Representative Scenarios, Not Case Studies
Client engagements are confidential, so there are no names, logos or testimonials here — just three composite scenarios showing how the dual-vetting philosophy plays out in real kinds of work.
A Note on What Follows
Our client work is confidential, so we cannot publish real names, real numbers or real quotes here. What follows instead are three representative scenarios — composites built from the kind of situation this marketplace exists for, not a record of any specific engagement. Any figure, timeframe or outcome mentioned is explicitly illustrative. If you want to know what we would actually do with your project, the fastest way to find out is to send it to us.
Shipping an MVP Faster, Without Skipping the Security Call
A small startup team is racing toward a launch date and hires a full-stack developer on a project basis to build out several features in parallel — an admin panel, a file-upload flow, an authentication layer. AI-assisted scaffolding does most of the boilerplate quickly: routes, forms, basic CRUD. That part goes roughly as fast as the sales pitch for AI coding tools promises.
The part that matters more happens on the upload flow. The AI assistant suggests a fast, convenient way to generate and validate file access links that would technically work — and would also leave a predictable gap an attacker could exploit once traffic grew. This is exactly the judgement call the fundamentals evaluation exists to test: recognising that a suggestion is convenient rather than correct, and taking the slightly slower, properly signed approach instead. The MVP still ships close to on schedule — illustratively, something like a few weeks faster than building every layer by hand — but the speed came from the boilerplate, not from skipping the one decision that needed a human to actually think about it.
Catching the Bug an AI Assistant Wrote With Total Confidence
An existing engineering team brings in an AI-fluent developer part-time to help clear a backlog of billing and reporting work. A few weeks in, a teammate opens a pull request for a proration calculation, largely AI-generated and passing its own tests. It looks finished: clean formatting, sensible variable names, a confident description.
Reviewing it, the augmented developer notices the calculation handles a mid-month plan change correctly in every case the tests cover and silently mishandles the one where a customer changes plans twice in the same billing cycle — a case nobody had written a test for, because the assistant that wrote the tests did not think of it either. This is the AI-assisted work review from our vetting process, showing up exactly where it is supposed to: not in a lab exercise, but in a real pull request that would otherwise have shipped a billing error. The fix is small once someone catches it. Catching it is the part that is not automatic.
A Team That Changed How It Reviews AI Output
No developer gets hired in this scenario at all — a client with their own in-house team wants those developers to get better at working with the AI tools they already use daily. Reviews had drifted toward approving anything that passed CI and read cleanly, because that had become the fastest way to keep pull requests moving.
The training engagement works through our AI code review standards directly against the team’s own recent pull requests: which ones had a hallucinated API call that happened to compile, which ones had an edge case quietly dropped. Afterward, reviewers on the team start asking a specific question before approving AI-assisted changes — have I checked the case nobody wrote a test for — instead of treating a green build as the finish line. That is a change in habit, not a number we can publish, and it is the actual goal of training: the same critical judgement our vetting looks for in candidates, taught directly to developers a client already trusts.
Want to See What This Looks Like on Your Project?
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