The startup offers a graph analytics platform designed for complex data environments, enabling organizations to consolidate data into a single repository and perform real-time analysis. This capability provides businesses with comprehensive insights for improved decision-making and enhanced customer experiences in a highly interconnected landscape.
Funding
$910K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
PNFounders
Product
Problem
Organizations struggle to gain real-time insights from complex and interconnected data environments due to limitations of traditional relational databases. These legacy systems often fail to efficiently process and analyze the relationships between data points, hindering effective decision-making and the discovery of hidden risks and opportunities.
Solution
Locstat offers a graph intelligence platform, LightWeaver®, designed to address the challenges of complex data environments by transforming graph database technology into a comprehensive solution. The platform incorporates graph-based AI, analytics, and event processing to enable organizations to rapidly scale next-generation data solutions. LightWeaver® integrates data from various sources into a unified graph data repository, facilitating real-time analysis, KPI monitoring, and the generation of actionable insights through digital twins, alerts, and notifications. By leveraging graph database technology, complex event processing, machine learning, and a high-performance computation framework, Locstat empowers businesses to uncover hidden patterns, improve operational efficiency, and make data-driven decisions.
Target Audience
The primary target audience includes organizations across various industries such as financial services, retail, supply chain, IoT, telecommunications, maritime, mining, law enforcement, and intelligence, seeking to enhance their data analytics capabilities and gain actionable insights from complex data environments.
Features
- Flexible data capture and onboarding for real-time streaming and batched data from diverse sources, including sensors, message queues, and third-party systems.
- Cloud-based, distributed, and linearly scalable data lake architecture for data storage, compatible with private clouds, AWS, Azure, or GCP.
- High-performance computation engine for analyzing data to generate patterns, trends, and statistics in batch or real-time within a distributed computing framework.
- Complex event processing capabilities for setting intricate rules that model complex logic, time, uncertainty, and graph algorithmic metrics.
- Graph AI and machine learning tools for developing computational data science workflows and operationalizing them through MLOps.
- Graph analytics and visualization using industry-standard graph database technology with Apache Tinkerpop and Gremlin, enabling visual data analysis.
- Geospatial processing engine for real-time location intelligence from 2D and 3D interfaces, including spatial aggregation and density mapping.
- Data fabric for managing governance, privacy, rules, access permissions, and data pipelines to enable data discovery, analytics, consumption, and monetization.