Gradient AI

• 30/07/2024
• Series C
• $56,100,000

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation.

Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast industry data lake comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance.

By using Gradient AI's solutions, insurers of all types achieve a better return on risk.

We are proud to have been chosen by Built In four years in a row, including three categories for 2024––Best Place to Work in Boston, Best Midsize Places to Work in Boston, and Best Midsize Places to Work Remote! Check out our page here: https://www.builtinboston.com/company/

Gradient AI was founded in order to address the need for state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) solutions for the trillion-dollar insurance industry. Our software-as-a-service platform uses AI to help commercial insurers automate and improve underwriting results, reduce claim costs, and improve operational efficiencies.

Our software and models are utilized by many of the world’s most recognized Insurance Carriers, MGAs, TPAs, Pools, PEOs and more. Our team of expert Data Scientists and Insurance Technology experts have an exceptional history of building wildly successful insurance technology companies, with the most satisfied customers in the industry. At GradientAI we focus exclusively on delivering measurable results for your underwriting and claims operations.

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