Ecommerce evidence collection with Jev
Catalog copy, reviews, and promo banners need a keep / edit / hold decision before they go live. Recs, search rank, and inventory still own merchandising math. Jev judges language against your policy excerpt. It does not set prices or personalize a homepage.
This unofficial page is the evidence collection slice of the ecommerce merchandising decisions pack. Intent: apply the Jev (TypeSafe System One) decision model to ecommerce merchandising decisions evidence collection. Primary search language: Ecommerce Jev evidence collection. Confirm patterns on docs.typesafe.ai. This site does not sell, issue, or proxy TypeSafe keys. Use a credential you already have from the console or a documented gateway.
Independent angle (cover ≠ clone): Claim/policy + merchandising action on catalog text — not a clone of a personalization-engine sitemap or recs API landing page.
Ecommerce use-case context
Evidence collection for ecommerce merchandising decisions happens before POST /v1/systemone. Jev does not browse your warehouse, retriever, or ESP. You gather the product copy or promo text facts, filter them, then ask snap questions. This slice is where fan-out cost math belongs: batch questions, do not re-send state.
Hub: Use cases. Compare, when the other tool is the real job: ecommerce personalization.
Evidence Collection inputs
Collect:
- Title + bullets as they would render
- Policy paragraphs you enforce
- Inventory / locale flags from the PIM — not “probably in stock” prose
Never send:
- Asking Jev which SKU to recommend to a shopper
- Bid or margin curves
- Raw HTML of the entire PDP
Shape the payload like this once the gather step finishes:
{
"item": { "id": "sku-88", "title": "Lose 10kg tea", "bullets": "Guaranteed results in 7 days." },
"policy": { "health": "No absolute health outcomes.", "stock": "Do not claim in-stock if inventory_flag is false." },
"flags": { "inventory_flag": true, "locale": "en" }
}Decision signals and actions
Each evidence field should change a named answer:
| Id | Type | Job |
|---|---|---|
action |
Choice | publish / edit / hold_legal / other |
claim_risk |
Score | plain description → aggressive outcome claim |
policy_ok |
Noul | Copy consistent with policy.* given flags.inventory_flag? |
Action + claim_risk + policy_ok per SKU (or per banner). Batch SKUs as separate states. Do not make one Choice over 200 titles.
Do not treat a Noul of 0.5 as a “medium” ecommerce merchandising decisions score — it means yes and no are equally likely. Conjunctions stay in your code.
Guardrails and escalation
If the gather step fails (empty product copy or promo text, redaction stripped everything, retriever empty), fail closed on publishing customer-visible copy or a live promo. Do not invent evidence so Jev has something to say. TypeSafe’s confidence-gated examples use a lower bar for recoverable reads than for irreversible actions. Those numbers are illustrations. For ecommerce merchandising decisions, treat publish as the high bar (publishing customer-visible copy or a live promo). Tune on labels — see offline evaluation.
Evaluation and rollout notes
Your eval set should include thin-evidence cases, not only happy product copy or promo texts. Label publish/hold gold from merch+legal, plus claim_risk gold. Pin jev-1.13.0 (the versioned id) after you fit thresholds. jev-latest and the marketing line jev-1.13 can move. Log the response model. TypeSafe’s published list price for jev-1.13 is $0.042 per million input tokens (vendor claim — confirm on the models page); output tokens are free on that same page. Unused distractors still bill as input.
Official Python and JavaScript SDKs read TYPESAFE_API_KEY and retry documented 429/529. This site does not sell, issue, or proxy TypeSafe keys. Use a credential you already have from the console or a documented gateway.
Pack map
| Slice | Page |
|---|---|
| Graph and primitives | decision workflow |
What may enter state |
input contracts |
| What to gather first | you are here |
| Atomic rules | policy checks |
| Act / review / abstain | confidence thresholds |
| Reviewer payload | human handoff |
| What to persist | audit trail |
| How it breaks | failure modes |
| Labeled replay | evaluation |
| Shadow → canary | production rollout |
FAQ
Should evidence live in the question text?
Put facts in state and point instructions at item.title, item.bullets, policy.health, flags.inventory_flag. Criteria stay stable so you can replay.
When do I split calls? Action + claim_risk + policy_ok per SKU (or per banner). Batch SKUs as separate states. Do not make one Choice over 200 titles.
Where is the rest of the Ecommerce pack? Start with Ecommerce input contracts and Ecommerce decision workflow. Cluster hub: Use cases.
Will Jev personalize the homepage? No. Recs stay in your personalization stack. This pack gates language.
Can we send product images? Not to System One. Transcribe claims. Images are not accepted.
What this page does not claim
- Not a recs engine, PIM, or pricing optimizer.
- No conversion benchmarks.
- Not official TypeSafe.
- Official TypeSafe status, or that jev.pro issues API keys.
- That a schema-constrained answer is automatically factually correct.
Disclaimer
This is an independent unofficial site and is not affiliated with TypeSafe AI; official documentation is available at https://docs.typesafe.ai.
Primary documentation: https://docs.typesafe.ai. Hub: Use cases.
Sources
Public TypeSafe or adjacent documentation only. No private claims.