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    BridgeAI Deployment Catalyst: AI for decision critical systems

    BridgeAI Deployment Catalyst: AI for decision critical systems supports UK AI SMEs and deployers to test existing AI solutions for high-risk decisions, building evidence towards trusted live deployment.

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    This grant was recently updated

    Applications are now open, with links to start a new application or sign in to continue an existing one. The online briefing recording and applicant briefing slides are now available, replacing registration and notices that materials would be provided after the event.

    Funder: UK Research and Innovation

    Due Dates: December 8, 2026, 11:00am UK time (Application deadline) | February 1, 2027 (Notification) | April 1, 2027 (Start by) | August 31, 2027 (Finish by)

    Funding Amounts: £100,000–£200,000 per consortium for three to five months; up to £5 million available

    Summary: Supports UK AI SMEs and deployers to prepare existing AI and machine learning solutions for consequential decision-making.

    Key Information: Collaborative projects only: two or three organisations, led by a UK-registered AI SME with a TRL 5–6 solution.


    Description

    Innovate UK, part of UK Research and Innovation, runs this Phase 1 competition to develop evidence-based deployment use cases for existing advanced AI and machine learning solutions. The Decision Critical Systems strand supports systems whose primary purpose is helping people make high-risk judgements, recommendations or decisions with significant consequences for deployers or end users.

    Projects must move beyond routine implementation and address substantive technical, operational, regulatory or assurance barriers. Consortia will assess supplier and deployer readiness, establish the solution’s operational value, and demonstrate it using representative deployer data and systems in a representative operational environment. They must also obtain support letters from potential future deployers and outline preparation for Phase 2. The longer-term aim is trusted live deployment and replication, rather than early-stage research or generic productivity improvements.


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