Get found
If shoppers never see you, they cannot choose you.
See why AI picked someone else.
Beseam keeps watching AI discovery, your store, and your shoppers to find what is worth improving, makes the changes you approve, and shows you the impact of your changes.
- Can AI shopping agents read your store?
- See where you stand
- What to fix first
Being considered doesn’t mean being chosen.
Beseam follows the shopper from discovery to purchase to find where confidence drops, questions go unanswered, or the journey stops.
See why shoppers hesitate
Find the unanswered question that makes the shopper hesitate.
Help shoppers choose
Add the missing information that helps the shopper choose.
Nothing ships without you
Every customer-facing change waits at step 03 until you approve it.
And step 05 checks the same journey it started from (AI appearances, product visits, add to cart) so a change is measured against the state it changed.
Explore the platformSee what gets in the way of the choice.
Beseam looks at what the shopper did, checks product, search, and stock data, rules out weaker explanations, then turns the strongest finding into a specific change.
Example trace
Onsite discovery · schematic
01
What the shopper did
waterproof jacket+ for commuting
Same jackets returned. The shopper left without opening one.
02
Strongest evidence
Search works.Stock is available.
Product pages
Commuting language is missing.
Not in the titles, descriptions, or tags.
03
Proposed change
Add the commuting use case to the returned jacket product pages.
Searched
“waterproof jacket”
Then added
... “for commuting”
What happened next
Got the same jackets back, then left without opening one.
Ruled out
The refinement returned nothing at all.
It still returns the waterproof jackets.
Product pages
None of those jackets mention commuting.
Strongest evidence
Not in the titles, the descriptions, or the tags.
Ruled out
The jackets it returned are out of stock.
Almost all are in stock in the shopper’s market.
Proposed change
Add the commuting use case to the returned jacket product pages.
Beseam finds what to improve next.
Add the commuting use case to the Urban Shell product page.
Needs approvalQuickTop 5% of your booked sales
Explain how Urban Shell fits over everyday layers.
In motionQuickTop quarter of booked sales
Ask the commuter questions again after the product-page change.
MeasuringQuickNot measured
Illustrative example · not customer results.
ChangeSales shareEffortStatus
Add the commuting use case to the Urban Shell product page.
The shopper asked for a commuting jacket, and the product page never answers whether this one fits that use case.
Explain how Urban Shell fits over everyday layers.
The shopper opened the size guide, and fit over layers is still unanswered at the decision point.
Ask the commuter questions again after the product-page change.
Ask the same shopping question again before saying the change helped discovery.
Every change keeps what Beseam found, the owner, status, and what to check afterward together.
Illustrative example · not customer results.
One problem. All the evidence behind it.
A missed recommendation, a hesitant shopper, or a lost sale rarely lives in one tool. Beseam connects AI discovery, your store, shopper behavior, and outcomes so the next change starts with the full picture.
Signals
Fixed set
One shopper journey
Coverage
Shortlist visibility
Input: what AI discovery reads
chatgpt, google_ai_mode, gemini, perplexity, copilot, google_search
Point-in-time samples, dated and repeatable. Never a model’s hidden ranking logic.
Platform
What will this shopper choose?
What Beseam can do
+ more
Immersive product experiences, marketplaces, brand workflows, and other specialist capabilities as enabled.
It only matters if the outcome moves.
After a change, Beseam asks the same shopper questions again, and shows whether the answers now name your store.
Results
Example figures
Change verifiedMeasured window 28 days
Commuter answers naming Urban Shell9% → 23%+14 pts
Answers citing your own product page1 in 12 → 1 in 4+3 answers
Where you sit when you are named5th → 2nd+3 places
Illustrative example · not customer results.
AI Shopping Report
Products can be visible in one place and missed in another.
Beseam asks the same buying questions across AI assistants to find where your products appear, where they are missed, and how those results differ. In the latest run, 71% of brand appearances occurred on only one assistant.
See the report and method- 108(71%)
- One assistant only
- 28(18%)
- Two assistants
- 17(11%)
- Every assistant
CategoryWhere assistants disagreed mostBrands on one assistant
- Consumer tech“Best budget mechanical keyboard for typing?”9 of 10
- Food & drink“Best hot sauce gift set?”11 of 15
- Apparel & footwear“Best waterproof rain jacket for commuting?”7 of 10
Start free.Pay when it proves its value.
Beseam watches, prepares the change, applies it once you approve, and shows you what moved, one subscription instead of a tool plus an agency. Try for free to see the impact before you pay anything.
Start freeFAQ
FAQ
What's the difference between the free scan and Beseam?
The free scan is a one-time look at your public storefront. Beseam keeps working after that: it connects the signals that matter, finds what deserves attention, prepares changes, applies supported changes after approval, and checks what happened afterward.
What does Beseam actually watch?
Depending on what you connect, Beseam can use AI and search discovery, catalog and storefront data, shopper behavior, fit, experiments, reliability, campaigns, conversion, orders, and revenue. You do not need every source; Beseam uses the evidence relevant to the problem being investigated.
How does Beseam decide what deserves attention first?
Beseam brings the evidence for a problem into one place, separates observed facts from possible explanations, and weighs things such as evidence strength, affected products or journeys, business context, effort, and risk. The reason a change is prioritized stays attached to the work.
What can Beseam actually change?
Where the connected system supports it, Beseam can prepare or apply changes to product data, content, merchandising, onsite search, recommendations and personalization, fit and sizing experiences, experiments, creative assets, campaigns, and other editable parts of the buying journey. Customer-facing changes follow your approval rules.
Does Beseam replace my analytics, personalization, or monitoring tools?
Not necessarily. Existing tools can remain specialist sources of evidence. Beseam's job is to connect useful signals around the same product, shopper question, journey, or business problem, turn the strongest findings into work, and keep the result attached. Exact integrations depend on your setup.
What needs approval before it goes live?
You set the rules. Beseam can keep checking, gathering evidence, and preparing work without waiting for you, but customer-facing changes that require approval do not go live until they have it. Changes that need brand judgment stop for review.
How does Beseam know whether a change worked?
Beseam checks the signal that exposed the problem again. That might be the same AI shopping question, a storefront or reliability check, shopper behavior, an experiment, campaign performance, conversion, orders, or revenue. The before-and-after stays with the change, and Beseam keeps observed movement separate from causation the evidence cannot prove.
What do I need to connect to get started?
Start with your domain. For ongoing work, connect the store first, then add analytics, behavior, search, campaign, customer, or revenue data only when it helps explain a problem, make an approved change, or measure what happened.