> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/more-in-this-section-2/scenarios-10.md).

# Retail and e-commerce scenarios

Five canonical scenarios where retailers and marketplaces use Stratix evaluation to gate AI features into production.

## 1. Product discovery and Q\&A

**Setup:** Customer asks "Will this jacket fit a 6'2" 220lb frame?" "Is this dishwasher compatible with my hardwired connection?" AI synthesizes the product spec sheet, prior reviews, manufacturer Q\&A; produces an answer with source citations.

**Why it's hard:**

* Product attributes may be missing or inconsistent across the catalog
* Reviews can contradict spec sheets
* Customers act on the answer — wrong fit info drives returns; wrong compatibility drives serious damage

**Stakes:**

* Returns directly hit margin; some categories see 30%+ return rates
* Compatibility errors on regulated/installed products (appliances, automotive parts) can trigger product-liability exposure
* FTC and state AGs flag misleading product claims even when AI-generated

**Stratix evaluation criteria:**

| Aspect                  | What you measure                                                     | Pattern            |
| ----------------------- | -------------------------------------------------------------------- | ------------------ |
| Spec grounding          | Output cites the spec sheet                                          | Citation existence |
| Review-spec conflict    | Output flags conflicts vs. picking arbitrarily                       | Code assertion     |
| Compatibility hard rule | High-risk categories require explicit compatibility-check disclaimer | Hard rule          |
| Hallucination rate      | Confident wrong claims about specs                                   | Faithfulness judge |
| Reading level           | Comparable to category baseline                                      | Code scorer        |

## 2. Personalized recommendations

**Setup:** Recommendation engine surfaces products. AI-augmented version uses LLM to expand "similar products," generate copy, or rerank.

**Why it's hard:**

* Stale catalog — recommendation surfaces a discontinued SKU
* Sensitive-category guardrails — no recommending alcohol/firearms/adult on a kid's account
* Personalization fairness — same product should not vary in price/availability based on protected-class proxies

**Stakes:**

* Bait-and-switch perception when out-of-stock items dominate recommendations
* FTC scrutiny on dark patterns and personalized pricing
* State AG actions on sensitive categories (alcohol, tobacco, firearms) reaching wrong audience

**Stratix evaluation criteria:**

* **Catalog freshness rule** — Hard rule: every recommended SKU must be in-stock and live in the catalog as of trace time
* **Sensitive-category gate** — Hard rule by account / age band
* **Personalization fairness** — Group fairness on availability and surfaced price
* **Diversity scorer** — Recommendation-set diversity to avoid filter-bubble effects
* **Conversion-correlation watch** — Soft drift signal; recommend regenerating evals if conversion drops

## 3. Customer service chatbot

**Setup:** Order status, returns, exchanges, escalation. Order data + policy data + customer history.

**Why it's hard:**

* Authoritative policy must be reflected (return windows, restocking fees)
* Refund-promise accuracy — bot promises a refund the policy doesn't allow
* Multi-channel consistency — same answer in chat, email, voice

**Stakes:**

* Refund-promise mismatch → regulatory and reputational exposure
* CCPA / GDPR data subject requests must route correctly
* FTC focuses heavily on dark-pattern returns/cancellation flows

**Stratix evaluation criteria:**

* **Policy citation rule** — Every refund/return decision cites the relevant policy section
* **Refund-promise gate** — Hard rule: bot cannot promise refunds outside policy parameters
* **DSR routing** — Privacy requests recognized and routed to the right intake
* **Tone and de-escalation** — Judge for customer-experience quality
* **Channel parity** — Same query produces same answer across chat/email/voice

## 4. Dynamic pricing and promotion

**Setup:** AI surfaces pricing or promotion suggestions. Human merchandiser approves; AI does not autonomously change prices.

**Why it's hard:**

* Personalized-pricing legal exposure — if it correlates with protected class, it's discriminatory
* Bait pricing — promotion that's never actually realized
* Cross-region price-parity expectations

**Stakes:**

* FTC and state AG actions on personalized pricing have multiplied in 2025-2026
* Class-action exposure under state UDAP statutes
* Marketplace platform-policy violations carry delisting risk

**Stratix evaluation criteria:**

* **Personalized-price fairness** — Group fairness on offered price across protected proxies
* **Realizability rule** — Hard rule: any "promotion" must be actually achievable, not bait
* **Cross-region parity** — Code scorer flagging unjustified regional spread
* **Audit trail** — Every price suggestion logged with inputs, model version, approver

## 5. Returns and fraud detection

**Setup:** AI scores returns for fraud markers (wardrobing, empty-box returns, serial returners, organized retail crime patterns). Outputs go to fraud team for action.

**Why it's hard:**

* False positives wrongly accuse legitimate customers and drive churn
* Disparate impact on legitimate but high-return categories (apparel, gifts)
* Privacy boundaries on what the score can use as features

**Stakes:**

* Retail loss to returns fraud reaches multi-billions annually across the industry
* Wrongful denials drive class-action exposure
* State consumer-protection AGs investigate algorithmic denial of returns

**Stratix evaluation criteria:**

* **Top-decile precision** — Tracked against confirmed-fraud labels
* **Fairness scorer** — Group-level denial rate parity
* **Reason-code coverage** — Hard rule: every flagged return cites contributing factors
* **Customer-explanation rule** — Hard rule: customer-facing denial reason is plain-language
* **Override rate watch** — Drift signal if humans override AI more than threshold

## See also

* [Retail eval patterns](/more-in-this-section-2/eval-patterns-10.md)
* [Retail compliance](/more-in-this-section-2/compliance-10.md)
* [Trace evaluations (SDK)](/more-in-this-section-9/trace-evaluations-1.md)


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