Key takeaway: SR&ED automation software doesn't decide whether your claim gets approved — evidence does. The CRA accepted 90% of claims as filed in FY2025-26 with no review at all, and that number tracks how well-documented the underlying work was, not which tool touched the paperwork. What automation actually buys you is contemporaneous documentation: a technical record built from real commits and tickets while the work happens, instead of reconstructed from memory the week before your 18-month filing deadline. The software captures. A qualified, independent SR&ED expert still reviews and stands behind what gets filed.
SR&ED automation software doesn't file your claim for you, and any platform that implies otherwise is selling you the wrong pitch. What it actually does is change where the documentation comes from. Instead of an engineer reconstructing eighteen months of work from memory every spring, the technical record gets built continuously, from the commits, tickets, and discussions where the R&D actually happened. That shift changes what a claim looks like on the page, and it changes how the claim holds up if the CRA asks questions. Here's what SR&ED automation software actually automates, what it still can't touch, and what to check before you trust one with your claim.
How SR&ED automation software actually works
SR&ED automation software connects to the tools your team already uses, GitHub, Jira, Slack, and pulls a running record of technical work as it happens, instead of asking someone to write it all down after the fact. Every commit message, every closed ticket, every thread where an engineer debugged a hard problem becomes raw material the platform can surface later. None of that is analysis. It's collection, done continuously instead of once a year.
What separates a real automation platform from a glorified export tool is what happens next: turning scattered technical activity into something that maps onto the CRA's eligibility test. That means flagging work that involves genuine technological uncertainty, grouping related commits and tickets into a coherent project narrative, and drafting a first pass at the language the CRA expects to see. The software does the pattern-matching. A human still checks whether the pattern actually clears the bar.
The year-end scramble it replaces
Picture a 20-person engineering team in March, staring down a T2 filing deadline, trying to remember what they built fourteen months ago. Someone pulls up a spreadsheet. Someone else messages an engineer who left the company in October. A third person is guessing at hours because nobody logged time against specific projects. This is still how a lot of companies build their SR&ED claim, and it produces exactly the kind of narrative you'd expect from that process: vague, generic, thin on the specific technical uncertainty the CRA is actually looking for.
The traditional model isn't just slow. It's lossy. Smaller qualifying activities get dropped because nobody remembers them by filing season, and the work that does make it into the claim gets described in whatever language the person reconstructing it happens to reach for, months after the fact. A founder who "just wants the credit" ends up spending weeks doing archaeology on their own company instead of running it.
The stakes for getting this wrong scale with the company. A CCPC spending at the current $6 million expenditure limit can earn up to $2.1 million a year in enhanced credits (more on how that limit and the deadline interact), but only on the qualifying spend that actually makes it into the claim. Undocumented work doesn't reduce that ceiling on paper; it just quietly shrinks the claim underneath it, one forgotten project at a time. That's not a rounding error from a rushed March scramble. It's the difference between a claim that captures what the team actually built and one that captures whatever a handful of people happened to remember.
What the software actually automates
Three things, mostly. Activity identification — scanning code repositories, project trackers, and internal docs to surface work that may qualify, instead of waiting for an employee to self-report it. Real-time capture — logging the technical detail (what was uncertain, what was tried, what failed) at the moment the work happens, so nobody has to reconstruct it later. Narrative drafting — assembling a first pass at the T661 project descriptions from that captured evidence, in roughly the structure a CRA reviewer expects.
That's genuinely useful. It's also, deliberately, where the automation stops. None of those three things is a judgment call about eligibility, and a platform that quietly makes that call for you is doing something riskier than it sounds.
