Onsite search
The refinement returned nothing at all.
Ruled out
It still returns the waterproof jackets.
Get more shoppers to choose you.
Beseam watches AI discovery, your store, shopper behavior, and revenue to find what is worth improving next.
Free. No login required.
- Where shoppers may lose you
- What to improve first
- Evidence behind every finding
Product
- Live with a real merchant pilot
- Every change approved, with rollback
Research
See the report & method15
shopper questions
3
AI engines
45
completed answers
71%
of appearances unique to one engine
See Beseam work
One shopper question, followed end to end.
Follow a commuting-jacket question from the shopper signal to the explanations Beseam checks. The strongest evidence then becomes a supported fix, and the same path is measured again after the change ships.
Example trace
Onsite discovery · schematic
01
Shopper signal
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
What happens next
Propose fixYou approveRemeasure
Searched
“waterproof jacket”
Then added
... “for commuting”
What happened next
Got the same jackets back, then left without opening one.
Product pages
None of those jackets mention commuting.
Observed
Not in the titles, the descriptions, or the tags.
Availability
The jackets it returned are out of stock.
Ruled out
Almost all are in stock in the shopper’s market.
What happens next
The strongest evidence becomes a proposed fix. You approve the change; Beseam applies it and remeasures what changed.
From being considered to being chosen
Getting considered is only the start.
Beseam follows the same shopper decision end to end: see whether you are considered, find what blocks the choice, add useful context, then remeasure the outcome and learn.
Get considered
AI · search · feeds
You can’t be chosen if you’re never considered.
Understand the decision
PDP · search · shopper behavior
See the unanswered question getting in the way of the decision.
Help shoppers choose
Adaptive discovery · personalization
Add the missing context that helps the shopper choose.
Measure & learn
Conversion · orders · revenue
Remeasure the same journey, learn what worked, and improve again.
Continuous loop
- Find the opportunity
- Propose the fix
- You approve
- Beseam applies it
- Measure and repeat
From evidence to action
Beseam finds what to improve next.
The strongest evidence becomes a supported change with its proof plan still attached.
Approval gate
Nothing customer-facing changes until you approve it.
Merchant view
Evidence → approval → verification
Growth plan
3 opportunities
Proposed fixPriorityEffortNext
Add the commuting use case to the Urban Shell product page.
HighQuickReviewExplain how Urban Shell fits over everyday layers.
MediumQuickReviewRecheck commuter queries after the product-page change.
LowQuickQueueGrowth plan
Illustrative · 3 opportunities
Opportunity and prepared fix
Priority
Effort
Impact
Next step
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.
Recheck commuter queries after the product-page change.
The same buying question should be observed again before claiming that the change helped discovery.
Every opportunity keeps its evidence, owner, approval state, commercial impact tier, and proof plan attached.
The outcome
It only matters if the outcome moves.
Beseam reruns the original signal and keeps the before, after, and commercial result attached to the approved change.
Results
Example figures
Verified fixes3Measured window 28 days
Commuter answers naming Urban Shell9% → 23%+2.6×
Store search → Urban Shell page46% → 57%+24%
Urban Shell page → add to cart2.4% → 2.9%+21%
Example figures, not a customer's. The layout is the product, and the first numbers published here will be a real store's.
AI Shopping Report
Where shoppers ask determines who gets considered.
We ask the same buying questions across AI assistants and record what comes back. In the latest run, 71% of brand appearances occurred on only one assistant.
See the report and methodConsideration is fragmented. Beseam follows what happens next.
- 108(71%)
- One assistant only
- 28(18%)
- Two assistants
- 17(11%)
- Every assistant
- Consumer tech“Best budget mechanical keyboard for typing?”9/10
- Food & drink“Best hot sauce gift set?”11/15
- Apparel & footwear“Best waterproof rain jacket for commuting?”7/10
- Supplements“Best protein powder for women over 40?”8/12
- Supplements“What is the best creatine supplement to buy?”7/11
- Consumer tech“What is a good portable monitor for travel?”5/8
30 days free
Start free. Pay when Beseam proves its value.
Use Beseam free for 30 days. See what it finds, review the changes it proposes, and decide if it has earned a place in your stack before you pay.
01 · Free scan
See where shoppers may lose you
01AI discovery
02Product evidence
03Store experience
02 · Beseam Growth
Continuous improvement with your approval
- Find
- Prepare
- Approve
- Repeat
FAQ
What do I need to connect to get started?
Start with your domain. Add commerce, analytics, search, behavior, or customer data only when it improves the evidence or gives Beseam permission to support a change.
How does Beseam decide what to fix first?
Beseam keeps shopper signals and relevant evidence together, separates observed facts from possible explanations, and prioritizes the opportunities most likely to matter commercially. The reason for every decision stays attached.
What can Beseam actually change?
With the right access, Beseam can support changes to product data, content, merchandising, onsite search, recommendations, and other editable store experiences. Execution depends on the connected system and the approval rules you choose.
What needs approval before it goes live?
You choose the rules. Today, Beseam prepares supported customer-facing changes and executes them after the required approval for your store. Judgment-heavy or unsupported changes stop for review. Where rollback is supported, the previous state and audit trail are retained.
How does Beseam measure whether a change helped?
Beseam rechecks the original shopper signal and measures the relevant behavior, conversion, order, or revenue signals afterward. The before-and-after stays attached to the action, while correlation and causation remain clearly separated.