Standard vector-based RAG fails on global questions ("what are the top themes across our entire dataset?") and complex multi-hop relationships ("how is Company A connected to Person B through three subsidiaries?"). Knowledge graphs bridge this gap by mapping entities and relationships explicitly. Codersarts builds production GraphRAG architectures using Neo4j and Microsoft GraphRAG. We extract entities, build hierarchical community summaries, generate Cypher query pipelines, and fuse graph traversal with vector search — delivering superior enterprise reasoning running in your cloud.
Vector-based RAG has a fundamental limitation: it treats information as isolated paragraphs of text. While vector similarity excels at finding specific passages (e.g., "what is the return policy?"), it fails completely when answering global questions across an entire document corpus ("what are the key strategic risks mentioned across all 500 reports?") or navigating complex multi-hop relationships ("which suppliers share board members with our direct competitors?"). Vector search cannot connect dots across documents because it lacks an understanding of entities and relationships.
GraphRAG combines the semantic power of Large Language Models with the structural rigor of knowledge graphs. Using Neo4j and Microsoft GraphRAG, Codersarts transforms your unstructured documents into an interconnected property graph. We extract entities and relationships, partition the graph into thematic communities using clustering algorithms, pre-generate hierarchical community summaries, and enable hybrid graph-vector queries. The result is an AI system that reasons across your entire knowledge base with verifiable multi-hop accuracy.
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Is This You?
You are pushing the limits of AI retrieval, but vector databases cannot solve your complex relationship queries:
"Our vector RAG system fails completely when asked holistic questions like 'what are the main themes across our entire 5,000-document archive?'"
"When questions require connecting dots across three different contracts or corporate subsidiaries, vector search retrieves isolated paragraphs and misses the relationships."
"We want to use Microsoft GraphRAG, but the indexing pipeline is complex, slow, and burns hundreds of dollars in LLM tokens if unoptimized."
"We have a Neo4j database, but our LLM generates invalid Cypher queries or hallucinates node relationships."
"We don't know how to automatically extract clean entities (nodes) and relationships (edges) from messy unstructured text without generating massive noise and duplicates."
"Our leadership wants deep relationship mapping and visual graph exploration alongside conversational AI search."
Relying solely on vector search leaves critical connections buried in your data, leading to shallow AI answers and blind spots in due diligence, fraud detection, and research. Organizations that deploy GraphRAG uncover hidden relationships and empower their AI to synthesize global enterprise intelligence.
Why Knowledge Graphs and GraphRAG Outperform Vector-Only Search
GraphRAG replaces fragmented vector chunks with an explicit, queryable knowledge network:
Explicit Entity & Relationship Extraction: Rather than indexing arbitrary text slices, an LLM-driven extraction pipeline identifies real-world entities (people, companies, products, regulatory statutes) and directed relationships (ACQUIRED_BY, SUPPLIES_TO, REGULATED_UNDER). These are modeled into a property graph in Neo4j.
Hierarchical Community Summarization (Microsoft GraphRAG): Microsoft GraphRAG uses community detection algorithms (like the Leiden algorithm) to group related entities into hierarchical clusters. It pre-generates summaries for each community at multiple levels of abstraction, allowing the LLM to answer macro-level questions across the entire dataset ("Global Search") that vector search cannot answer.
Multi-Hop Traversal vs. Semantic Proximity: Standard vector search only finds chunks with similar wording. A knowledge graph traverses 3–5 degrees of separation across interconnected nodes, uncovering hidden relationships between entities that never appear in the same document.
Hybrid Vector + Graph Fusion: We combine the strengths of both paradigms. Vector embeddings provide fuzzy, semantic entry points into the graph, while graph traversals and Cypher queries retrieve precise, structured subgraphs for the LLM to synthesize.
For your business, this means:
Holistic Corpus Understanding: Answer broad, high-level questions across thousands of documents in seconds.
Flawless Multi-Hop Reasoning: Accurately trace complex relationship webs across subsidiaries, supply chains, and legal agreements.
Auditability & Explainability: Every insight is grounded in verifiable graph nodes, edges, and source document citations.
Zero Fragmented Context: Eliminate hallucinations caused by information being severed across chunk boundaries.
Our Knowledge Graph & GraphRAG Services
Codersarts provides complete lifecycle engineering for property graphs and GraphRAG architectures.
Custom GraphRAG Architecture & Knowledge Graph Design
We design and implement bespoke GraphRAG systems tailored to your domain. We establish ontology schemas, define entity types and relationship properties, and architect the integration between graph storage and language models.
Automated Entity & Relationship Extraction Pipelines
We build high-accuracy extraction pipelines that parse unstructured documents (PDFs, filings, emails, wikis). We implement entity resolution, deduplication, and schema validation to ensure your graph remains clean and free of duplicate nodes.
