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R&D Tax Credit for Software / SaaS / AI

New platforms, APIs, algorithms; ML/AI models, data pipelines, security, integrations, blockchain & fraud tooling.

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How the R&D credit applies to Software / SaaS / AI

Software companies are among the most consistent R&D credit claimants because the daily work of building and improving software maps directly onto the four-part test. Developing a new platform, designing novel algorithms, training and tuning ML models, or hardening security all involve resolving technical uncertainty through iterative experimentation.

The credit covers far more than greenfield product work. Re-architecting for scale, building data pipelines, integrating disparate systems, and developing fraud or anomaly-detection tooling typically qualify when the outcome isn't certain at the outset.

Work that often qualifies in Software / SaaS / AI

Typical qualifying roles

Software Engineer ML Engineer Data Engineer DevOps/SRE QA/Test Engineer Engineering Manager Product Engineer Security Engineer

Wages for time these roles spend on qualified research may count toward your credit.

Example credit scenario

Illustrative first-year ASC calculation

Qualified W-2 wages$500,000
Supplies$5,000
Cloud & computer rental$20,000
Contractor research spend$100,000
↳ 65% statutory haircut (IRC §41(b)(3))$65,000
Total QRE$590,000
Illustrative first-year ASC credit (6%) ≈ $35,400

Illustrative example using sample figures. Your actual credit depends on your facts; see Form 6765 and consult a tax professional.

The four-part test — applied to Software / SaaS / AI

The IRS requires qualifying research to satisfy four tests. Here's how each typically maps in this industry:

Permitted purpose

The activity aims at improving the performance and functionality of the platform.

Technological in nature

The work relies on computer science and software engineering.

Elimination of uncertainty

Unsure whether a given architecture will meet latency/scale targets.

Process of experimentation

Teams use prototyping, A/B testing, and benchmarking alternative designs.

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