R&D Ledger is not affiliated with or endorsed by Neo.Tax. This independent comparison is based on publicly available information and is provided for educational purposes.
Last competitor information verified: August 2026
Businesses evaluating R&D tax credit automation may encounter Neo.Tax, which uses AI agents to process engineering and payroll data. This page provides an independent comparison of Neo.Tax’s publicly described model and the year-round internal documentation approach represented by R&D Ledger. For a broader framework, see our page on how to choose an R&D tax credit provider.
What Neo.Tax Publicly Offers
According to Neo.Tax’s current website, Neo.Tax provides AI for enterprise tax, automating the R&D tax credit in real time. The website describes the platform as ingesting data from systems including Jira, Linear, Workday, Payroll, Ledger, Confluence, Oracle NetSuite, GitHub, ADP, Azure, and others.
Neo.Tax’s website describes six AI agents that run continuously over engineering data: Project Creator, IRS Qualification, Quantifier, QRE Calculator, Project Narrative, and QRE Defender. The website states that each agent is trained on IRS code, regulations, and case law.
Neo.Tax’s website describes outputs including an R&D tax credit study, software capitalization (ASC 350-40), and R&D capitalization (Section 174). The website states that the platform is SOC 2 Type II certified and describes enterprise security features including single-tenant deployments, customer-hosted options, and closed-off LLMs. The website lists enterprise customers including Adobe, Block, Capital One, SoFi, Thomson Reuters, Mercury, Notion, and Zapier.
Provider and Engagement Model
Neo.Tax describes itself as an AI-driven enterprise platform. According to its website, the platform ingests data from engineering and payroll systems, runs AI agents continuously, and produces audit-ready deliverables. The model is software-driven, oriented toward enterprise tax teams.
How the Process Appears to Work
According to Neo.Tax’s website, the process involves ingesting messy company data (Jira tickets, payroll, org structure, GitHub, etc.), running six AI agents over the data, and producing audit-ready deliverables. The website describes AI analyzing 20+ passive signals across systems to qualify projects under Section 41.
Documentation Approach
Neo.Tax’s documentation approach, as described on its website, involves AI agents generating project narratives, QRE calculations, and audit-ready studies from ingested data. The website states that every figure is traced to source data.
Role of Software
Software is central to Neo.Tax’s model. According to its website, six AI agents run continuously over the company’s real engineering data to produce R&D credit studies, software capitalization, and Section 174 documentation. The platform is the primary product, with enterprise security features.
Role of Human Professionals
Neo.Tax’s website describes the platform as AI-driven, with the tax team reviewing the AI’s output. The website states that "AI does the work" and the tax team reviews, reducing the number of interviews needed. The model appears to be software-first with human review by the company’s own tax team rather than Neo.Tax providing advisory services.
Year-Round vs. Study Considerations
Neo.Tax describes a real-time, continuous AI model that runs over engineering data. This is distinct from a retrospective study model. However, the model is AI-driven and platform-centric, ingesting data from connected systems. Year-round internal documentation, as represented by R&D Ledger, involves the business’s own team organizing project, technical, cost, and evidence records throughout the year for professional review. For more on this distinction, see our page on year-round R&D documentation vs. annual study.
What a Buyer Should Compare
A buyer evaluating Neo.Tax against year-round internal documentation software may consider:
- AI-driven vs. documentation-first: Neo.Tax uses AI agents to process ingested data. Does the buyer want AI to generate the study from connected systems, or does it want to organize records internally for professional review?
- Enterprise scale: Neo.Tax describes enterprise customers and security features. Is the buyer at enterprise scale?
- Data integration: Neo.Tax describes integrations with Jira, GitHub, payroll, and other systems. Does the buyer use those systems?
- AI agents: Neo.Tax describes six AI agents for qualification, calculation, and narrative generation. Does the buyer want AI-generated narratives and calculations?
- Security: Neo.Tax describes SOC 2 Type II, single-tenant, and customer-hosted options. Does the buyer need enterprise-grade security?
For a broader comparison, see our page on R&D tax credit software vs. consultant.
When Neo.Tax’s Model May Make Sense
Neo.Tax’s model may make sense for a business that:
- is an enterprise-scale company with engineering and payroll systems that can be integrated;
- wants AI to automate the R&D credit study from connected data sources;
- has a tax team that can review AI-generated output;
- needs enterprise-grade security (SOC 2, single-tenant, customer-hosted); and
- wants software capitalization and Section 174 documentation alongside the credit.
When Year-Round Internal R&D Tracking May Make Sense
Year-round internal R&D documentation may make sense for a business that:
- wants to organize project, technical, cost, and evidence records throughout the year internally;
- already has a CPA who handles filing and needs organized records to support that work;
- prefers human-organized documentation rather than AI-generated narratives; and
- wants a documentation workspace that is not dependent on specific system integrations.
For more, see our page on best R&D tax credit software.
Can the Two Approaches Coexist?
A business could maintain year-round project and evidence records in a documentation workspace and also use an AI-driven platform like Neo.Tax for automated study generation. The internal records could complement the AI’s output by providing human-organized context. The two approaches are not mutually exclusive: a business might use internal documentation for ongoing recordkeeping and an AI platform for automated computation, with its tax team reviewing both.
Questions to Ask Before Choosing
- Is the business at enterprise scale with integratable engineering and payroll systems?
- Does the business want AI to generate the study, or does it want to organize records for human review?
- What security requirements does the business have (SOC 2, single-tenant, customer-hosted)?
- Does the business’s tax team have capacity to review AI-generated output?
- How does the platform’s pricing model align with the business’s expected benefit?
Key Takeaway
Neo.Tax is an AI-driven enterprise platform that uses six AI agents to automate R&D tax credit studies from connected engineering and payroll data, with enterprise security features and a focus on large-company tax teams. Year-round internal R&D documentation, as represented by R&D Ledger, involves the business’s own team organizing project, technical, cost, and evidence records throughout the year for professional review. The two approaches differ in automation level and scale orientation, and a buyer’s choice depends on its size, system integrations, and preference for AI-driven vs. human-organized documentation. For more on choosing a provider, see our page on how to choose an R&D tax credit provider.