Microsoft GraphRAG Implementation & Indexing Optimization
We deploy and tune Microsoft GraphRAG. We configure Leiden community clustering, hierarchical community summary generation, and dual-mode retrieval (Global Search for high-level summaries and Local Search for specific entity reasoning). We implement prompt caching and batching to reduce indexing token costs by up to 60%.
Neo4j Property Graph Modeling & Cypher Engineering
We architect enterprise property graphs in Neo4j (Self-hosted or AuraDB). We build Text-to-Cypher pipelines (using LangChain GraphCypherQAChain or LlamaIndex PropertyGraph), implementing schema guardrails and query validation to prevent syntax errors and runaway database queries.
Hybrid Graph + Vector Search Integration
We integrate Neo4j's native vector search with graph traversal. User queries identify candidate entry nodes via vector similarity, and graph traversals expand the neighborhood to gather complete context before passing it to the LLM.
Document-to-Graph ETL Pipelines
We build automated ETL data pipelines that ingest data from PDFs, relational databases, S3 buckets, and enterprise APIs, continually extracting and merging new entities and relationships into your production graph.
Interactive Graph Visualization (Neo4j Bloom & Web UIs)
We configure interactive exploration dashboards using Neo4j Bloom or custom web-based graph visualizers (D3.js / Cytoscape.js). Stakeholders can visually explore node connections, expand paths, and verify relationships alongside conversational chat.
Graph Maintenance, Pruning & Drift Management
Over time, knowledge graphs can accumulate obsolete entities or conflicting relationships. We implement graph maintenance workers that prune stale edges, resolve entity conflicts, and maintain graph hygiene.
Common Use Cases
M&A Due Diligence & Corporate Ownership: Mapping complex webs of holding companies, subsidiaries, beneficial owners, and shared directors across thousands of regulatory filings.
Pharmaceutical & Biomedical Research: Connecting genes, proteins, diseases, drug compounds, and clinical trial results to accelerate scientific discovery and identify drug repurposing opportunities.
Fraud Detection & Anti-Money Laundering (AML): Uncovering circular financial transactions, shared addresses, and hidden relationships between sanctioned entities and corporate accounts.
Supply Chain & Vulnerability Analysis: Mapping multi-tier supplier dependencies, geographic choke points, and component vulnerabilities across manufacturing networks.
Legal Precedent & Contract Intelligence: Tracing cross-references, judicial citations, and contractual obligations across Master Service Agreements and court dockets.
Cybersecurity & Threat Intelligence: Mapping attack vectors, software dependencies (SBOMs), CVE vulnerabilities, and compromised network infrastructure into unified graph topologies.
What You Get
When you hire our knowledge graph development service, you receive a production-grade GraphRAG asset:
Production-ready source code written in Python, delivered directly to your GitHub/GitLab repository with 100% intellectual property ownership.
Configured graph database (Neo4j Enterprise or Neo4j AuraDB) with optimized schema, indices, and constraints.
Automated entity and relationship extraction pipeline with built-in entity resolution and deduplication.
Microsoft GraphRAG implementation featuring hierarchical Leiden community clustering and pre-generated summaries.
Text-to-Cypher query engine equipped with schema validators and syntax error-recovery guards.
Hybrid graph-vector retrieval API (FastAPI) exposing endpoints for Global Search, Local Search, and multi-hop queries.
Interactive graph visualization setup (Neo4j Bloom or custom web visualizer) for visual exploration.
Automated test suite validating entity extraction fidelity, Cypher query generation, and answer accuracy.
Technical documentation and runbook detailing ontology definitions, indexing parameters, and maintenance procedures.
A 30-minute handover call walking your engineering team through the codebase, plus post-delivery support.
What We Need From You
To begin developing your GraphRAG knowledge graph, we will ask for:
Sample documents: A representative batch of the files you want to analyze (PDFs, filings, contracts, text archives).
Target entities & relationships: The core concepts of interest (e.g., Organizations, Executives, Products, Regulations, Contracts).
Expected query types: Whether your priority is global corpus summaries, multi-hop relationship discovery, or specific entity lookups.
Hosting and infrastructure preferences: Cloud provider (AWS, Azure, GCP), preference for self-hosted Neo4j vs. Neo4j AuraDB, or Microsoft GraphRAG.
One designated technical lead: A software engineer, data architect, or domain expert available for scoping discussions and review milestones.
If you don't have a formal ontology schema defined, we will help you design one during our initial scoping call.
How Our Knowledge Graph Development Service Works
Free Scoping Call & Ontology Review (30 minutes): We inspect your document corpus, identify key entities and relationships, review query requirements, and choose between Neo4j and Microsoft GraphRAG.
GraphRAG Architecture Blueprint & Fixed Quote (within 1 business day): You receive a written technical specification covering ontology design, extraction strategies, indexing cost estimates, and a fixed price.
