Unleash Your Data's Potential,

Node by Node,

for a Graph-Powered Advantage

With Peruri Graph Analytics, we convert your data into a source of strength and a competitive edge for your business. Your data isn't just information; it's the key to your success.

About Us

Experience the difference with our exceptional product

Choosing the right database is essential in data processing. Graph Database enables efficient handling of complex relationships between data, unlike the rigid structure of RDBMS. With enhanced analytical capabilities, it offers the speed and scalability necessary for managing interconnected data. Our product transforms your data interaction, making it more intuitive and responsive. Explore further to discover how Graph Database can elevate your data processing experience.

See Details

Our Features

Graph Data Science

Our powerful graph data science tool helps you make better predictions using your existing data.

Easy Integration

Effortlessly integrate our product into your workflow and boost your productivity.

Graph Database & Analytics

Uncover hidden insights from your data with our innovative graph analysis tools.

Interactive Data Visualization

Our intuitive data visualization tools bring your data to life, making it easy to understand and share.

Eliminate Joins, Simplify Querying

Easily transform and analyze your data with our simple query language, requiring much less code than SQL.

Our Use Case

Graph Analytics is a technology that enables the analysis and understanding of relationships and patterns within complex data. Unlike traditional data analysis, which focuses on individual entities, Graph Analytics emphasizes the relationships between those entities. This makes it particularly useful for identifying patterns, networks, and trends that are not visible through more linear data analysis methods.

Fraud Detection

Fraud is a financial drain, a risk for businesses and consumers alike. With fraud attempts skyrocketing, how can you identify fraud in time to stop it? Graph-based approaches to detecting fraud analyze complex linkages between people, transactions, and institutions.

Learn More

Recommendation Engine

Today’s retailers face a number of complex and emerging challenges. To remain viable, they must be nimble enough to face their colossal online competition while also addressing another new reality: The customer is now at the center of the value chain.

Learn More

Use Cases (Sector)

  • Fraud Detection:
    Graph Analytics helps financial institutions detect fraudulent activities by analyzing transaction patterns and identifying suspicious relationships between accounts. This enables real-time monitoring and quick response to potential fraud.
  • Risk Management:
    By mapping out relationships between various financial entities, Graph Analytics allows organizations to assess and manage risks more effectively, identifying potential vulnerabilities in their networks.
  • Customer Segmentation:
    Financial institutions can use Graph Analytics to segment customers based on their behaviors and relationships, enabling targeted marketing strategies and personalized services.
  • Anti-Money Laundering (AML):
    Financial organizations utilize Graph Analytics to identify and prevent money laundering activities by analyzing transaction networks and detecting unusual patterns that may indicate illicit behavior.

  • Customer Recommendation Systems:
    Retailers utilize Graph Analytics to analyze customer behavior and preferences, providing personalized product recommendations that enhance the shopping experience and increase sales.
  • Supply Chain Optimization:
    By visualizing the relationships between suppliers, products, and customers, Graph Analytics helps retailers optimize their supply chain processes, reducing costs and improving efficiency.
  • Market Basket Analysis:
    Retailers can analyze the purchasing patterns of customers to identify which products are frequently bought together, allowing for strategic placement and promotions.

  • Patient Relationship Management:
    Graph Analytics enables healthcare providers to understand the relationships between patients, treatments, and outcomes, leading to improved patient care and personalized treatment plans.
  • Disease Outbreak Prediction:
    By analyzing the connections between individuals and their health data, Graph Analytics can help predict and track disease outbreaks, facilitating timely interventions.
  • Clinical Trial Optimization:
    Researchers can use Graph Analytics to identify suitable candidates for clinical trials by analyzing patient data and their relationships with specific conditions or treatments.

  • Churn Prediction:
    Telecom companies can analyze customer relationships and usage patterns to predict churn, allowing them to implement retention strategies proactively.
  • Network Optimization:
    Graph Analytics helps telecom providers visualize and optimize their network infrastructure by analyzing the relationships between different network components and user demands.
  • Fraud Detection:
    Similar to finance, telecom companies can use Graph Analytics to detect fraudulent activities, such as SIM card cloning or unauthorized usage, by monitoring call patterns and relationships.

  • Claims Processing Optimization:
    Graph Analytics can streamline the claims processing workflow by mapping out the relationships between claimants, adjusters, and service providers, leading to faster and more efficient claim resolutions.
  • Customer Segmentation:
    Insurers use Graph Analytics to segment customers based on their behaviors and relationships, allowing for more targeted marketing strategies and personalized insurance products.
  • Risk Assessment and Underwriting:
    By analyzing the connections between various risk factors, Graph Analytics helps insurers improve their underwriting processes, enabling them to make more informed decisions about policy approvals and pricing.

  • Public Safety and Crime Analysis:
    Graph Analytics is used to analyze crime patterns and relationships between incidents, helping law enforcement agencies identify hotspots and allocate resources more effectively to enhance public safety.
  • Fraud Detection in Welfare Programs:
    Government agencies utilize Graph Analytics to detect fraudulent claims in welfare programs by examining relationships between beneficiaries and identifying suspicious patterns in claims data.
  • Infrastructure Management:
    Graph Analytics assists in managing and optimizing public infrastructure by analyzing the relationships between various infrastructure components, such as roads, utilities, and public transport systems, to improve maintenance and service delivery.

  • Entity Resolution:
    Entity Resolution is the process of identifying and merging the same entities from various data sources. By using Graph Analytics, relationships between entities can be analyzed to determine if they refer to the same entity, thereby helping to clean and unify data scattered across various systems.
  • Supply Chain Management:
    In supply chain management, Graph Analytics is used to map and analyze relationships between various entities, such as manufacturers, distributors, and consumers. For example, in the fertilizer industry, graph analysis can help track product flow from factories to end-users, identify bottlenecks, and optimize distribution processes to improve efficiency and reduce costs.
  • Transportation and Logistics:
    Graph Analytics can be used to analyze transportation and logistics networks, including shipping routes and relationships between various distribution points. By modeling data as a graph, companies can optimize shipping routes, reduce travel time, and improve operational efficiency. This also enables risk analysis and contingency planning in unexpected situations, such as traffic jams or natural disasters.

Contact

Address

Jl. Palatehan No. 4, Blok K-V, Kebayoran Baru, Jakarta

E-mail
(Contact Center)

cs.digital@peruri.co.id

E-mail
(Marketing)

digitalchannel@peruri.co.id

Whatsapp Chat