SR&ED for Software Companies
Software development can absolutely qualify for SR&ED, but CRA evaluates it differently than traditional R&D. Understanding these nuances is the difference between a successful claim and a denied one.
Why software SR&ED is different
Unlike pharmaceutical or manufacturing R&D, software development rarely involves lab equipment or physical prototypes. The "experiments" happen in code: branches, pull requests, failed deployments, and iterative refactoring. CRA recognizes this, but their evaluation framework was originally designed for traditional sciences. This creates a translation problem: your team's genuine R&D work needs to be framed in language CRA reviewers understand.
CRA's Application Policy on Eligibility of Work for SR&ED Investment Tax Credits includes specific guidance on software development. The key takeaway: software work qualifies when it involves resolving technological uncertainty that cannot be overcome through standard practice or routine engineering.
What qualifies as software SR&ED
Software work is eligible when it meets all three SR&ED criteria simultaneously:
- Technological uncertainty. The outcome wasn't achievable through known techniques or standard practice. You couldn't simply look up the answer, hire an expert, or follow existing documentation.
- Systematic investigation. Your team followed a structured approach: hypothesizing a solution, testing it, analyzing results, and iterating. This doesn't require formal lab notes. Pull requests, code reviews, and documented architectural decisions count.
- Technological advancement. The work generated new knowledge or capabilities that didn't previously exist within your organization. Even failed experiments that reveal what doesn't work count as advancement.
Examples of qualifying software work
Likely eligible
- Developing a custom real-time data pipeline where existing frameworks (Kafka, Spark) couldn't meet latency requirements, requiring novel approaches to message batching and backpressure handling
- Building a machine learning model for a domain where pre-trained models performed poorly, requiring experimentation with custom architectures, feature engineering, and training strategies
- Solving a concurrency problem in a distributed system where standard patterns (locks, queues) introduced unacceptable performance degradation, leading to development of a novel synchronization approach
- Creating a new algorithm to optimize resource scheduling where known heuristics (genetic algorithms, simulated annealing) failed to produce acceptable results within time constraints
Likely not eligible
- Building a CRUD application using established frameworks (Rails, Django, Next.js) with known patterns
- Integrating third-party APIs following their documentation, even if it takes significant time and debugging
- Migrating an application from one cloud provider to another using standard tools and runbooks
- Writing frontend UI components using standard design system patterns and component libraries
- Performance tuning through profiling and applying known optimization techniques (caching, indexing)
The "routine engineering" trap
The most common reason software SR&ED claims are denied is that CRA classifies the work as routine engineering, skilled work that uses known techniques to produce a predictable outcome.
This is where many companies stumble. Just because a project was difficult, time-consuming, or required senior engineers doesn't make it SR&ED. The critical question is: could a competent professional in the field have predicted the outcome using existing knowledge?
If the answer is yes, even if your team didn't know the answer at the time, the work is routine engineering. SR&ED eligibility requires that the uncertainty be technological, not just a knowledge gap within your team.
For example: building a recommendation engine might be routine if you're applying standard collaborative filtering. But if standard approaches produce unacceptable cold-start performance for your specific domain, forcing you to experiment with novel hybrid approaches, that's where SR&ED eligibility begins.
How SRED AI helps
SRED AI analyzes your pull requests and commit history to surface work that exhibits genuine technological uncertainty: experiments that failed, alternative approaches that were tried and abandoned, and iterative refinements that go beyond standard implementation. This helps you separate truly eligible work from routine development before you invest time writing the claim.
How to identify SR&ED-eligible work in your codebase
Look for these signals in your development history:
- Abandoned branches and reverted PRs. These often indicate experiments that didn't work, which is evidence of technological uncertainty and systematic investigation.
- Performance benchmarking commits. Systematic comparison of approaches to solve a performance problem shows the kind of experimentation CRA wants to see.
- Architecture decision records (ADRs). Documents that explain why standard approaches were rejected in favor of novel ones are powerful SR&ED evidence.
- Lengthy code review threads. Technical debates about trade-offs and alternative approaches often reveal the uncertainty and investigation that characterize SR&ED work.
- Proof-of-concept branches. Spike work that evaluates whether an approach is technically feasible demonstrates hypothesis testing.
Common mistakes in software SR&ED claims
- Claiming entire projects instead of specific uncertainties. Not every line of code in a project is SR&ED. Isolate the specific technical challenges that involved genuine uncertainty.
- Confusing business risk with technological uncertainty. "We didn't know if customers would use it" is market uncertainty, not technological uncertainty. SR&ED requires that the technical feasibility itself was in question.
- Weak project descriptions. Describing what you built rather than what you tried to overcome. CRA reviewers want to understand the problem, not the product.
- No contemporaneous evidence. Reconstructing your R&D narrative months later from memory is both difficult and less credible. Evidence created during the work (commits, PRs, design docs) is far more defensible.
- Overclaiming hours. Including time spent on non-eligible activities (project management, routine testing, deployment) inflates the claim and draws CRA scrutiny.
Positioning your software projects for SR&ED
The best time to think about SR&ED is before the fiscal year ends, not after. Here's how to set your team up for a strong claim:
- Train developers to recognize SR&ED-eligible work as it happens. Most engineers underestimate how much of their problem-solving qualifies
- Use pull request descriptions to document what was tried, what failed, and why. This creates contemporaneous evidence naturally
- Keep design documents and architecture decision records for non-trivial technical decisions
- Track time by project or feature, not just by sprint, since CRA needs hours tied to specific SR&ED projects
- Engage your SR&ED advisor early so they can help identify eligible work throughout the year, not just at claim time
Stop reconstructing what your team built
SRED AI extracts technological uncertainties and experiments from your GitHub pull requests, giving you a T661-ready first draft in minutes instead of weeks.