Ontology Design & Extraction Pipeline Build: We define the property graph schema, write LLM entity extraction prompts, build entity resolution routines, and run initial graph construction.
Community Summarization & Retrieval Integration: We run community detection algorithms, pre-generate hierarchical summaries, build Text-to-Cypher query handlers, and tune hybrid vector-graph fusion.
Deployment, Visualization & Handover: We deploy the database and API endpoints to your cloud, configure Neo4j Bloom dashboards, deliver full documentation, and train your team.
Book Your Free Ontology Review →
Security, Privacy & Data Governance
Enterprise knowledge graphs contain sensitive relationship intelligence:
100% Private Cloud Deployment: Neo4j and Microsoft GraphRAG can be deployed entirely within your private AWS VPC, Azure VNet, or Google Cloud VPC. Zero graph data leaves your infrastructure.
Zero Model Training Guarantees: We configure enterprise API agreements ensuring your proprietary entities, relationships, and source texts are never stored or used to train public foundation models.
Sub-Graph Role-Based Access Control (RBAC): We implement node- and relationship-level security policies in Neo4j, ensuring sensitive relationships (e.g., executive compensation or confidential investigations) are only visible to authorized roles.
Encrypted Storage: All graph databases, vector indices, and intermediate extraction caches are encrypted at rest using customer-managed KMS keys.
Mutual Non-Disclosure Agreement: We sign a comprehensive mutual NDA before examining your files, schemas, or proprietary datasets.
How Much Does a Knowledge Graph Development Service Cost?
Pricing depends on document volume, ontology complexity, entity extraction token costs, and whether you require Microsoft GraphRAG community summaries or custom Neo4j Cypher pipelines. Every project receives a fixed quote before kickoff.
GraphRAG Pilot
Best for: Validating knowledge graph reasoning on up to 1,000 documents
Includes: Custom ontology design, automated entity extraction, Neo4j setup, Text-to-Cypher query generator, and staging deployment
Production Microsoft GraphRAG System
Best for: High-depth multi-hop and global reasoning across up to 20,000 documents
Includes: Full Microsoft GraphRAG pipeline, Leiden community summaries, Global & Local Search, hybrid vector fusion, Bloom visualization, and full test suite
Enterprise High-Scale Graph Platform
Best for: Massive multi-corpus archives (100,000+ files) or mission-critical investigations
Includes: Multi-node Neo4j cluster, continuous real-time graph updates, custom ontology fine-tuning, interactive web visualizer, and production SLA
What affects the cost: Document corpus scale and visual complexity, entity extraction LLM token costs, real-time graph update requirements, and custom interactive visualization frontend development.
Engagement Models
Fixed-Price Project: Best for a clearly defined knowledge graph build or GraphRAG deployment. You receive a guaranteed price, defined deliverables, and a committed timeline.
Dedicated Knowledge Graph Engineer: Best for organizations continuously expanding complex graph data models. A senior Codersarts graph engineer embeds into your team on a sprint basis. See Hire ML Engineers.
Graph Maintenance & Ontology Retainer: Best after launch. We monitor entity deduplication, update community summaries as new documents are added, and optimize Cypher queries over time. See our Production Support plans.
Tech Stack
Graph Databases: Neo4j (Enterprise, AuraDB), Amazon Neptune, and Memgraph.
GraphRAG Frameworks: Microsoft GraphRAG, LangChain (GraphCypherQAChain), and LlamaIndex (PropertyGraphIndex).
Graph Algorithms: Leiden Community Detection, PageRank, Betweenness Centrality, Node2Vec, and NetworkX.
Query Languages: Cypher, openCypher, and GraphQL.
Language Models: OpenAI (GPT-4o, text-embedding-3-large), Anthropic Claude (3.5 Sonnet, 3.5 Haiku), and local open-source models via vLLM.
Infrastructure & Visualization: Docker, FastAPI, Python, Neo4j Bloom, and Cytoscape.js.
Why Choose Codersarts for Knowledge Graph Development
Pioneering GraphRAG Specialists: We specialize in modern GraphRAG architectures (Neo4j and Microsoft GraphRAG), moving beyond standard vector limitations.
Deep Ontology Engineering: We build structured property graphs with clean entity resolution and relationship verification, avoiding messy, noisy graphs.
Token-Optimized Indexing: We implement prompt caching and intelligent batching to slash GraphRAG extraction and community summarization costs by up to 60%.
100% Code & Data Ownership: You own all source code, ontology schemas, and graph databases with zero recurring software licensing fees to us.
Deployed in Your Infrastructure: We build directly in your GitHub repositories and private cloud accounts.
Transparent Fixed Pricing: You get clear deliverables and binding fixed quotes upfront, with no unexpected consulting hours.
