Candidate-set snapshot
Natural buyer questions, captured AI answers, the observed shortlist, an initial failure classification and prioritized blockers.
Product candidate-set diagnostic
Find out where one product falls out of the AI shopping decision.
For one Shopify product that deserves to be understood correctly. We test it against natural buyer questions, diagnose why it enters or misses the right shortlist, and identify the smallest useful fix.
Test one productOne SKU. Two accountable stages.
Start with captured evidence. Pay for a fix only after the snapshot shows a useful, addressable problem.
Focused fix + retest
The source-ready Focused Fix Brief is delivered within two additional business days. Its corresponding live retest is included once the approved change is live and available to the AI shopping systems being tested.
Walkthrough + revision
One live explanation and one in-scope revision. Implement it yourself or ask us to quote implementation separately.
Test. Diagnose. Fix. Retest.
The evidence shows whether the opportunity sits in technical eligibility, product positioning, category association or competing sources—and whether a source change is warranted.
Observe the candidate-set outcome
Is the SKU absent, placed in the wrong category, shortlisted but rejected, contradicted by its sources or correctly recommended?
Trace the failure point
We separate product-source problems from Shopify data eligibility and the surrounding authority environment.
Change the smallest useful source
Recommendations target the evidenced break—not a generic bundle of schema, crawler or copy changes.
Measure the approved change
We repeat the same buyer questions and market conditions so the before-and-after result can be compared directly.
One observed outcome. Three possible cause layers.
Shopify increasingly provides the technical foundation. MC Lab checks that layer, then focuses the diagnosis on what actually explains the product's result.
Facts and category bridge
Buyer fit, exact attributes, deciding proof, comparisons and trade-offs must form one consistent product story.
Shopify commerce data
We check publicly observable taxonomy, attributes, variants, price, availability, identifiers and structured product signals.
Surrounding sources
Reviews, editorial coverage and community discussion can support, contradict or redirect how the product is represented.
If the technical foundation is already sound, we say so. Robots.txt, schema and product feeds are checks—not automatic paid fixes.
What the final artifact contains
Candidate-set matrix
Buyer questions, AI systems, target-product outcomes, competing products and original test conditions.
Discovery failure type
A precise classification of where the product left the path from buyer need to shortlist and purchase decision.
Category bridge map
Buyer need → product category → deciding attributes → supporting proof → priority SKU.
Competitor evidence gap
The evidence behind products that entered the answer compared with what your product currently makes available.
PDP decision brief
Fit, difference, proof, limitations, price, availability, delivery and returns reviewed as a stand-alone decision page.
Change-and-retest plan
Prioritized actions tied to the diagnosed problem, implementation location, controllability and retest condition.
Evidence your team can inspect and use.
Product-specific testing, source comparison and deeper AI analysis lead to a captured evidence set, a clear diagnosis and source-ready next actions.
Frequently asked questions
Is this right for my store?
It is designed primarily for founder-led independent Shopify brands—from solo makers to compact teams—with one priority SKU, a public product page and a differentiated product that benefits from explanation, comparison or clearer category fit. We confirm fit before payment.
How does payment work?
For this professional service, the first $99 covers the candidate-set snapshot and the optional $100 stage covers the Focused Fix + Retest. We normally use PayPal Business Invoice; US clients may choose Zelle, which has no purchase protection. Instructions are sent only from hello@mclab.party. The separate automated Self-Check sells $9/$19/$39 credit packs through Paddle.
Do you need Shopify admin access?
No access is needed for the snapshot. If you ask us to implement changes, we request only the minimum access after a separate scope is approved.
How do you decide what to change?
We do not ask AI to freely rewrite your product page. We trace an observed shopping failure to a missing, unclear or conflicting product fact, recommend the smallest change you can control, and retest the same buyer question under recorded conditions.
Can my team implement the changes?
Yes. The artifact is designed for you or your Shopify team to use. The corresponding live retest is included once the approved change is live and available to the AI shopping systems being tested; MC Lab implementation and additional scheduled or multi-SKU retesting are separately quoted.
When does the included retest happen?
Once the approved change is live and available to the AI shopping systems being tested, MC Lab repeats the corresponding buyer questions and records the new result.
Do you guarantee rankings or recommendations?
No. We deliver captured evidence, a diagnosis of the failure point and a controlled retest. Final retrieval, selection, ranking and presentation remain controlled by each AI platform.
What if the product is already performing well?
Your snapshot documents the healthy path and confirms which technical or content work is unnecessary, so you can direct investment elsewhere.