Research Grid has raised a $6.5m seed funding round led by Fuel Ventures, aimed at advancing its mission to expedite and improve the success rate of clinical trials.

The funding round saw contributions from various investors, including Ada Ventures, Arve Capital, Morgan Stanley Inclusive Ventures Lab, Plug and Play Ventures, Arāya Ventures, Atomico Angels and Ascension Ventures.

The investment will enable the company to focus on developing its AI capabilities to develop admin-free trials that will address the market’s blocks due to outdated software systems and manual processes, which lead to inefficacy, errors, and poor patient engagement.

Research Grid’s products, Inclusive and TrialEngine, are aimed to address this issue as they allow customers to manage the entire clinical trial lifecycle in one integrated platform.

These products leverage Research Grid’s patent-pending AI algorithms that automate administrative and data management workflows and eliminate tedious tasks.

The company’s customers reported an average of 98% efficiency gains, cost savings of 45%, and a 145% rise in patient engagement.

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With the new funding, Research Grid plans to invest in research and development (R&D), creating more AI automation and expanding its engineering teams.

The company also intends to enhance its marketing and sales operations as it grows its footprint in the US and Asian markets.

Research Grid founder and CEO Dr Amber Hill said: “We’re delighted to have the support of our investors. Now we’re focusing on expanding our engineering teams and scaling up our R&D efforts, allowing us to deliver more of the features our customers are asking for.”

Research Grid’s Inclusive product addresses the issue of patient recruitment, and sample collection delays by providing access to more than 91,000 global communities network, covering 2,050 medical conditions, and automating patient outreach and data management.

TrialEngine works in tandem with Inclusive to manage administration, data, and report workflows during the trial. It uses AI algorithms for automating tasks such as protocol generation and data extraction.