R&D tax credit claims fail on documentation, not on merit. The engineering work was real. The technical uncertainty was real. What is missing is a defensible record connecting the two, and reconstructing that record after the fact costs more than the credit is worth for some teams. Staff spend weeks hunting through repositories, ticket systems, and calendars, then ask engineers to describe projects they finished eighteen months ago from memory.
The result is a claim that is weaker than the underlying work. Narratives drift between business units. Hours are estimated rather than evidenced. Reviewers ask for supporting detail that nobody can produce, and the credit is reduced or disallowed on procedural grounds. R&D tax credit documentation exists to fix the evidence problem, not the accounting problem.
Most companies assemble the claim annually. A finance or tax team sends a spreadsheet to engineering managers, who forward it to engineers, who fill it in between sprint work. The responses are recollections: approximate percentages, project names that no longer match the repository, and descriptions of technical problems that have since been solved and forgotten.
The evidence that would settle the question already exists. Commit history shows when work started and how long it continued. Issue trackers record the technical uncertainty, the failed approaches, and the resolution. Design documents capture the alternatives considered. None of it is connected to the claim, because the systems that hold it were never built to talk to each other.
The cost shows up twice. Engineering teams lose weeks to paperwork during a period when they are also shipping. Finance teams carry risk they cannot quantify, because a claim supported by recollection is a claim that depends on the reviewer accepting the narrative.
The platform connects the systems that already hold the evidence and assembles the documentation file from them. It delivers:
Engineers review and confirm what the system assembled instead of writing narratives from scratch. Finance teams get a documentation file with traceable provenance, so a reviewer asking how a figure was derived gets a link to the record rather than an explanation. The hours that were spent reconstructing history go back into engineering work.
Shakudo deploys inside the company's own environment and connects to the systems that hold engineering evidence: version control, issue tracking, documentation stores, and time or project systems. Pipelines extract activity, normalize it against a consistent project model, and link each qualifying project to the technical uncertainty that justified it.
The mapping is configured around the company's own definition of qualifying activity and its own project taxonomy, rather than a generic template. Engineers receive a review queue where they confirm or correct what the system inferred, which keeps a human in the loop on anything that will be submitted. Finance teams generate the documentation file on demand, with each line traceable to its source. A first working pipeline, connecting one engineering group's activity to a draft documentation file, is in place within days of deployment.
Airbyte moves data from version control, issue trackers, and project systems into the platform on a schedule. Postgres holds the normalized project and activity model that the claim is built from. dbt applies the transformation logic that maps raw activity to qualifying projects and computes the figures that appear in the documentation file. Elasticsearch makes the underlying evidence searchable, so a reviewer or an engineer can locate the issue, commit, or design document behind any line in the claim. n8n orchestrates the review workflow, routing assembled projects to engineers for confirmation and collecting their responses. Metabase provides the dashboards that finance teams use to track claim progress, review coverage, and the status of each business unit's documentation.
This serves companies that claim the R&D tax credit or a comparable research incentive and run engineering organizations large enough that manual documentation has become a bottleneck. Finance and tax teams use the assembled documentation file and its traceable provenance. Engineering managers use the review queue to confirm what their teams worked on without writing it up from memory. Technical leads provide the detail on technical uncertainty where the system flags a gap. It fits organizations where engineering work is spread across many repositories and teams, and where the documentation burden currently falls on the people who are also expected to ship.
By tying each qualifying project to evidence of technical uncertainty and the work done to resolve it. The strongest documentation is contemporaneous, drawn from the systems where the work happened rather than reconstructed afterward. Connecting repositories, issue trackers, and project systems into one model gives finance teams a file where every figure traces back to a source record.
Commit history and pull requests show when work occurred and how long it continued. Issue trackers record the technical problems, the approaches that failed, and the resolution. Design documents capture alternatives that were considered. Together they establish that a project involved technical uncertainty and that qualified people spent time resolving it.
Yes. The system extracts activity from systems engineers already use, then presents a review queue where they confirm or correct the result. That shifts the work from writing narratives to checking them, which takes minutes rather than hours. Engineers stay in the loop on anything submitted, without carrying the documentation burden.
No. The platform runs on infrastructure the company controls, whether on-premises or in its own cloud. Repository data, issue history, and project records stay inside that boundary under the company's own governance. That matters when the evidence file contains unreleased product detail or sensitive technical information.
When the goal is a documentation file that holds up to review, a conversation with Shakudo is the fastest way to see it built from your own engineering data. The platform runs inside your own environment, and a first working pipeline is in place within days. Book a demo and scope R&D documentation for your teams.