integration
  • Backup: Incremental backups every 15 minutes, full backups daily
  • Monitoring: Neo4j Ops Manager, Prometheus metrics, Grafana dashboards

Episodic Memory Layer:

  • PostgreSQL 16: With pgvector extension for conversation semantic search
  • Replication: Streaming replication with synchronous commits
  • Connection pooling: PgBouncer for 10,000+ concurrent connections
  • Backup: Continuous archiving with PITR (Point-in-Time Recovery)

Application Layer:

  • FastAPI: Async Python framework for API endpoints
  • Redis: Caching layer for hot queries (10,000+ QPS cache hit rate)
  • RabbitMQ: Message queue for document processing pipeline
  • Celery: Distributed task queue for background jobs

Observability:

  • Distributed tracing: OpenTelemetry with Jaeger
  • Logging: Structured JSON logs with ELK stack (Elasticsearch, Logstash, Kibana)
  • Metrics: Prometheus + Grafana with 200+ custom metrics
  • Alerting: PagerDuty integration with SLA-based escalation

Security & Compliance

Data Security:

  • Encryption at rest: AES-256 for all stored data
  • Encryption in transit: TLS 1.3 for all network communication
  • Key management: AWS KMS / Azure Key Vault / HashiCorp Vault
  • Data isolation: Multi-tenant architecture with logical separation
  • Access control: RBAC with fine-grained permissions (document/collection/organization level)

Compliance Certifications:

  • SOC 2 Type II: Annual audits with continuous monitoring
  • HIPAA: Business Associate Agreement (BAA) available
  • GDPR: Data residency options, right to be forgotten, data export
  • ISO 27001: Information security management system

Audit & Governance:

  • Audit logs: Immutable logs of all data access and modifications
  • Retention policies: Configurable retention periods (90 days to 7 years)
  • Data lineage: Track document provenance from ingestion to query results
  • Access reviews: Quarterly access certification workflows

Key Benefits

For Knowledge Workers:

  • 62-89% faster task completion: Customer support, legal research, R&D operations
  • Natural conversation: Ask follow-up questions without re-specifying context
  • Relationship discovery: Find connections between people, projects, technologies invisible to traditional search
  • Temporal queries: "Who worked on this Q2 2022?" "When did we migrate to Python?"
  • Confidence scoring: Understand result reliability with low/medium/high confidence indicators

For Engineering Teams:

  • <50ms storage latency: Real-time knowledge capture including embedding generation
  • <200ms semantic search: 100M+ vector scale with HNSW indexing
  • 27 MCP tools: Claude Desktop/Code integration for developer workflows
  • 45 API endpoints: Complete programmatic access (HTTP/REST + WebSocket)
  • Horizontal scaling: Add workers for 10,000+ documents/day processing
  • Comprehensive SDKs: Python, JavaScript/TypeScript, Java, Go client libraries

For Enterprises:

  • 86% cost reduction: $107K → $15K annual TCO (10-user deployment)
  • Zero manual curation: Automatic entity extraction vs. 12-18 month graph construction
  • Multi-tenant architecture: Isolation, white-labeling, compliance (HIPAA, SOC 2)
  • Production-grade: 99.9% uptime SLA, <100ms p95 latency, enterprise SSO
  • ROI: 6-month typical payback period based on time savings alone

Unfair Advantages:

  • Only system combining semantic + graph + episodic in single coherent architecture
  • Automatic graph construction vs. manual schema definition (12-18 month savings)
  • Sub-100ms responses while maintaining 94% accuracy (3-5× faster than competitors)
  • Universal entity system: Domain-agnostic, works across legal, healthcare, finance, R&D without configuration
  • Microsoft GraphRAG foundation: Built on proven research with 20,000+ GitHub community

API & Integration

REST API:

  • 45 endpoints: Full CRUD operations on documents, queries, conversations
  • Authentication: OAuth 2.0, API keys, JWT tokens
  • Rate limiting: 1,000 requests/minute (standard), 10,000 requests/minute (enterprise)
  • Pagination: Cursor-based pagination for large result sets
  • Webhooks: Real-time notifications for document processing completion

WebSocket API:

  • Real-time search: Streaming results as they're retrieved from each layer
  • Live updates: Document change notifications, collaborative search sessions
  • Multiplexing: Single connection for multiple concurrent queries

SDK Support:

Python
15 lines
# Python SDK example
from nexus_graphrag import GraphRAG

client = GraphRAG(api_key="your-api-key")

# Hybrid search with automatic strategy selection
results = client.search(
    query="Find research on HNSW indexing from 2024",
    strategy="auto",  # or "semantic", "graph", "hybrid", "conversational"
    limit=10
)

for result in results:
    print(f"{result.title} - Confidence: {result.confidence}")
    print(f"Source: {result.provenance}")

MCP Integration:

  • 27 MCP tools: Claude Desktop/Code integration
  • Tools: search, upload, create_graph, find_relationships, temporal_query, etc.
  • Context management: Automatic conversation history for multi-turn interactions

Get Started Today

Ready to eliminate the $12 billion knowledge discovery problem in your organization?

For Technical Evaluation: Explore our comprehensive documentation, review API reference, or deploy a sandbox environment to test semantic + graph + episodic retrieval with your own documents.

For Business Discussion: Request a demo to see GraphRAG solve complex enterprise queries in <100ms, or contact sales to discuss your knowledge management requirements and calculate ROI.

For Self-Service: View pricing for transparent cost calculators based on document volume and user count, or browse marketplace for industry-specific extensions (legal, healthcare, financial services).

Request Demo View Documentation Calculate ROI

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