Get chosen
Get found & get chosen
You have to win the sale twice now.
A shopper still lands on your product page and decides. But more often there's a machine in front of them, assembling the shortlist from your product data before a human ever sees the page. We build for both, because a small lift in conversion is real revenue on traffic you already have.
- How it runs today
- A/B tests on the human funnel
- What breaks
- The agent builds the shortlist and never opens your page
- What you get
- A product record that wins the machine and the shopper
The shift
The shortlist is being built by a machine.
The old way
Winning the sale meant optimizing a person's path: test the page, tune the offer, watch the funnel. That's still real work, and it still pays.
The AI-era shift
But more of the shortlist is now assembled by a machine before the shopper ever arrives, and that machine doesn't look at your page. It reads your product data. If the record is thin, you're out before the human gets a say. So the work is winning twice: get picked by the machine, then close the shopper on a site you own.
Often the shortlist is settled before anyone sees your page.
What we actually do
The work, made concrete.
Agent-readiness audit
We start by reading your catalog the way an agent does: is the product data complete, do the reviews and ratings travel with it, do price and stock say the same thing everywhere. What comes back is a scored list of exactly where the machine loses you.
Product page & conversion rebuild
Then the human side. Pages that load fast, offers and pricing that make sense, and a reviews system that keeps compounding on its own. Unglamorous work, and it's what moves the number.
Right-sized personalization
Personalization tools are easy to buy and easy to waste. We add one only where it demonstrably moves the number, and we're just as happy to tell you to skip it.
Experimentation as a function
A testing operation your team runs after we step back, with honest rules about what counts as a win, so the improvements don't stop when we do.
Content that reads to both
Product and comparison pages written for the shopper and structured for the machine, so the same page does both jobs without your team writing everything twice.
Proof
The math that decides it.
The machine skips what it can't read.
Run almost any catalog with real SKU depth past an answer engine and the same gap shows up: a slice of the products carry complete, machine-readable data, and the rest rarely make the shortlist, no matter how well the page converts once a shopper lands. Closing that gap is conversion work on traffic you already paid for. That's the number this work exists to move.
The first step
The same read we run on AI visibility, pointed at your catalog: how complete your product data is, whether your reviews, prices, and stock read the way an engine needs them to, scored SKU by SKU. You see exactly where the machine loses you before you fix anything.
What we move
What we watch on conversion.
Benchmarks and targets, not guarantees. We baseline yours first.
How we work
How the engagement runs.
- 01
Diagnose
We baseline your numbers and map the operation end to end, so the work targets a real leak, not a hunch.
- 02
Prioritize
We rank the opportunities by dollars of impact and effort, and agree on what to do first.
- 03
Build
We build the real thing in production (for you, or alongside your team) against a measured baseline.
- 04
Prove
We hold the work against a holdout or benchmark, so the lift is proven, not asserted.
- 05
Hand over
Documentation, dashboards, and an accountable owner on your team, so the work keeps running without us.
Where this connects
See where the machine loses you.
The agent-readiness score is the honest place to start. We read your catalog the way the machines do and show you what they see, and what they skip. From there you'll know what's worth fixing first, and whether we're the ones to fix it.