From managed cloud graph databases to AI-powered agents and advanced data science — our certified Neo4J practice team helps enterprises model, query, and derive intelligence from complex, highly-connected datasets at scale.
AI-native graph intelligence — a managed agent platform that combines LLMs with Neo4J's knowledge graph to deliver accurate, context-rich, reasoning over your enterprise data.
Neo4J Aura Agent is Neo4J's managed AI agent infrastructure, purpose-built to ground LLM reasoning in structured graph knowledge. By connecting large language models to a live Neo4J knowledge graph, Aura Agent enables AI applications to retrieve precise, relationship-aware context — dramatically reducing hallucinations and improving the accuracy of generative AI outputs. Our practice helps enterprises design, build, and operate Aura Agent deployments that connect their proprietary graph data to AI workflows, enabling intelligent search, question-answering, and autonomous agent pipelines at production scale.
Designing enterprise knowledge graphs optimised for LLM retrieval — modelling entities, relationships, and properties so that graph traversals yield contextually rich, accurate answers for AI agents.
Building Graph Retrieval-Augmented Generation (GraphRAG) pipelines that query Neo4J for structured context before passing results to LLMs — delivering grounded, fact-checked AI responses at scale.
Designing multi-step AI agent workflows using LangChain, LlamaIndex, or custom orchestration frameworks — integrating Neo4J Aura Agent as the knowledge retrieval layer for complex reasoning tasks.
Implementing entity extraction, deduplication, and automated graph enrichment pipelines that ingest unstructured text and structured data sources into a continuously updated knowledge graph.
Configuring Aura Agent with enterprise security controls — VPC peering, private endpoints, role-based access, audit logging, and data residency compliance for regulated industry deployments.
Profiling and tuning Aura Agent query patterns, index strategies, and retrieval logic — ensuring sub-second graph lookups that keep AI agent response times within acceptable latency budgets.
Fully managed graph database as a service — zero-ops Neo4J delivered on cloud infrastructure with automatic scaling, built-in backups, and enterprise-grade availability.
Neo4J Aura DB is Neo4J's fully managed cloud database service, available on Google Cloud, AWS, and Azure. It eliminates the operational overhead of running Neo4J on-premises or on self-managed VMs — handling provisioning, patching, backups, scaling, and high availability automatically. Aura DB is available in Free, Professional, and Enterprise tiers, with AuraDB Enterprise offering VPC isolation, private endpoints, and advanced SLAs. Our practice helps organisations design their Aura DB architecture, migrate from self-managed Neo4J or relational databases, and integrate Aura DB into their application and data platform landscapes.
Selecting the right Aura DB tier, instance size, and cloud region — based on graph size, query concurrency, latency requirements, and data residency constraints — and designing the overall topology.
Planning and executing migrations from self-managed Neo4J, RDBMS (PostgreSQL, Oracle, SQL Server), or other graph databases to Aura DB — including data modelling transformation and zero-downtime cutover strategies.
Integrating Aura DB into existing application stacks using Neo4J Bolt drivers (Java, Python, JavaScript, Go, .NET) — with connection pool tuning, retry logic, and transaction management best practices.
Configuring Aura DB Enterprise with VPC peering, private endpoints, IP allowlisting, and role-based database access — ensuring data never traverses the public internet in regulated environments.
Analysing slow Cypher queries, designing optimal index strategies (range, text, point, vector), and refactoring query patterns to maximise Aura DB performance for production workloads.
Building data ingestion pipelines into Aura DB using Neo4J Data Importer, APOC, Kafka Connect, Spark connector, or custom ETL — enabling continuous graph population from operational data sources.
The world's leading native graph database — purpose-built to store, query, and traverse billions of relationships with millisecond performance that relational databases cannot match.
Neo4J's native graph database stores data as nodes, relationships, and properties — exactly reflecting how real-world data is connected. Unlike relational databases that simulate relationships through joins, Neo4J traverses connections natively, making it orders of magnitude faster for highly-connected queries: fraud detection, recommendation engines, supply chain analysis, identity graphs, and network topology. Available as Neo4J Enterprise Edition (self-managed) or via Neo4J Aura, it supports Cypher query language, full ACID transactions, cluster deployments, and a rich ecosystem of integrations. Our practice covers everything from initial graph data modelling through production deployment, clustering, and ongoing operations.
Designing optimal property graph models for your domain — translating relational schemas, document structures, or conceptual domain models into efficient Neo4J node and relationship patterns with appropriate properties and labels.
Writing, profiling, and optimising Cypher queries for complex traversal patterns — using EXPLAIN/PROFILE plans, index-backed lookups, and query rewriting to achieve consistent sub-100ms response times.
