Graph Analytics Services & Solutions

SG Analytics (SGA)’s graph analytics services harness the power of knowledge graphs to drive smarter business decisions. By leveraging interconnected data, we unlock advanced use cases such as hyper-personalization, fraud detection, and Generative AI (GenAI)-powered reasoning, enabling businesses to gain deeper insights and enhance operational efficiency.

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Industries We Serve

BFSI (Banking, Financial Services, and Insurance)

Capital Markets

TMT (Telecom, Media & Entertainment, & Technology)

Other Industries

Unlock the Power of Networks with

Advanced Graph Analytics

Graph analytics treats data as an interconnected set of nodes and edges, uncovering complex relationships that traditional methods miss. Graph analytics helps determine entity closeness, connections, and patterns using specialized algorithms for insights and predictions. Unlike relational databases, which focus on individual data points, graph analytics excels in areas like supply chain management, fraud detection, IT management, and social networks. It also relates to network and link analysis for connected data insights.

Benefits of Graph Data Analytics

Our graph analytics solutions help enterprises tackle a wide variety of use cases with interconnected data. When compared to traditional analytics using structured databases, graph analytics helps us:

Uncover Hidden Relationships

Graph analytics reveals complex connections between entities that traditional relational databases fail to detect, making it ideal for fraud detection, social networks, and supply chains.

Optimize Supply Chain & Logistics

Graph analytics helps identify bottlenecks, improve routing, and enhance risk management in supply chains by analyzing relationships between suppliers, warehouses, and customers.

Improve Personalization & Recommendations

By analyzing user behavior and relationships, graph-based recommendation engines provide highly accurate and contextual suggestions, enhancing customer engagement.

Detect Fraud & Anomalies Faster

Fraud detection models benefit from graph analytics by identifying hidden connections between fraudulent actors, enabling proactive risk mitigation.

Enhance AI & LLMs

Graph-powered AI improves reasoning, fact-checking, and contextual understanding, enabling more accurate and explainable AI-driven insights.

Improve Decision-Making with Knowledge Graphs

Enterprise knowledge graphs enhance search, discovery, and automated reasoning, helping organizations make informed, data-driven decisions.

Graph Data Analytics Services

Fraud Detection & Risk Management

Identify hidden connections between fraudulent entities.
Detect suspicious transaction patterns in banking and finance.
Combat money laundering through graph-based anomaly detection.

Personalized Recommendations

Enhance recommendation engines for e-commerce and streaming platforms.
Improve user engagement with AI-driven content suggestions.
Analyze customer behavior to deliver hyper-personalized experiences.

Supply Chain & Logistics Optimization

Map supplier relationships and optimize inventory flows.
Detect weak points and risks in supply chains.
Improve delivery route efficiency using graph-based routing algorithms.

Knowledge Graphs & Enterprise AI

Build and maintain enterprise knowledge graphs for better decision-making.
Enable GenAI-powered reasoning for automated insights.
Improve search and discovery within unstructured data.

Social Network & Influencer Analysis

Identify key influencers and communities in social networks.
Detect fake accounts and misinformation networks.
Understand sentiment and trends across digital platforms.

Graph-Powered LLMs & AI Reasoning

Enhance large language models (LLMs) with knowledge graphs to provide more factual and contextual responses.
Enable multi-hop reasoning for complex question-answering and decision- making.
Improve LLM accuracy by grounding responses in structured, connected data.
Optimize enterprise chatbots and assistants with graph-based contextual memory.
Enable explainability in AI decisions through transparent graph-based reasoning.

Graph Analytics Techniques

To deliver our graph analytics solutions, we leverage a variety of techniques that help extract insights from complex, interconnected data. Here are some key techniques:

1. Graph-Based Machine Learning

2. Knowledge Graph Construction & Reasoning

3. Graph Algorithms for Network Analysis

4. Graph Embeddings for LLMs & AI

5. Temporal & Dynamic Graph Analysis

Graph Neural Networks (GNNs)

Learn patterns from graph-structured data, which are useful for fraud detection, recommendation systems, and knowledge graphs.

Graph Convolutional Networks (GCNs)

Integrate deep learning techniques to handle graphs, helping in predictive analytics and entity classification.

Graph Attention Networks (GATs)

Assign different weights to node relationships, improving recommendations and NLP applications.

Entity Linking & Relationship Extraction

Identify and connect entities from unstructured data (e.g., documents, web pages).

Ontology-Based Reasoning

Use predefined relationships to infer new knowledge and enhance AI-driven insights.

Multi-Hop Reasoning

Navigate multiple relationships in a graph to answer complex queries (e.g., Gen AI-powered search).

PageRank & Centrality Measures

Identify key influencers, high-risk fraud nodes, or important supply chain hubs.

Community Detection

Discover clusters in social networks, fraud rings, and business ecosystems.

Shortest Path & Connectivity Analysis

Optimize logistics, delivery networks, and knowledge retrieval.

Node Embeddings (e.g., Deep Walk, Node2Vec)

Convert graph data into vector representations for machine learning (ML) models.

Graph-Augmented Retrieval (RAG or Retrieval-Augmented Generation)

Improve LLM responses by retrieving knowledge from structured graphs.

Graph-Based Prompt Engineering

Dynamically modify LLM prompts using graph relationships for better contextual understanding.

Evolving Graphs

Track changes in network structures over time (e.g., social networks, fraud patterns).

Graph Change Detection

Identify anomalies in supply chains, financial transactions, and recommendation systems.

Influence Propagation

Model how trends, information, or risks spread across a network.

Why SGA for Graph Database Solutions?

We specialize in cutting-edge graph databases, graph neural networks (GNNs), and AI-driven graph analytics, ensuring powerful and scalable solutions.

Expertise in Advanced Graph Technologies

Our data scientists and engineers can work across multiple graph databases such as Neo4j, dgraph, Cosmos DB, etc., and libraries such as Network X to build graph solutions.

Tailored Industry-Specific Solutions

From fraud detection to supply chain optimization and personalized recommendations, we design graph analytics solutions customized to your business needs.

Seamless Integration with Existing Systems

Our solutions work with enterprise data ecosystems, integrating smoothly with databases, LLMs, and cloud infrastructures for minimal disruption.

Graph-Powered AI & LLM Enhancement

We leverage knowledge graphs to improve LLM accuracy, contextual search, and AI-driven decision-making, ensuring smarter insights.

Proven Success & Business Impact

Our graph analytics expertise has helped businesses uncover hidden patterns, reduce risks, and optimize operations, delivering measurable ROI.

End-to-End Support & Scalability

From strategy and implementation to ongoing optimization, we offer comprehensive support to ensure your graph analytics infrastructure grows with your business.

Our Ins(AI)ghts

Whitepaper

Leveraging AI and Machine Learning for Data Quality Management

As organizations embrace digital transformation, the volume and value of data are growing exponentially. At SG Analytics, we help enterprises elevate their data quality management (DQM) practices by leveraging artificial intelligence (AI) and machine learning (ML). Our solutions automate data cleansing, improve accuracy, and ensure consistency—enabling faster, more reliable decision-making. By embedding intelligence into DQM, businesses can reduce risk, optimize operations, and unlock the full potential of their data assets for sustained growth and competitive advantage.

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Top Data Science Trends and Predictions in 2025

The world of data never stands still; new technologies are constantly transforming at a much faster and more accurate pace. With new trends emerging, they are bringing along new thinking and innovation.

Businesses are transitioning to a data-driven business model, where decisions are made based on data-backed insights