Robynn AI
Websites that improve and heal with self-learning
573 followers
Websites that improve and heal with self-learning
573 followers
Your website starts decaying the day it ships. Broken links, stale pages, slipping rankings, invisible to ChatGPT. Robynn connects to your live site — no rebuild, no migration. It audits every page against your brand, ICP, and competitors, then proposes evidence-backed fixes. Point at any element, describe a change in plain English. Agents stage it, you approve, Robynn publishes and measures what moved in GA. Wins get reinforced. Regressions roll back. First audit is free.
@madhukar_kumar1 How quickly do customers usually notice measureable improvements after deploment?
Robynn AI
@hana_salazars Usually 7 days to know if/what direction has data moved before next iteration.
The rollback is the part that got me. Most of these stop at telling you what's wrong, which just hands you another backlog. Closing the loop is the hard half - and once reverting is automatic you don't have to be right about any single change, just reversible. That's the whole trick.
One thing I'd want: a holdout. I run a metrics dashboard for my own stuff and got burned by this recently - download numbers are weekday-driven, weekends run 40-60% below midweek, so every Monday looks like a win and every Saturday looks like a regression. Nothing to do with anything I shipped. Without a control slice you end up reinforcing the calendar.
Congrats on the launch.
Robynn AI
@ryan_davis23 As a former CMO I looked at a lot of dashboards that were un-actionable. My goal is to connect the analytics to action in a tight feedback loop at the web page level.
the email digest with a default-publish timeout is a fair middle ground honestly, at least it forces someone to actively ignore it rather than never seeing it at all. curious if the digest highlights which changes are lower-risk copy tweaks vs actual layout/pricing changes, since I'd guess most of the rubber-stamping risk comes from lumping trivial and consequential changes into one daily email
Robynn AI
@omri_ben_shoham1 Not right now but we will add this. Thank you for the feedback 🙏
"Wins get reinforced, regressions roll back" implies Robynn is measuring post-publish outcomes and acting on them automatically. Curious what the measurement window looks like before something is classified as a win or regression, because SEO and conversion signals can take weeks to stabilize, and rolling back too quickly based on short-term noise could undo changes that would have worked given more time.
Nice one, congrats on the launch. The approval step is the part that stands out for me. A lot of website AI tools jump straight from suggestion to publishing, which is probably too much trust for most teams. How does Robynn decide whether a change actually worked? GA can show movement, but traffic and conversions are influenced by campaigns, seasonality, pricing, and plenty of other things. How does the platform separate a real website improvement from everything else happening at the same time? That measurement layer feels like the hardest part, but also the most valuable.
he part that caught me is the rollback on regressions. I've used tools that let you publish changes but never actually track if they helped or hurt. Measuring against GA and reversing bad calls is the real differentiator here.
Robynn AI
@kimberly_west thanks. The platform is built on a very opinionated marketing practices (based on my former CMO learnings). In marketing agents should never do 100% of the work - only 90% of grunt work so marketers add the true taste and judgement. Second, strong feedback loops that lead to better learnings and reflections over time
"Wins get reinforced. Regressions roll back." is the sentence I'd stress-test, from someone who ships an AI that takes real-world actions and has been burned by exactly this loop.
Two failure modes it doesn't cover, and neither shows up as a regression:
The first is that rollback assumes the damage is visible in the metric you're watching. The changes that actually hurt are the ones GA scores as neutral or positive. A page that converts better because it overpromises is a win in the data and a support problem six weeks later. We shipped an AI summary that told a user their call had gone well when nobody had picked up — every downstream metric looked fine, because the failure lands on someone who isn't in your funnel. If reinforcement runs on the metric alone, the loop will happily climb toward things you'd reject on sight.
The second is approval decay. "You approve" holds beautifully for the first twenty changes. Then it becomes a reflex — people approve to clear the queue, not because they read it. Now you have a human-in-the-loop on paper and an unsupervised agent in practice, and the audit trail says a human signed off, which is worse than no record at all because it looks like oversight. Nobody decides to lower the bar; it just drifts.
What worked for us was gating on reach rather than on reversibility. Irreversibility is a bad axis because it over-fires on things nobody cares about and under-fires on things that are technically undoable and genuinely ruinous. Mapping to yours: a copy tweak on a low-traffic page and a change to your pricing page are both "reversible," and they should absolutely not sit in the same approval queue. Volume of low-stakes changes is what erodes attention for the high-stakes one.
Concrete version, if useful: let auto-publish run unattended where reach is small, and reserve the human for a small number of high-reach changes — so approval stays expensive enough that people actually read it.
Curious where you've landed on this: is the approval per-change, or can users set a reach threshold above which it always stops and asks?