Your customers find the bugs before you do. Tower changes that — every defect named, screenshotted, and written as a ticket before anyone bounces.
0 integration needed every run makes the system better 97% of findings actionable
Three-layer shells, tested at 3,400 m. Free returns for sixty days.
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Alpine Shell Jacket
The banner as a visitor meets it. Five defects, none of them announced.
Agents take it apart, region by region. Each one measured, each one captured.
The banner rebuilt from its own findings. Nothing here was guessed.
Analytics says a page under-performs. Tower says why — with the screen to prove it.
Agents browse your store on schedule — search, filters, checkout, translations — and surface issues before they reach real users. Each finding arrives with its screenshot and byte-exact quotes from the page. What serves as proof is never generated by a model.
Tower reads your store in production the same way your customers do — public pages only, no access, no snippet, no deploy. Your first audit runs tonight, not next quarter.
Thousands of products, multiple languages, external supplier feeds. Nobody on your team re-reads 400 product pages — Tower does, every run, and only escalates what costs conversions.
Not a PDF of recommendations nobody opens. Findings, split into Broken vs Decisions, each ready to become a line in the sprint.
Queries your personas actually type, run against your live index. When the bestselling shell ranks 14th behind a headlamp, you get the screen — not a hunch.
“Waterproof jacket” returns headlamps first — the bestselling shell is ranked 14th.
capture saved → ticket TW-115The agent switches locale like a real shopper. A French title sitting on your English market page never survives a run.
Down fill · −18 °C comfort · 890 g · ripstop shell
Add to cartFrench title on the English market page — the persona switched locale; the catalog didn’t.
capture saved → ticket TW-114Struck prices lower than the sale price, currency mismatches, feed drift from external suppliers — read off the page, not the feed.
32 L · 1.2 kg · hip belt · rain cover included
Add to cartStruck price lower than the sale price — the discount reads backwards on a bestseller.
capture saved → ticket TW-113The product that pays your margin, sitting on page 3 of the wrong collection — named, with the path a shopper would have to survive to find it.
The bestselling shell sits on page 3 of the wrong collection — behind boots and headlamps, where its buyers never scroll.
capture saved → ticket TW-116We train a model specialised in e-commerce CRO, with its own evaluation of success — built on hundreds of stores and conversion research. A finding doesn’t ship until the loop says so.
A model specialised in conversion defects, with its own evaluation of success — built on hundreds of e-commerce stores and CRO research, not general web data.
Each finding runs the evaluation loop: challenged, scored, shipped or killed. That filter is how we reach 97% actionable findings per run.
The agent learns permanently. Your verdicts feed back into training, the model narrows on what converts, and the next run outperforms the last.
Small, dated, reversible. You judge before we ever talk integration — and every verdict you give trains the watch.
No integration, no access, no contract. You get the findings with their captures — the whole run is on us, because proof beats pitch.
Actionable, false, or noise — your verdict, one keystroke each. It proves the value better than any pitch, this page included.
Tower does. First audit free — you judge every finding yourself.
