To work alone your organisation must be a UK registered micro, small or medium sized enterprise (SME). Subcontractors are not allowed in this competition.

The competition is intended primarily for:

• research intensive AI startups
• deep tech SMEs
• university spinouts
• pre-commercial companies developing proprietary AI technology

An eligible organisation can only lead on one application. Any further applications by the same organisation as lead will be made ineligible.

Your project must:

• have total costs of between £50,000 and £150,000
• last between 1 and 3 months
• start by 1 April 2027
• end by 30 June 2027

Any organisation receiving funding must carry out its project work in the UK, intend to exploit the results in the UK, and spend most of the funding within the UK.

Up to 70% of costs can be covered, depending on business size.

The aim of this competition is to advance the development of novel reusable artificial intelligence (AI) mechanisms that will form the foundations for the next generation of AI and machine learning (ML) systems of the future.

Your project must drive development of a new core AI or ML technology by establishing experimental evidence for a new architecture, learning process, representation or control mechanism. The technology developed must have the potential to underpin capabilities, products, platforms or services across multiple future applications or markets.

We are looking for applications with a clear route to defensibility, such as protectable IP, proprietary data advantage, specialist know-how or other credible barriers to entry, with a path to scale.

Your project is not required to develop a complete commercial product. You must demonstrate the technical feasibility of the core mechanism, architecture or critical subsystem being developed.

Projects must start at a Technology Readiness Level (TRL) of 1 to 3 at time of application and we expect projects to be able to make an advance by 0.5 to 1 TRL level during project delivery.

Your proposal must demonstrate that AI or ML innovation is the core technical contribution and principal source of any potential competitive advantage you are likely to generate if successful.

Your project must deliver:

• experimental validation of your hypothesis
• a clear validation methodology against predefined metrics
• evidence of technical novelty and a technical asset
• a scaling rationale for the technology and business model
• a technical white paper

To be in scope of this competition you must sufficiently describe:

• the technical novelty and hypothesis driven experimentation statement
• what technical asset the project will create
• how it could underpin future products, platforms or services
• the justification of TRL classification

Your proposal must fall within one or more of the following themes. You must focus on at least one specific priority within each theme you select. If your application does not align with the theme and specific priority you select, it will not be sent for assessment.

Theme 1. Resource and data adaptive learning

Learning mechanisms that reduce dependence on large datasets, repeated full model training or unnecessary computation by adapting what information is acquired, retained or updated according to uncertainty, task requirements or expected information value. You must focus on one of the following priorities:

• active and probabilistic learning, including uncertainty aware selection of informative data, experiments, simulations or interactions
• continual and selective learning mechanisms that enable new knowledge or capabilities to be acquired without repeated full model retraining
• data efficient learning under limited or expensive observations, using probabilistic representations, structured priors or uncertainty to improve learning efficiency

Theme 2. Causal and intervention grounded models

Mechanisms that learn, discover or exploit causal and counterfactual structure to improve generalisation, prediction, intervention selection or decision making when underlying conditions or data generating processes change. You must focus on one of the following priorities:

• causal representation learning
• interventional or counterfactual learning
• causal discovery under partial observability

Theme 3. Learned modular computation and routing

Learning mechanisms that discover, select, compose or reuse internal computational components, enabling models to dynamically route computation and reuse learned functions across tasks, environments or novel combinations. You must focus on one of the following priorities:

• learned modular architectures and computational routing
• reusable learned functional primitives or subroutines
• adaptive composition and reuse of learned computational components

Theme 4. Geometric, equivariant and physics structured learning

Learning mechanisms that exploit or discover geometric, symmetry, relational or physical structure to improve data efficiency, generalisation, robustness or consistency beyond what can be achieved by learning these regularities from data alone. You must focus on one of the following priorities:

• equivariant or invariant learning architectures
• geometric and relational learning on structured or non-Euclidean domains
• physics structured learning, including methods incorporating physical symmetries, constraints, conservation structure or differentiable dynamics

We are not funding projects that:

• do not sufficiently provide clear background IP ownership or rights
• do not sufficiently provide a specific defensibility route
• do not align with the competition theme and specific priority areas
• do not sufficiently provide a named baseline with metrics and numeric targets for validation
• do not provide a falsifiable hypothesis driven experimentation plan
• do not sufficiently address the scope of the competition
• are primarily literature review studies, requirement gathering, without substantive experimental research and development,
• propose routine integration, deployment, orchestration or productisation of existing AI tools or third party models without novel AI or ML development
• are not delivering measurable and specific objectives
• are primarily routine integration or deployment of existing AI tools without substantive technical innovation
• don’t have clear technical novelty and feasibility challenge
• do not result in defensible foreground IP
• focus primarily on non-AI or non-ML research and development, including cross-cutting projects
• focus on prompt engineering, generic agent orchestration, routine Graph Neural Network (GNN) application, passive only agentic layers

Innovate UK will hold an online briefing at 11am on Friday 16 October 2026: click here for the joining link (Zoom required). A recording and slides will be available afterwards.

Contact Innovate UK’s Robotics & AI team for queries.