SR&ED for AI, ML & Blockchain: Make Your Case

Introduction

Emerging tech domains like AI, ML, and blockchain present rich opportunity—and complexity—for SR&ED claims. Because outcomes aren’t always predictable, credible claims require careful logging, narrative framing, cost attribution, and audit readiness.

In this post, we’ll walk you through how to structure AI/ML/blockchain R&D so it qualifies, what evidence auditors look for, pitfalls to avoid, and how to integrate compute costs.

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Why They Qualify

• Projects typically start with technical uncertainty (which architecture, model, consensus method will succeed?)
• You operate via systematic experimentation (testing variants, hyperparameters, architectures)
• You learn and refine, pushing the boundary of what was known

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But your claim must go beyond just “we used machine learning.” It must explain why, how, what you tested, what failed, and what you gained.

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Eligible Activities & What to Capture

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Best Practices

1. Version control everything (code, dataset versions, config files)
2. Log metrics & experiments (loss, error rate, convergence)
3. Record failures — failed runs are valuable proof
4. Separate R&D vs production compute clearly
5. Narrative journal for each experiment: hypothesis, method, results, next step
6. Time attribution: annotate developer hours to experiment runs

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Sample Use Case

A startup developing a fraud-detection model tests different architectures (e.g. CNN, LSTM, transformer) across input feature sets. Each run is versioned, metrics tracked, failed approaches recorded, and pivot decisions logged. GPU hours per experiment are tracked separately from production inference runs. The narrative weaves uncertainty → experiments → outcome → learning.

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Common Pitfalls

• Black-box claims like “we used AI” without experiment detail
• Claiming production compute usage as R&D
• Vague narrative not tied to experiments
• Retroactively reversed logs or story rewriting
• Weak cost attribution of compute, labour

Pre-Submission Checklist

• Versioned experiment logs with metrics
• Narrative linking uncertainty → experiments → learning
• Cost maps (labour, compute) aligned to experiments
• Clean separation between R&D and production work
• Backup evidence: code diffs, logs, meeting notes
• Well-structured submission: index, cross references

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How GovMoney Can Help

GovMoney’s Advanced Tech R&D Service helps your AI/ML/blockchain teams by designing logging frameworks, advising cost splits, reviewing narratives, and preparing you for audit questions. We help your experiments become claims.