SR&ED for AI and Machine Learning Companies

AI and machine learning development is fundamentally experimental — that's what makes it one of the strongest categories for SR&ED eligibility. If your team is training models, tuning architectures, or solving inference problems that existing frameworks can't handle, you likely have a strong claim.

Why AI work commonly qualifies

Machine learning is inherently iterative and uncertain. Choosing a model architecture, selecting training data, tuning hyperparameters, and optimizing inference pipelines all involve genuine technological uncertainty. CRA has consistently recognized that AI/ML development involving systematic investigation of technical unknowns is SR&ED-eligible.

AI and ML work that typically qualifies

• Model architecture research where standard architectures fail to meet accuracy, latency, or compute constraints for a specific domain
• Training data investigation — determining what data characteristics, augmentation strategies, or labelling approaches are required
• Inference optimization on constrained hardware where existing frameworks don't meet latency or memory requirements
• Novel loss function or training objective design for tasks where standard losses are insufficient
• Multi-modal learning and sensor fusion where temporal alignment or cross-modal integration required original investigation
• Uncertainty quantification or calibration in high-stakes applications where model confidence was not achievable with standard techniques
• Federated learning or privacy-preserving ML where novel protocol design was required

AI work that is less likely to qualify

• Fine-tuning pre-trained foundation models on new data using standard procedures
• Deploying existing models into production using documented frameworks
• Standard prompt engineering and LLM integration work
• Running experiments where the approach is fully documented and the uncertainty is operational, not technical
• Purchasing and integrating third-party AI APIs without original model development

Documenting AI SR&ED work

AI and ML work often generates excellent evidence — the challenge is organizing it in a way CRA can follow:

• Training run logs with loss curves, accuracy metrics, and configuration details
• Benchmark results comparing model variants against baseline and target metrics
• Hyperparameter search records (W&B, MLflow, TensorBoard logs)
• Architecture decision documents — why specific choices were made and what alternatives were rejected
• GPU/compute usage records as evidence of systematic experimental activity
• Dataset version control and documentation of data preprocessing investigations