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 organization.
How our skillset can help you claim.
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 narrative, supported by a practical and defensible approach to cost capture, helping you secure funding to reinvest in further model capability and deployment resilience.
When qualifying activities associated with developing AI, it is important to ensure there is an element of software development associated with designing and building out the artificial intelligence. Common areas that are eligible for the R&D Credit include:
- Model Development & Algorithm Design
- Training and Testing the AI on a Variety of Datasets & Models
- Data Engineering & Processing
- Unstructured Data and/or Scalability/Performance Issues
- System Infrastructure & Integration to Deploy AI Models at Scale
- Model Development that Supports Natural Language Processing (NLP)
Project Examples:
Developing a custom fraud detection model and iterating to improve accuracy by continuously evolving the data inputs, retraining the model based on prior outputs and enhancing the underlying source code used to drive the AI engine.
An asset management company developed an AI agent to replace a human trader that was capable of ingesting massive datasets in real time and cross reference the data points against an algorithm embedded within its engine to determine when to execute trades.
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.
