# Mobikit > Unified telematics data platform that harmonizes connected vehicle data for insurance carriers, fleets, and automotive organizations\. ## At a glance | Field | Value | | --- | --- | | Category | Data & Analytics | | Type | Solutions Provider | | Region | NA | | Headquarters | Columbus, Ohio | | Founded | 2019 | | Stage | Growth | | Employees | 1-10 | | Funding | Acquired | ## Overview Mobikit is a SaaS telematics data platform built to help insurance carriers, fleet operators, and automotive organizations integrate and analyze connected vehicle data at scale. Founded in 2019 by Arnab Nandi (an Ohio State University associate professor) and Igor Ferst, the company was acquired by Azuga -- a Bridgestone company -- on March 9, 2021. The platform solves a core data engineering problem: telematics data arrives from dozens of incompatible hardware and software sources, making it difficult for insurers to build consistent risk models. Mobikit harmonizes these fragmented streams into a single, standardized data pipeline, enabling underwriting, loss control, claims, and data science teams to work from clean, reliable vehicle data without building complex internal infrastructure. Post-acquisition, Mobikit operates as a subsidiary of Azuga, integrating its data platform with Azuga's driver performance analytics and video telematics capabilities. The combined offering strengthens insurance risk management and fleet safety solutions for carriers and commercial fleets alike. ## Products & services ### Data Harmonization Unifies telematics data from 50+ hardware and software providers -- including OBD-II devices, smartphone apps, and vehicle APIs (Tesla, BMW, General Motors) -- into a single, standardized data stream. Supports cloud-agnostic deployment across AWS, Google Cloud, and Microsoft Azure. **Key features:** - Ingestion from 50\+ telematics providers - Standardized data schema across all sources - Cloud\-agnostic architecture \(AWS, GCP, Azure\) *Target users:* Insurance carriers, MGAs, fleet operators ### Automated Feature Extraction Machine learning models extract driving behavior indicators, risk signals, and vehicle-specific metrics from raw telematics data. Includes third-party data enrichment with weather data, road conditions, and historical records. **Key features:** - ML\-based driver behavior scoring - Risk signal extraction and anomaly detection - Third\-party data enrichment \(weather, road, historical\) *Target users:* Underwriting and data science teams ### Analytics & Exploration Advanced analytics dashboards and exploration tools enable insurance teams to identify patterns, assess driver risk, and build data-driven underwriting models. Supports SQL-based custom queries and data visualization. **Key features:** - Configurable dashboards and visualizations - SQL\-based custom query support - Risk pattern identification for underwriting *Target users:* Underwriting, loss control, claims, actuarial teams ### Turnkey Insurance Workflow Integrations Purpose-built connectors for core insurance use cases, including fleet qualification and quoting, loss control program optimization, claims investigation, and underwriting model development. **Key features:** - Fleet quoting and qualification acceleration - Loss control program tooling - Claims investigation workflow support *Target users:* Carriers, MGAs building telematics programs ## Category & focus - Subcategories: Telematics, Connected Vehicle Data, Risk Scoring, Data Harmonization - Insurance verticals: P&C Personal, P&C Commercial - Target customers: Carriers, MGAs/MGUs, Brokers ## Notable customers - Auto and commercial insurance carriers - Fleet management companies and operators - MGAs and brokers developing telematics programs - Automotive companies building risk assessment models ## Links - Website: - Directory profile: - Contact: - Linkedin: - Insurance solutions: - Technology/platform: --- *Last updated: 2026\-06\-03*