Designing and deploying Neo4J Causal Clusters on Kubernetes, bare metal, or cloud VMs — with primary/secondary topology, read replica configuration, disaster recovery, and multi-data-centre replication.
Implementing production graph use cases — fraud detection networks, recommendation engines, identity resolution, knowledge graphs, supply chain graphs, and IT/network topology mapping — end-to-end.
Migrating from relational databases (Oracle, PostgreSQL, SQL Server) to Neo4J — including schema-to-graph transformation, ETL pipeline development, data validation, and application query layer refactoring.
Configuring Neo4J Enterprise security — role-based access control, database-level privileges, sub-graph privilege restrictions, encryption at rest and in transit, LDAP integration, and audit logging.
Real-time analytical graph workloads at scale — enabling business intelligence teams to run complex graph analytics over live or historical connected data without impacting operational performance.
Neo4J Graph Data Analytics extends the Neo4J platform for large-scale analytical workloads — enabling organisations to run graph algorithms, pattern detection, and aggregation queries over billions of nodes and relationships. It supports analytical use cases such as influence analysis, community detection, centrality scoring, path analytics, and temporal graph queries. By separating analytical from transactional workloads, it ensures OLAP-style graph queries do not impact the performance of production databases. Our practice helps data and analytics teams integrate Neo4J graph analytics into their broader data platform, building pipelines that surface graph-derived insights into BI tools and dashboards.
Designing the analytical graph architecture — separating OLTP and OLAP workloads, configuring read replicas or dedicated analytical instances, and defining the data freshness strategy for analytics pipelines.
Implementing community detection (Louvain, Label Propagation), centrality algorithms (PageRank, Betweenness, Closeness), and influence scoring to identify key nodes, clusters, and structural patterns in your graph.
Building shortest path, all-paths, and weighted path analytics for use cases such as supply chain routing, network topology analysis, dependency chain impact assessment, and fraud ring detection.
Connecting Neo4J analytical results to business intelligence tools — exporting graph metrics and algorithm outputs to Tableau, Power BI, Looker, and custom dashboards via JDBC/ODBC or REST endpoints.
Analysing how graph structure evolves over time — detecting relationship pattern changes, temporal centrality shifts, and event-driven topology alterations for fraud, operations, and risk use cases.
Building automated analytics pipelines using Apache Airflow, dbt, or Spark — scheduling graph algorithm runs, persisting results back to Neo4J or data warehouses, and triggering downstream business processes.
The leading graph machine learning library — 65+ production-ready graph algorithms and ML pipelines that add structural intelligence to your data science and AI workflows.
Neo4J Graph Data Science (GDS) is a plugin library that brings the power of graph algorithms and machine learning directly into Neo4J — enabling data scientists to compute graph features, train link prediction and node classification models, and generate graph embeddings that capture structural patterns invisible to tabular ML. GDS integrates with Python data science toolchains (pandas, scikit-learn, PyTorch, PyTorch Geometric) via the GDS Python client, and supports in-database ML pipeline execution. Our practice embeds GDS into enterprise data science workflows — from exploratory graph analysis through to production ML model training, embedding generation, and real-time inference pipelines.
Implementing GDS algorithms tailored to your use case — node similarity, link prediction, community detection, centrality, and pathfinding — configured with appropriate projection strategies and parameter tuning for production accuracy.
Generating graph embeddings using GDS methods (FastRP, GraphSAGE, Node2Vec, HashGNN) to create structural feature vectors — enriching downstream ML models with relationship-aware representations of graph entities.
Building GDS ML pipelines for link prediction (predicting future relationships) and node classification (categorising nodes by structural and property features) — trained in-database and served via the GDS model catalogue.
Integrating Neo4J GDS with Python ML workflows using the GDS Python client — projecting subgraphs into GDS, running algorithms, exporting results to pandas/NumPy, and feeding embeddings into scikit-learn or PyTorch models.
Designing graph-based fraud detection systems using GDS — combining structural anomaly scores, community outliers, velocity pattern detection, and supervised node classification to surface high-risk entities in real time.
Building graph-powered recommendation engines using GDS similarity algorithms (KNN, Jaccard, Cosine) and collaborative filtering patterns — delivering personalised, relationship-aware recommendations with explainability.
Our engineers hold Neo4J certifications and have delivered graph database programmes across financial services, healthcare, logistics, and enterprise IT domains.
From data modelling and Cypher optimisation through to graph ML and AI agent integration — we cover the full Neo4J stack so you never need multiple vendors.
We integrate Neo4J seamlessly into your existing data platform — connecting it with data warehouses, streaming pipelines, BI tools, and AI/ML frameworks your teams already use.
Proven graph patterns, reusable Cypher libraries, and deployment accelerators mean faster time-to-insight — with lower project risk and predictable delivery milestones.