One model I've seen is moving the review stage to the designs, not the code itself.
I.e. have a `planning/designs/unbuilt/...` folder that contains markdown descriptions of features/changes that would have gotten a PR. Now do the review at the design level.
in 2020 we lived through the extreme left's remedial lesson in public safety. Now we're living through the MAGA right's remedial lesson in trade policy.
(Most MAGA right know this, don't care, see tariffs as a hammer. But they are hitting all of us)
I see the value here. The problem isn't just the search; it's the trust. The biggest hurdle for Zenode won't be the tech, but convincing an engineer that your AI's summary of a footnote is accurate enough to risk a $10,000 board spin. That's a high bar.
I'd argue the core value isn't just a better search or a faster reader. It's about providing a verified, reliable source of truth. This brings up a key tension: you say the AI isn't yet at your co-founder's level of accuracy, but is that precisely the level of confidence required to replace an engineer's manual check? How do you close that gap? You've got the data, but the trust factor is a different threshold?
I.e. maybe you've built the tool to make the problem faster, but the real win would be a tool that makes the problem safer? The killer feature might not be more speed, but rather a confidence score on every AI-generated fact, with a clear path to the source document so an engineer can verify it. It’s not about avoiding the document entirely; it’s about having a better starting point and knowing exactly what to double-check.