Short answers. No promises we can't measure.

Tower sends an AI agent through your store's public pages, behaving like your buyers — home, categories, search results, product pages, each rendered exactly as your store serves them. Everything that costs you conversions gets named, screenshotted, and written up with a severity and an estimated conversion impact, before a real customer bounces on it.

Nothing to install, nothing to integrate. The first audit is free — see what they find on your store.

Analytics and session replay show where shoppers drop, not why; crawlers check pages, not journeys. Tower's agents walk the journey themselves — page by page, as your store actually serves it — and come back with proof: a screenshot and quotes extracted byte-exact from the page. What serves as proof is never generated by a model.

And where others promise magic numbers, we publish our measurements — accuracy, coverage, and misses. Put one Tower report next to your current tool's and compare.

A set of findings split into Broken — things that are objectively wrong — and Decisions — choices that plausibly cost you conversions. Each comes with its screenshot, a written observation, a severity and an estimated conversion impact, so you sort by what costs the most first — a prioritized backlog, not another audit report.

The fastest way to see one is on your own store: generate yours.

The UX audit is live: the Discovery agent walks the buying journey — home, categories, search, product pages — and flags what costs conversions along the way, with an evaluation judge screening every finding before you see it. Every Tower agent must pass a measured evaluation gate before it audits client stores; Discovery has passed it, and the next agents ship when they clear the same gate — not before.

If the defect you care about lives in a journey, the UX audit will walk it.

Three mechanisms. Proof is captured, not generated: every finding ships with its screenshot and byte-exact quotes. You judge every finding yourself — actionable, false, or noise, one keystroke each. And Tower never marks a defect "resolved" without proof.

In the latest published pilot, 11 out of 11 findings were judged true by the store's owner (a small sample, and we say so); 91% factual accuracy in the earlier phase. Judge it on your own store rather than on our pilot.

We measured it instead of guessing. By capture-recapture on a real store, one audit finds about 65% of the defects present; two audits reach about 90%. We publish this instead of pretending one pass sees everything.

That is why Tower runs on a schedule rather than as a one-shot — and findings are deduplicated from one run to the next, so a weekly audit surfaces new defects instead of refilling your backlog with duplicates. Start with one and see what the first 65% looks like.

Here is the record, not a promise: 0 real orders placed across all real runs — including two where an agent reached the payment tunnel and was stopped. The stop is a hard guard in the code on the checkout URL, not a prompt instruction.

Traffic is throttled to stay polite to your infrastructure. Run one audit and watch the traffic yourself.

Yes, and that is deliberate: the user-agent identifies itself instead of masquerading as a human session. Exclude it the way you exclude any declared bot, and your traffic and conversion numbers stay clean.

The exact string shows up in your server logs from the first run — filter it once, and you are done.

In Frankfurt — eu-central-1, pinned explicitly, not a "Europe" pool that could drift jurisdiction. Workspaces are isolated by Postgres RLS, and the isolation is tested adversarially: a bench replaces each policy with a permissive version and must break. Audit machines are ephemeral, destroyed after every run.

We keep little because we see little: public pages only — never your admin, customer or order data. Screenshots live 90 days in a private bucket behind short-lived signed URLs, then are deleted.

No. Agents read your public pages the way any shopper does — no snippet, no code, no access to your admin, no ticket to your infra team.

You provide two things: your store's URL, and your personas — upload a file or type them in — so the agents browse as those shoppers would. Start here.

There is nothing to set up, so your first audit can run tonight. When the run finishes, you get the findings with their captures — not a meeting, not an onboarding call — and judging each one takes a keystroke.

Request the audit now and read the findings over coffee tomorrow.

Any storefront reachable on the public web. Tower audits your pages as they are actually served — full server render, your real markup — so your stack makes no difference: Shopify, Magento, headless, custom. Locales are exercised too: runs render your store in your market's language and currency, and a French title on your English store is exactly the kind of defect a run flags.

Unusual store? The answer is one audit away — run one.

Ask for your free first audit on the Try Tower page — no contract, no integration. You get the findings with their captures; the rest is your call.

We are in pilot phase: pricing is discussed after you have judged the findings, not before, and there is no lock-in to get out of. If the findings don't hold up on your store, you have lost nothing but the time it took to read them.