SR&ED for AI and Machine Learning Projects (2024)

AI and machine learning development is inherently experimental—testing models, tuning parameters, and iterating toward uncertain outcomes. This makes AI/ML work well-suited for SR&ED tax credits, yet many companies don't claim or significantly underestimate their eligible work.

Why AI/ML Projects Qualify for SR&ED

Natural SR&ED Fit

AI/ML development inherently involves:

Technological uncertainty - Will the model achieve target performance?
Hypothesis testing - "This architecture should outperform that one"
Systematic experimentation - Testing configurations, analyzing results
Advancement - Achieving capabilities beyond existing methods

The Opportunity

Development is highly experimental by nature
Strong documentation habits (experiment tracking)
Claims often $100K-$500K+ for ML-focused companies
Particularly valuable for pre-revenue AI startups

What AI/ML Work Qualifies

Eligible AI/ML SR&ED

Model Development:

Novel architectures for specific problems
Achieving accuracy beyond existing approaches
Transfer learning for new domains
Multi-modal or ensemble approaches

Training Optimization:

Novel training strategies
Custom loss functions
Data augmentation approaches
Hyperparameter optimization beyond grid search

Performance Challenges:

Inference speed optimization
Model compression and quantization
Edge deployment challenges

Data Challenges:

Handling sparse or noisy data
Few-shot learning approaches
Active learning strategies
Synthetic data generation

Domain Applications:

NLP for specific domains with uncertainty
Computer vision for novel problems
Recommendation systems
Time series forecasting

Not Eligible

Applying off-the-shelf models without modification
Standard fine-tuning without technical challenge
Routine data labeling
Using AutoML without experimentation
Standard model deployment

AI/ML SR&ED Examples

Example 1: Custom NLP Model

Project: Build sentiment analysis for financial text

Uncertainty: General-purpose models achieved only 65% accuracy on financial jargon and numeric context. No existing model handled domain-specific language.

Experimentation: Tested 6 architectures (BERT, RoBERTa, custom transformers). Experimented with domain-specific pre-training, custom tokenization, numeric feature integration. Tested 50+ configurations.

Advancement: Achieved 89% accuracy through fine-tuned FinBERT with custom numerical encoding. Proved transfer learning approach for financial text.

Claim value: $135,000

Example 2: Computer Vision Edge Deployment

Project: Deploy object detection on resource-constrained edge device

Uncertainty: Required 30fps inference on device with 1GB RAM. Standard models couldn't meet latency/memory constraints while maintaining accuracy.

Experimentation: Tested model distillation approaches, developed custom pruning strategies, experimented with quantization-aware training, optimized for specific hardware.

Advancement: Achieved 35fps at 85% mAP (vs. 92% full model). Developed quantization approach reducing model size 8x with 7% accuracy loss.

Claim value: $95,000

Example 3: Recommendation System

Project: Build recommendation engine for B2B marketplace

Uncertainty: Cold-start problem with limited user data. Existing collaborative filtering achieved <15% engagement.

Experimentation: Tested hybrid approaches, experimented with knowledge graph integration, developed custom embedding strategies, tested various negative sampling approaches.

Advancement: Achieved 32% engagement through novel hybrid system combining collaborative signals with content features. Solved cold-start using entity embeddings.

Claim value: $160,000

Documentation for AI/ML SR&ED

Strong Documentation Habits

Many ML teams already document well:

Experiment tracking (MLflow, Weights & Biases)
Model versioning
Hyperparameter logs
Performance metrics

Leverage these tools for SR&ED documentation.

Key Documentation

For each experiment:

Hypothesis and objectives
Model architecture
Training configuration
Results and metrics
Analysis and conclusions
Next steps based on results

Example Experiment Log

Experiment: transformer-v3-attention
Date: 2024-03-15
Hypothesis: Multi-head attention with learned query embedding will improve accuracy
Configuration:
  - Architecture: Custom Transformer
  - Heads: 8
  - Dimensions: 256
  - Learning rate: 3e-4
Results:
  - Accuracy: 82.3% (baseline: 78.1%)
  - Inference time: 45ms
Analysis: Attention mechanism shows improvement but inference too slow
Next: Test distillation or quantization

Expenditure Considerations

Salaries

Eligible roles:

ML engineers
Research scientists
Data scientists (when doing model development)
MLOps (when building experimental infrastructure)

Note: Data labeling staff typically not eligible (routine work)

Cloud Computing

Often significant for AI/ML:

GPU compute for training
Experimentation infrastructure
MLOps platforms for R&D

Not eligible:

Production inference costs
Commercial deployment
General cloud services

Data Costs

Eligible:

Data for experimentation
Data augmentation tools
Annotation tools used in R&D

Not eligible:

Production data pipelines
Ongoing data feeds for commercial use

Maximizing AI/ML SR&ED Claims

1. Document Experiments Systematically

Use experiment tracking tools. Each run should show:

What you tested
Why you expected it to work
What happened
What you learned

2. Include All Cloud Costs

Training runs, experimentation compute, development infrastructure.

3. Track Time by Project

Allocate ML engineer time to specific model development projects.

4. Include Failed Experiments

Many experiments don't improve performance. These are valuable SR&ED evidence—you tested hypotheses and learned.

5. Show the Uncertainty

Don't just say "we built a model." Explain what wasn't known and how you experimented to solve it.

Work with AI/ML-Savvy Consultants

AI/ML teams need consultants who understand:

Modern ML architectures and frameworks
Cloud computing costs for training and experimentation
The difference between routine deployment and technical innovation
How to articulate technical uncertainty

See our detailed guide on How to Choose an SR&ED Consultant →

Browse Consultants by Service

AI/ML companies typically benefit from:

Claim Preparation Services - End-to-end claim preparation
Technical Documentation Services - Writing compelling technical narratives
Financial Analysis Services - Optimizing cloud compute costs
Eligibility Assessment Services - Determining what development qualifies

Find AI/ML SR&ED Consultants →

Frequently Asked Questions

Is fine-tuning pre-trained models SR&ED?

If it involves genuine uncertainty and experimentation beyond standard approaches, yes. Simple fine-tuning without technical challenges doesn't qualify.

Can we claim GPU cloud costs?

Yes, for experimentation and training. Production inference costs aren't eligible.

What about data labeling?

Generally not eligible as it's routine work. Novel annotation approaches with technical challenges may qualify.

Is hyperparameter tuning SR&ED?

Standard grid/random search isn't typically eligible. Novel optimization approaches or search spaces with genuine uncertainty may qualify.

Do we need to publish papers?

No. SR&ED doesn't require publications or external validation.

Next Steps

Review ML projects for technical uncertainty
Audit experiment tracking for SR&ED documentation
Identify failed experiments as SR&ED evidence
Calculate cloud computing costs for R&D
Connect with AI/ML SR&ED specialists

Find AI/ML SR&ED Consultants →

Last updated: November 2024. Consult a qualified SR&ED professional for your specific situation.

SR&ED for AI and Machine Learning Projects (2024)