Graph Analytics

Graph Strategy & Use-Case Discovery

work with business and technical stakeholders to identify where graph thinking delivers the highest ROI, including:

• Relationship-heavy data problems

• Complex dependency analysis

• AI explainability and reasoning gaps

• Fraud, risk, and entity resolution challenges

Deliverables

• Graph feasibility assessment

• Use-case prioritization matrix

• Reference architecture recommendations

• POC roadmap (30-60-90 day plan)

Graph-RAG (Retrieval-Augmented Generation) Systems

Design and implement Graph-powered RAG architectures that significantly outperform vector-only RAG systems.

What Graph-RAG Enables

• Multi-hop reasoning across entities

• Explainable AI responses

• Reduced hallucinations

• Context-aware answers

• Fact grounding with relationships

Architecture Components

• Knowledge Graph + Vector Store

• LLM orchestration layer

• Hybrid retrieval (graph traversal + semantic search)

• Prompt engineering for graph-aware reasoning

Use Cases

• Enterprise knowledge assistants

• Compliance & regulatory Q&A

• Financial investigation copilots

• Engineering dependency analysis bots

• Customer support intelligence

AI + Graph Integration

specialize in bridging Graph Data with AI/ML systems.

Capabilities

• Feature extraction from graphs for ML models

• Graph embeddings

• GNN (Graph Neural Network) readiness

• Explainable AI with graph context

• LLM grounding with knowledge graphs

Graph Data Modeling & Knowledge Graph Design

design scalable, domain-driven graph models that accurately represent real-world relationships.

Capabilities

• Property graph & RDF modeling

• Ontology & schema design

• Entity resolution & identity graphs

• Temporal and hierarchical relationships

• Data lineage & provenance graphs

Industries Supported

• Financial services & payments

• Healthcare & life sciences

• Supply chain & logistics

• Telecom & cybersecurity

• Retail & customer 360

Graph Databases & Platform Engineering

End-to-end implementation of enterprise-grade graph platforms.

Technologies

• Graph databases (e.g., Neo4j, Amazon Neptune, TigerGraph)

• Cloud-native deployments (AWS / Azure)

• High-availability & performance tuning

• Data ingestion pipelines (batch + streaming)

• Integration with relational, NoSQL, and event systems

Deliverables

• Production-ready graph infrastructure

• Data ingestion & synchronization pipelines

• Query optimization & performance benchmarks

• Security & access control design

Graph Analytics & Algorithms

enable advanced analytics that traditional SQL and BI tools cannot handle.

Graph Analytics

• Centrality, community detection, pathfinding

• Similarity & recommendation engines

• Fraud ring detection

• Dependency & impact analysis

• Network risk propagation

Outcome
Actionable intelligence from how things are connected, not just what they are.