INSIGRAL - Independent AI Evaluation
AI SHOPPING ACCURACY BENCHMARK

Is your AI shopping assistant actually accurate?

We stress-test AI shopping experiences with realistic customer scenarios and verify critical recommendations against the merchant's actual product catalog.

See how it works ↓
No integration required · Independent verification · Evidence-backed findings
ILLUSTRATIVE ACCURACY CHECK
SYNTHETIC EXAMPLE
person_search Shopper Request

“Find a carry-on suitcase under $300, less than 22 inches tall, and in stock.”

AI Recommendation
AeroCarry Pro — $279 — Height: 24"
Catalog Verification
Official product data — Price: $279, Height: 24", Availability: In stock
cancelHARD CONSTRAINT FAILED: Height exceeds shopper requirement by 2".
HIGH
THE INVISIBLE ACCURACY GAP

Your AI can sound right — and still be wrong.

Usage metrics tell you whether shoppers use AI. They don't tell you whether AI gets the product decision right.

ILLUSTRATIVE COMPARISONPRICE CONSTRAINT
Shopper Constraint
Budget: < $300

Ceiling: $300.00

AI Recommendation
Apex Sound X — $329

Claimed: Under budget

Official Catalog Data
Verified Price: $329

Confirmed in catalog

cancel RESULT: WRONG SHOPPING DECISION+$29 OVER BUDGET

The assistant was conversational and persuasive, but recommended a product exceeding the customer's explicit budget ceiling.

VERIFICATION PROTOCOL

See how an accuracy check works.

A single end-to-end verification trace from natural-language query to catalog ground truth.

01SHOPPER REQUEST
person

Natural-language request with realistic constraints.

“Under-desk walking treadmill < 4.5 inches high”

Ceiling:4.50" MAX
02AI INTERACTION
smart_toy

We interact with the shopping assistant as a real customer would.

WalkFit Mini Treadmill

“Ultra-compact 5.2" profile fits under almost all standing desks!”

warning Ignores BoundarySURFACED
03PRODUCT DATA
database

Recommendations are checked against official merchant product data.

SKU: WF-MINI-01VERIFIED
Height:5.20 in
Clearance:5.50 in
Data Source:Official product data
04ACCURACY CHECK
compare_arrows

We compare the response against shopper requirements and verified product facts.

Ceiling:4.50"
Actual:5.20"
Delta:+0.70" (+15.5%)
cancel BREACH DETECTEDFAIL
05FINDING
HIGH

We document the discrepancy, severity, and why it matters.

Physical failure trace: Recommended treadmill exceeds stated clearance height.
Impact: Product will not fit shopper's space.
FAILURE PATTERNS

Where AI shopping accuracy breaks down.

Four common ways conversational AI can diverge from verified merchant product data.

LIMIT EXCEEDED 01 / SPEC CUTOFF
$0 BASE MAX $1,000
CAP $1,000
$1,420
arrow_upward +$420 Overrun

Hard Constraint Breach

AI recommended $1,420 flagship despite explicit $1,000 strict price ceiling.

SKU CONFUSION 02 / ATTRIBUTE MIX
headphones
SurfacedSpace Gray256GB
swap_horiz
headphones
Actual SpecSilver512GB

Variant / Attribute Mix-Up

AI associates attributes from different variants, recommending Space Gray 256GB with Silver 512GB specifications.

ZERO STOCK 03 / AVAILABILITY
inventory_2
0 UNITS
block False Delivery Promise

Availability Mismatch

AI claimed product is ready-to-ship, but merchant catalog data shows out-of-stock or pre-order.

MISMATCH 04 / CATEGORY
Category divergence illustration showing fine art statue vs vinyl toy figure

Wrong Product Category

Shopper requested a collectible statue, but AI recommended a vinyl toy figure instead.

DIAGNOSTIC MATRIX

What We Test

The 7 core dimensions of conversational commerce evaluated across every scenario.

7 CORE EVALUATION VECTORS
01RECOMMENDATION

Recommendation Accuracy

Does the recommended product actually match the shopper's core request?

02CONSTRAINTS

Hard Constraints

Price, dimensions, weight, and other non-negotiable requirements.

03PRODUCT DATA

Product Data Accuracy

Are price, dimensions, specifications, and other product facts correct?

04VARIANTS

Variant Accuracy

Does the assistant correctly distinguish sizes, colors, editions, and SKUs?

05DISCOVERY

Product Discovery

Can the assistant find the right products from the merchant catalog?

06AVAILABILITY

Availability

Does it accurately represent whether a product can actually be purchased?

07CONSISTENCY

Conversation Consistency

Does it preserve shopper requirements and facts across multiple turns?

THE DELIVERABLE

A benchmark built to show where your AI breaks.

You receive a concrete, evidence-backed evaluation benchmark. Every discrepancy is cross-checked against your catalog so your team knows what's actually broken.

100
Realistic Shopper Scenarios
Verified
Evidence checked against merchant product data
Prioritized
Ranked by Severity & Business Impact
Actionable
Clear Findings & Explanations
analyticsILLUSTRATIVE BENCHMARK DOSSIER & METRIC REPORT
ILLUSTRATIVE BENCHMARK REPORT · SYNTHETIC SAMPLE DATA
FINDINGS BY SEVERITY (ILLUSTRATIVE PROPORTIONS)SYNTHETIC DISTRIBUTION
HIGH SEV
Critical
Breaches constraint
MED SEV
Moderate
Attribute mix-up
LOW SEV
Minor
Suboptimal match
Evidence BasisOfficial Merchant Product Data
FINDINGS ACROSS TEST SCENARIOS (ILLUSTRATIVE VISUALIZATION)100 SCENARIOS
Illustrative visualization of how findings can be mapped across scenarios.
Start25%50%75%Completion
DISCREPANCY RECORD · DIMENSION CONSTRAINT
HIGH SEVERITY
SHOPPER QUERY
“Under-desk elliptical < 9.0 inches clearance”
AI RECOMMENDATION
AeroGlide (Claimed clearance: 8.5")
VERIFIED PRODUCT DATA
Actual clearance: 11.2" (Source: Official merchant product data)
FINDING: The recommendation does not satisfy the shopper's explicit dimension constraint.
EVIDENCE: Shopper requested < 9.0 in clearance; recommended AeroGlide claims 8.5 in clearance; official merchant product data verifies actual clearance of 11.2 in.
WHY IT MATTERS: The product may not fit the shopper’s stated physical requirement.
RECOMMENDED NEXT STEP: Review how explicit dimension constraints are validated prior to product recommendation.
100 SCENARIOS · FULL BENCHMARK DELIVERABLE
BUILT FOR TEAMS SHIPPING AI SHOPPING EXPERIENCES:
Ecommerce Leaders Product Managers AI & Engineering Leads Customer Experience
AI SHOPPING ACCURACY BENCHMARK

Want to know what your AI shopping assistant gets wrong?

Give us your AI shopping experience. We'll stress-test it with 100 realistic customer scenarios and deliver an evidence-backed accuracy benchmark.

✓ No integration required✓ Evidence-backed report✓ Independent verification