HammerAI captures and structures how R&D happens — the reasoning, experimentation, and validation behind every claim — creating compliant, defensible records that qualify tax credits, protect IP, and retains enterprise intelligence.
The adoption of coding assistants such as Copilot and similar technologies reflects a broader shift: code is now co-authored by humans and AI.
Regulators, auditors, and IP teams will soon require proof of human reasoning and authorship within AI-assisted workflows.
HammerAI provides the context layer — capturing how ideas evolve between humans and AI systems, creating a verifiable innovation ledger.
"Built at the intersection of R&D governance and AI systems engineering."
HammerAI is a research and technology company founded by experts in AI, data lineage, and R&D tax governance.
Originated from large-scale SR&ED and §41 engagements with Fortune 500 financial clients and leading firms including PwC.
Core focus: transforming human-AI interaction data into structured evidence and enterprise intelligence.
"AI has accelerated output — but fragmented provenance."
"IRS and CRA are formalizing attribution expectations. The evidentiary lens is shifting from lines of code to lines of reasoning."
Provide a detailed record of the time and resources invested
Demonstrate experimentation, technical uncertainty, and systematic process
Explain the challenges and technical issues being solved
Software companies face significant challenges in effectively preparing audit ready R&D tax credits due to scattered evidence, lack of documentation, and developers not writing with R&D language in mind.
"This is infrastructure — not productivity software. HammerAI complements AI Coding Assistants by delivering a record of how innovation actually happened."
The system scans and discovers all available evidence sources, normalizes and logs every data point, and creates a complete inventory of the evidence.
The system scans the data environment (wherever files are stored) to identify and discover all available sources of evidence, such as Git repositories, Jira tickets, Slack conversations, and Confluence documents.
The system normalizes and logs every relevant data point, including commits, pull requests, issues, documents, and discussions, to create a comprehensive and structured record of the available evidence.
By scanning and logging all the available evidence, the system generates a complete inventory of the company's R&D activities, providing a clear understanding of the resources and information that can be leveraged for the R&D tax credit process.
The system analyzes the complete evidence to map relationships, understand complexity and uncertainty, and identify patterns of experimentation and problem-solving.
The system examines the comprehensive evidence catalog created in Phase 1 to gain a deep understanding of the software development activities.
The system analyzes the relationships between different components, decisions, and iterations within the software development process.
The system identifies and articulates the technical challenges, uncertainties, and complexities faced by the software development team during the R&D process.
The system detects and highlights the patterns of experimentation, problem-solving, and iterative development that occurred throughout the R&D activities.
The two-phase approach provides a comprehensive inventory of all available evidence sources, normalizing and logging every data point related to the R&D activities.
The system context analysis enables a deep understanding of the relationships between components, technical challenges, and patterns of experimentation and problem-solving.
The two-phase approach automates the evidence discovery and logging, reducing the manual effort required by developers to document their R&D efforts.
By unlocking the full potential of their R&D activities, software companies can identify more eligible projects and secure higher tax credit benefits.
With a complete evidence catalog and a detailed understanding of the R&D activities, software companies can effectively prepare and maximize their R&D tax credit claims.
The HammerAI team combines 30+ years of SR&ED and R&D tax experience with deep technical and AI expertise — but what truly sets us apart is that we've productized the expertise.
Manually reconstruct R&D stories once a year, relying on interviews and spreadsheets. We've spent decades doing that work, defending billions in claims, and we know exactly where the process breaks.
Doesn't replicate the manual model — we've codified it into software. Our system captures and analyzes evidence directly from the work itself, producing structured, verifiable data instead of retrospective narratives.
That means every HammerAI output — from SR&ED eligibility assessments to gap analyses and predictive analytics — is grounded in real evidence, not recollection.
By leveraging a comprehensive evidence catalog and deep system understanding, companies can confidently substantiate their R&D activities and maximize their tax credit claims, empowering them to continue driving innovation.