Like all game-changing ideas, Neo.Tax’s AI solve for R&D Tax Credits feels like a simple fix to a widespread problem. Every innovative enterprise company has to sort through mountains of data and conduct time-consuming interviews with engineers to file their IRC Section 41 R&D Tax Credit. Almost all of those same companies now use internal task-level data sources such as Jira and GitHub. Clearly, an AI tool should be able to outperform a human in ingesting and translating the datasets from multiple sources.

Of course, for a problem as meticulous and high stakes as taxes, the construction of the AI tool is of paramount importance. At Neo.Tax, our team of machine-learning experts, AI engineers, and tax experts dove into creating an LLM specifically fluent in R&D Tax-related inputs. 

"People have been waiting for a breakthrough application of AI for business,” Neo.Tax co-founder and CTO Firas Abuzaid says. “This LLM use-case for the R&D credit demonstrates how powerful AI can be when thoughtfully designed to solve a rote, time-consuming, and repetitive task.”

The LLM sorts through the varied datasets to determine which tasks are R&D-related and each employee’s effort-per-task to determine the percentage of their compensation that counts as a qualified R&D expense. On top of that, the LLM has been trained to determine duplicate tickets and other human-error inputting issues that muddy the data. Finally, the LLM aggregates all the qualified employee/contractor expenses for the company and calculates the total QREs, creating an accurate, exhaustive, and audit-proof IRC Section 41 filing (as well as a study that the IRS may request at a future date).

The IRC Section 41 LLM is a groundbreaking example of the ways that AI can revolutionize backend business processes. To learn more about how our technology works, download our White Paper: Leveraging AI in Conducting an IRC Section 41 R&D Tax Credit Study

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