Where This Fits in Your AI Project
Knowledge graphs represent the connected intelligence and multi-hop reasoning layer of your AI architecture. Related Codersarts services:
Build a RAG Chatbot on Your Documents: Pair your GraphRAG knowledge base with an end-user chat interface with verifiable citations.
Fix Poor RAG Answer Accuracy: Resolve multi-hop and holistic corpus questions that vector search alone cannot answer.
Convert Messy PDFs into AI-Ready Data: Parse complex documents upstream before running entity and relationship extraction.
Add Hybrid Search & Reranking: Fuse graph traversals with hybrid keyword-vector search for maximum retrieval precision.
Build a Custom AI Agent for Your Business: Equip autonomous reasoning agents with GraphRAG tools for complex investigative workflows.
Set Up LLM Observability & Tracing: Instrument Cypher query generation, community summary lookups, and token costs in production.
Planning a full RAG system? Explore Codersarts RAG Development Services.
Frequently Asked Questions
How much does a knowledge graph development service cost?
Cost depends on your document volume, ontology complexity, entity extraction token requirements, and whether you need Microsoft GraphRAG community summaries or custom Neo4j Cypher pipelines. Following a free 30-minute scoping call, we provide a transparent, fixed-price quote within one business day.
How long does a GraphRAG implementation project take?
A focused GraphRAG pilot covering up to 1,000 documents typically takes 1 to 2 weeks. Comprehensive enterprise systems featuring Microsoft GraphRAG community clustering, Neo4j integration, and custom visualizations take 2 to 4 weeks. We confirm exact delivery dates in your quote.
What is GraphRAG and how does it differ from traditional vector RAG?
Traditional vector RAG slices documents into isolated text chunks and searches based on semantic similarity. GraphRAG extracts entities (nodes) and relationships (edges) from documents to build a connected knowledge graph. It enables two capabilities vector search cannot provide: (1) multi-hop relationship reasoning across documents, and (2) global corpus-level summarization via hierarchical community clusters.
What are Global Search and Local Search in Microsoft GraphRAG?
Microsoft GraphRAG offers two search modes: Global Search answers high-level, holistic questions ("what are the key themes across all documents?") by aggregating pre-generated community summaries across the graph. Local Search answers specific, entity-centric questions ("what is the relationship between Company X and Person Y?") by exploring immediate node neighborhoods, connected entities, and related raw text chunks.
How does Microsoft GraphRAG use community detection (the Leiden algorithm)?
The Leiden algorithm partitions the knowledge graph into densely connected clusters or "communities" of related entities. Microsoft GraphRAG then prompts an LLM to generate comprehensive summaries for each community at multiple hierarchical levels. This allows the system to synthesize answers from top-level macro themes down to granular local details.
Why is entity extraction and resolution so critical in knowledge graphs?
Without entity resolution, the system might treat "IBM", "International Business Machines", and "IBM Corp" as three distinct nodes, fracturing the graph and breaking multi-hop reasoning. We implement fuzzy matching, entity linking, and LLM-assisted deduplication to ensure clean, unified entities across all documents.
How do LLMs interact with Neo4j (Text-to-Cypher)?
Using Text-to-Cypher frameworks (such as LangChain GraphCypherQAChain), the user's natural language question is translated into a structured Cypher query by the LLM based on the graph's schema. The query executes against Neo4j, retrieves the precise subgraph, and feeds the structured data back to the LLM to generate a verified, natural language response.
How do you keep Microsoft GraphRAG indexing token costs manageable?
Full GraphRAG indexing can be token-intensive because it requires multiple LLM calls to extract entities, relationships, and community summaries. We optimize costs by implementing Anthropic prompt caching, using smaller evaluator models (GPT-4o-mini or Claude 3.5 Haiku) for initial extraction passes, and caching intermediate outputs to avoid redundant processing.
Can GraphRAG run completely inside our private cloud or VPC?
Yes. Neo4j can be hosted inside your private AWS VPC, Azure VNet, or Google Cloud VPC. For extraction and summarization, we can deploy open-source LLMs (like Llama 3 via vLLM) on private GPU instances, ensuring that no document data or entity relationships ever leave your network.
Who owns the ontology, graph database, and source code?
You do. All source code, ontology schemas, extraction scripts, and Neo4j database configurations belong 100% to you. We build directly in your repository and cloud accounts.
Want to build it yourself?
Read our technical guide: Microsoft GraphRAG tutorial and building knowledge graphs from documents on the Codersarts Discuss forum.
Turn Your Documents into an Intelligent Knowledge Graph This Week
Stop settling for shallow vector search and unlock deep multi-hop reasoning across your entire document library. Tell us about your data and investigative use case, and our GraphRAG engineers will deliver an architecture plan and fixed quote within one business day.