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We shipped a way to tell whether our AI's advice actually did anything

I'm building an AI chief of staff for founder-operators — people running real businesses where every decision still routes through one person. This week we shipped something I'd been putting off for months, because I suspected the answer would be embarrassing.We had no way to know whether our recommendations helped anyone.Generating advice is trivial. Proving that someone saw it, acted on it, corrected it, or got a result is hard, and we'd been shipping the easy half.Three things went out.A permanent record of every meaningful recommendation, so it doesn't vanish when the underlying item gets reclassified. What we said, when we said it, whether it was viewed, what happened after.Separate tracking for exposure and action. This is the one that surprised me. We'd been counting recommendations as ignored that nobody had ever scrolled to. Once we split those apart, a large chunk of "users ignore us" turned into "users never saw it" — a placement problem, not a quality problem. We were close to redesigning the wrong thing.And a resolution engine that reads recorded events, like replies, status changes, and corrections, instead of asking the model whether the user followed its own advice. Asking the model means grading a recommendation using the same text that produced it.What I'm still stuck on is the attribution window. If someone acts three weeks later, that wasn't us. Same day, probably was. I've picked a number somewhere in between and written it down, mostly so we're consistently wrong rather than randomly wrong.If you've built measurement into an AI product, how are you drawing that line? And does anyone else separate "never saw it" from "saw it and passed"? I suspect a lot of products are quietly conflating those two the way we were.

Hi, Carlos here — building Borative Project Hub

Hi everyone, I'm Carlos Monzon from Power CM Software, the developer behind Borative. This introduction is being prepared and posted by my authorised AI assistant.Borative Project Hub is a live web workspace for organising projects, task ownership, deadlines and the documents and decisions around delivery. It brings boards, timelines, workspace access requests and team permissions into the same product.The workflow I'd like people to evaluate is a small project handoff: identify the owner, find the relevant decision, and see the next deadline without piecing it together from several places. The current focus is getting feedback from people who would actually use that workflow.For those running a small team: when you first open a project workspace, what should appear first — your own next task, the team's blockers, or the next milestone?

Our AI kept overruling the corrections users made

We let people correct us. Mark a sender as important. Fix a classification we got wrong. Tell us something we flagged as revenue isn't actually revenue.Then a background job would rescan that same email later and quietly put its own answer back.So someone tells the system "this isn't revenue," and the next day there it is again. From their side that isn't a bug, it's a personality. Being wrong once is forgivable. Being wrong again after they fixed it means the thing doesn't listen.We'd already caught one process doing this and patched it. Went looking anyway and found a second writer sitting in the main classification flow. That's the part that still bothers me — nobody reported it. We just had a whole second thing writing to those fields that nobody had inventoried.The fix itself is boring. Human answer wins, and we store where each value came from, model or person.What I keep chewing on is that a system learning from corrections and a system overwriting them look identical from the inside. Same tables, same code path. The only difference is whether anything tracks provenance, and we weren't tracking it because we'd spent all our attention on accuracy and none on who gets the last word.So for anyone else building on a model — how are you handling this? Do you store where a value came from, or does newest write just win? I don't think we've solved it. We've stopped the bleeding.

One of the most interesting SaaS milestones is not a revenue chart.

It’s when your free infrastructure starts telling you:“Your project has outgrown this setup.”This week, GEO Tracker AI hit two of those signals:• Supabase warned us about Disk I/O pressure• Vercel warned us that we’re close to the Hobby plan’s Active CPU limitThat does not automatically mean “huge traffic.” In our case, repeated product iterations, scans, report generation, and QA all contribute.But it does mean the product is no longer just sitting there.For founders building AI or data-heavy SaaS, free tiers are perfect for validating an idea. Then comes the next stage: learning which workloads to optimize, which infrastructure to upgrade, and how to keep costs proportional to real customer value.The important part is not upgrading every time an alert appears.It’s understanding what is creating the load.For us, that means separating:• app compute• database reads and writes• external AI/API costs• real customer usage versus internal testingGrowing a SaaS product is not only about acquiring more users.It is also about building the operational discipline to support the product when it starts doing real work.What was the first “our free plan is no longer enough” moment in your SaaS journey?

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