At Neo.Tax, we talk to scores of controllers and Heads of Tax every month. One thing is clear: the old way of filing for R&D Tax Credits has become a burdensome pain. You’d think that so many businesses moving their project management and payroll online, via software like Jira, Rippling, and Workday would simplify the filing process. But, in some ways, it’s made it harder.

The payroll and project management output is a rich dataset, but it’s also, well, voluminous. If that data is not inputted perfectly—and let’s be honest, it never is—that means it’s up to a controller or Head of Tax to sift through the mess to create the filing. Engineers need to be trained (and retrained) on the correct project tagging process, and then cajoled to sort out any inputting errors. It’s a waste of time for your filer and your engineering teams.

Which is why Neo.Tax created our AI system. 

‍But what if we have messy or incomplete Jira, Linear, or Azure DevOps data? Can we still use Neo.Tax?

Here is a non-exhaustive list of concerns we’ve heard about data that we’ve heard from companies interested in trying Neo.Tax’s AI-powered solution. (Spoiler Alert: Neo.Tax’s AI system can handle each and every one.)

Hierarchy

• Inconsistent use of Epics/Projects. Some teams use them, others don’t
• Inconsistent naming of Epics & Projects
• Hierarchy doesn’t map well to Capitalization / R&D — it’s all mixed together
• Project names too technical

=> How we solve: 

Our AI system organizes your data into a consistent structure with clear high-level summaries of the work–exactly what you would do if you had infinite time.

• That means it automatically takes into account hierarchy where it exists
• And when it doesn’t we, our system clusters similar tickets together based on phrases, details, who worked on them, and other metadata

Tagging/Labeling

• Don’t tag projects as R&D
• Have tag but don’t consistently use it

=> How we solve: 

Our AI system automatically qualifies each project by analyzing the underlying ticketing data.  

• That means engineering teams no longer need to care about this — at all.

And because our AI is expert trained on codes & regulations, court cases AND on the technical details, it evaluates each project step by step based on the 4-Part Test to understand if it’s technical, has a qualified purpose, and involves technical uncertainty. Best of all, it summarizes its reasoning and flags edge cases for a filer to focus on.

Time/Effort/Status

• Team doesn’t track time at all or does so inconsistently
• Don’t always close tickets on time

=> How we solve: 

Our AI system calculates effort automatically via passive metadata. No time tracking, story points, etc required.

• Our AI does this by looking at status changes and other metadata
• Then, it weights the time spent based on the content of the work
• Finally, it normalizes for each person based on all work in progress at the same time

Multiple Systems 

• Some teams use Jira, others in Linear.

=> How we solve:

Connect all the systems to Neo.Tax and we sort out the complexity. That’s all you have to do.

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What That Means For You

So, if you’re worried about the status of your project management or payroll data, that’s not a reason to avoid an AI solution. In reality, it’s actually the reason to go with Neo.Tax. Because we’ve trained our AI system to do things like identify duplicate entries and identify mislabeled or untagged projects. 

Large language models love large bodies of text. What would take a human months can be done in an instant. Even with the messiest of data, Neo.Tax can deliver an R&D credit with better substantiation than the time-consuming process of engineer interviews, because it’s one based on contemporaneous data, which the IRS now prefers.

So get in touch and we’ll show you how Neo.Tax has solved for R&D.