Each of the three shows up differently depending on your stack. A team that lives in GitHub and closes everything through pull requests gets most of its activity identification from commit history and PR descriptions. The technical back-and-forth in a review thread is often where the real uncertainty gets spelled out, in the engineers' own words, months before anyone thinks about SR&ED. A team running most of its planning through Jira gets more value from ticket-level capture: the difference between a routine bug ticket and one where the acceptance criteria kept changing because nobody knew if the approach would work is a signal the software can pick up on automatically. Slack threads tend to matter most for the parts that never make it into a commit message at all, like the conversation where someone explains why the first three approaches failed before the fourth one worked. None of that is exotic. It's the ordinary record of how software actually gets built, captured instead of lost.
What it doesn't automate, and why that's the point
Eligibility is a judgment call, and judgment calls need a person who can be held accountable for being wrong. Software can surface that a sprint involved unusual technical difficulty. It can't reliably tell you whether that difficulty rises to the CRA's bar for "technological uncertainty" versus routine engineering, and it shouldn't be trusted to make that call unsupervised, because the cost of getting it wrong is a rejected claim months from now, not a bad autocomplete suggestion today.
That's why automation and expert review aren't competing approaches. They're two halves of the same system. The software does the capture: identifying eligible activity, logging technical detail as work happens, drafting the narrative shell. A qualified, independent SR&ED expert does the judgment: confirming the eligibility call, tightening the narrative language, and putting their name behind the claim before it goes to the CRA. Take either half away and what's left is worse. Automation alone gives you a fast, confident claim with nobody accountable for its accuracy. A human alone gives you the same slow, lossy process automation was built to fix.
What contemporaneous documentation buys you in a review
Documentation created while the work happened is exactly what a CRA reviewer is looking for, and it's the single biggest lever you control over how a review goes. The CRA's own numbers back this up: in FY2025-26, 90% of SR&ED claims were accepted as filed, with no modification and no extended review. Only 4% were denied outright. That split isn't random. A reviewer asking follow-up questions is testing whether the narrative traces back to something real, and a narrative built from actual commits and tickets answers those questions the same way it was written: specifically, and without having to guess.
Timing matters here too. Refundable claims accepted as filed are processed within 60 calendar days, and claims selected for review within 180 — the CRA hit those targets 95% and 92.5% of the time respectively in FY2025-26. A well-documented claim isn't just more likely to sail through; if it does get pulled for a closer look, contemporaneous evidence is what turns a review into a quick document exchange instead of a months-long back-and-forth built on reconstructed memory.
A realistic first quarter with SR&ED automation software
The first month is mostly plumbing, not insight. Wiring up the integrations takes hours, not weeks, but the software has nothing useful to say yet because it hasn't watched your team work through anything. That's normal, and it's worth setting the expectation internally before someone assumes the tool is broken because week one's report looks thin.
By the second month, patterns start showing up: clusters of commits and tickets around a specific technical problem, discussion threads where an approach got abandoned and another one tried. This is usually the point where a founder sees the first surprise: some project everyone assumed was "just engineering" turns out to have real, documented technological uncertainty behind it, because the software surfaced the messy middle of the work instead of just the final shipped feature. That surprise is common enough to be the norm rather than the exception; a lot of eligible SR&ED work gets built and shipped without anyone on the team framing it as R&D at the time.
By the third month, the software has enough continuous history that drafting a narrative stops being a from-scratch exercise and becomes an editing one. This is also the point where the expert review step starts to matter more, not less: more raw material means more judgment calls about what actually clears the eligibility bar, and a bigger draft narrative is not the same thing as a stronger claim. The software's job was to make sure nothing eligible got lost. The expert's job is deciding what belongs in the claim and defending that decision if the CRA asks.
Who should be skeptical of "AI does everything" pitches
If a platform's pitch is that AI alone will maximize your claim without anyone qualified checking the eligibility calls, be skeptical. That's exactly the setup that produces a claim nobody can defend when the CRA follows up. The tell is usually in the language: promises about how much bigger your refund will be, with no mention of who reviews the technical narrative before it's filed. Automation with no accountable expert behind it isn't a shortcut. It's the old, unsupported claim problem wearing a faster interface.
