Ai
Developing AI is one of the most common and strongest areas for the Research & Development Tax Credit, because it naturally involves technical uncertainty, complex algorithms and iterative experimentation. AI requires designing new machine learning or deep learning models that improve model accuracy, speed and efficiency. These efforts become eligible for the R&D credit when a company is solving for how to achieve a technical outcome that is unknown at the outset as opposed to applying a known model that is commercially available or already known within the organisation.
How our skillset can help you claim.
Development is often driven by experimentation because performance depends on the interaction between data quality, augmentation, model architecture, hyperparameters, and the inference pipeline used in production. Our specialist team works directly with your engineers to define the advance being pursued, set a clear baseline against what existing models and documented methods can achieve, use training and testing evidence to support the iterations made, and separate qualifying R&D from routine integration or prompt-level usage of third-party tools. We then set out the development work in a clear, HMRC-ready narrative, supported by a practical and defensible approach to cost capture, helping you secure funding to reinvest in further model capability and deployment resilience.
Project Examples:
A common advance is achieving production-grade accuracy where images, text, or sensor inputs vary widely in quality, lighting, format, or noise, and standard models degrade materially. Progress is demonstrated through iterative training and validation that shows reliable performance across representative real-world conditions.
Projects may develop pipelines that combine multiple processing stages (e.g., detection then segmentation, or extraction then classification) where a single-pass approach fails to meet accuracy targets. The advance is evidenced by designing and validating the end-to-end workflow so outputs remain aligned and stable across varied inputs.
Some work focuses on enabling high-volume experimentation and reliable deployment by building GPU-based training/inference orchestration, monitoring, and reproducible evaluation. The advance lies in proving the system can run consistently at scale with predictable performance, rather than relying on ad hoc training runs or manual deployment steps.
