Engineering leaders can't answer 'was AI involved?' after a production incident. That's a real problem worth building for.
There's a Reddit thread that's been living rent-free in my head. Amazon's services go down, the culprit is an AI coding bot, and the top comment is just: 'how do you even audit this?' 1,537 upvotes. 171 comments. Every reply is some variation of 'we have no idea which commits came from AI.'
That thread is your market research. That's your customer telling you exactly what they need.
Companies are mandating AI adoption. '80% of engineers using Copilot by Q3' is a real target at real companies. And simultaneously, those same companies are getting asked by their boards: 'what's our risk exposure from AI-generated code?' The CTO has no answer. There's no tool that connects these dots.
LinearB and Jellyfish track developer productivity. GitHub's Copilot dashboard tracks seat usage. Nobody is asking 'did AI-generated code touch this service before it went down?' That's the gap.
Serioulsy, don't write code yet. This one actually has teeth as a validation problem.
Here's the test: Post a Loom walkthrough (5 minutes, Figma mockup, no code) to Rands Leadership Slack and CTO Craft. The post title should be a question, not a pitch: 'How do you audit AI-generated code when something breaks?' Then DM 20 VPs of Engineering at Series B-D companies. LinkedIn makes this easy if you filter by company size and look for people who've posted about Copilot or AI adoption recently.
Your DM: 'We're building an audit trail for AI-generated code incidents. Would a 15-min call be worth it if it helped you answer your next board question about AI risk? We'll do a free manual analysis of your last 90 days of commits either way.'
Your success bar: 5 VPs agree to a 30-minute discovery call, and 2 of them say they'd pay $500/month for what you described. If you can't hit that in 3 weeks, the idea needs rethinking before you spend 5 weeks building OAuth integrations.
Three things, in order of importance:
AI commit fingerprinting. This is where you'll spend most of your time and where you'll hit your first wall. GitHub Copilot Enterprise exposes metadata you can actually use. Cursor doesn't leave a trail. Tabnine doesn't either. For MVP, be honest about this: build Copilot-native attribution first, display confidence levels on every attribution, and let users flag false positives. Don't pretend your detection is accurate when it isn't. One high-profile wrong accusation ('you said AI caused this but it didn't') and you're done.
Incident correlation engine. Connect GitHub commits to PagerDuty or Jira incidents within a configurable time window. A commit tagged as AI-generated, merged 6 hours before an incident, from a high-velocity team - that's a signal worth surfacing. It's not proof. Label it as a signal.
The PDF report. This sounds boring but it's your retention hook. A monthly PDF that a CTO can drop into their board deck becomes part of their workflow. Removing it means explaining to the board why they stopped measuring AI risk. That's your moat.
Next.js frontend, Supabase for auth and data, Stripe for billing. GitHub and GitLab OAuth for repo access, PagerDuty and Jira OAuth for incident data. PDF generation with React-PDF or Puppeteer. You can wire all of this together in 5-7 weeks solo if you're using Cursor or Lovable to scaffold the boilerplate.
The OAuth integrations are genuinely the hard part, not the UI. Budget two weeks just for getting GitHub, GitLab, and PagerDuty connected and handling token refresh edge cases. Bolt is decent for scaffolding the dashboard views quickly, but the API integration work is where you'll actually earn your stripes.
For the commit classifier, use an LLM to analyze diff patterns for stylistic AI fingerprints. Fine-tune it over time as customers correct false positives. That correction data is your moat - every customer who flags a wrong attribution is making the model better for everyone.
$499/month for up to 100 devs. $999/month for 101-300. $1,999/month for 301-500. Annual at 20% off. The math: 8 customers at the $999 tier covers $8K/month in founder salary and infrastructure. At 88% gross margin with Supabase and API costs as your only real variable expense, you're looking at a healthy unit economics story once you're past the first 10 customers.
Target Series B-D companies specifically. VP of Engineering at a 150-person company has $500-2K/month discretionary budget that doesn't require CFO approval. That's your entry point. Stay away from enterprise until you're at $50K MRR and can survive a 6-month sales cycle.
LTV at mid-market is around $14K over 24 months. Community-led CAC (Rands Slack, CTO Craft) is essentially zero. The LTV:CAC math works embarrassingly well if you don't screw up the go-to-market.
I want to be direct about the hard stuff, because this idea has genuine fatal flaws that aren't just execution risk.
First: AI fingerprinting accuracy. If Cursor and Tabnine don't expose metadata, and they currently don't, then your risk scores for those tools are essentially guess work. And Cursor is growing fast. If your product mislabels a human commit as AI-generated right before a high-profile incident, you're not just losing a customer - you're getting posted about on the same subreddits that gave you your initial validation.
Second: procurement velocity. Getting OAuth access to production GitHub repos AND PagerDuty AND Jira simultaneously triggers security review at most companies above 100 engineers. That's a 4-8 week blocker. Your trial-to-paid conversion will be slower than you expect. Build your runway assumptions around this.
Third: the regulatory tailwind could become a moat destroyer. If the EU AI Act eventually requires GitHub and Cursor to provide standardized usage logs directly to enterprise customers, the aggregation problem you're solving gets commoditized by vendor compliance. You'd need to shift your value to the correlation layer and the historical data, not the aggregation itself.
None of these are reasons not to build it. They're reasons to be clear-eyed about what you're betting on and to move fast enough to build switching costs before the incumbents wake up.
Post in Rands Leadership Slack today. Not a pitch - a question about the problem. See who responds. DM them. Do 5 discovery calls before you write a single line of code. If 2 of those 5 people ask you when they can pay you, you have your answer.
The aha moment for this product, when it clicks for a customer, is the first time they see a PagerDuty incident flagged as 'likely AI-generated commit involved' within 48 hours of connecting their accounts. Everything before that is just promises. Your job in the first 5 weeks is to get someone to that moment as fast as possible.