There's also an honest case for skipping automation software entirely. A two-person team running one clearly-scoped R&D project doesn't need a platform connected to their entire GitHub org. A founder who can describe the technical uncertainty in a paragraph and has the commit history to back it up is already most of the way there. Automation earns its keep at a certain scale: once you've got multiple projects running in parallel, several engineers whose work overlaps, and enough activity that nobody can hold the whole technical history in their head by March. Below that scale, it's solving a problem you don't have yet.
What to check before choosing SR&ED automation software
Ask what happens after the software finishes its part, because that answer tells you more than any feature list. Whether the platform actually helps comes down to a short list: does it integrate natively with the tools your team already uses, so evidence gets captured without extra process. Does it run continuously, not just as a year-end batch job. And most importantly, does a qualified, independent SR&ED expert review the output before anything reaches the CRA, or does the narrative go straight from the model to the filing.
This is where Glauq's own approach is worth naming plainly, since it's one paragraph and then back to the general case: we build the technical record continuously from your dev tools, and every claim is reviewed by a qualified independent SR&ED expert who stands behind it before it's filed — for a flat fee, so a bigger claim doesn't mean a bigger bill. That's the model we think the category should default to. Whichever platform you evaluate, the question to ask is the same one: who's accountable if the CRA pushes back, and can you name them.
Pricing structure is worth a specific look, separate from the feature checklist. Some platforms charge a percentage of the refund, which means the vendor's incentive is a bigger claim, not necessarily a more defensible one — those interests aren't automatically opposed, but they're not automatically aligned either. A flat fee removes that tension: the vendor gets paid the same amount whether your claim is $80,000 or $800,000, so there's no built-in incentive to inflate a narrative past what the evidence supports. Ask how a platform is priced before you ask what it can do, because the pricing model shapes the incentives behind every other feature on the list.
Frequently asked questions
Does SR&ED automation software replace a SR&ED consultant or accountant? No, and a platform that claims it does is a red flag. Automation replaces the manual, after-the-fact documentation work — chasing down commits, reconstructing timelines, drafting narrative language from memory. It doesn't replace the judgment call on eligibility or the accountability of a qualified expert reviewing the claim before it's filed. The two are complementary, not substitutes.
What tools does SR&ED automation software need to integrate with? The tools your engineering team already lives in: code repositories like GitHub, project trackers like Jira, and communication platforms like Slack. Native integrations matter because they let the platform capture technical evidence — commits, closed tickets, technical discussions — as it's created, instead of relying on someone to manually log it later.
Can the CRA tell if a SR&ED claim was drafted with AI? The CRA's eligibility test doesn't mention authorship — it asks whether the work involved genuine technological uncertainty investigated systematically. Our read: what a reviewer actually checks is whether the narrative matches real evidence, not who or what typed it. See our deeper look at what the CRA actually cares about for the full breakdown.
Does automated documentation reduce audit risk? It reduces the risk that a review goes badly, which is a more useful way to think about it. Contemporaneous documentation doesn't change whether the CRA selects your claim for a closer look, but it changes what happens once it's selected: a narrative built from real commits and tickets answers follow-up questions instead of triggering more of them.
Is SR&ED automation software worth it for a small, early-stage team? Not always. A small team with one clearly-scoped project and a founder who can describe the technical uncertainty accurately often doesn't need a platform connected to their whole toolchain. Automation earns its cost once there are multiple overlapping projects and enough engineering activity that no one person can reconstruct the full technical history from memory.
How long before SR&ED automation software actually helps with a claim? Expect the first month to be setup with little visible output, since the platform needs a working history to draw from before it can surface anything useful. Patterns worth acting on tend to show up by month two, and by month three there's usually enough continuous documentation that assembling a narrative is an editing task instead of a reconstruction project.
Want the full picture before picking a tool? Read our complete SR&ED guide for 2026, see what actually happens in a CRA review, or estimate your potential refund in under two minutes.
Curious whether continuous, expert-reviewed documentation would change your SR&ED claim? Check if you qualify or estimate your refund.