E-commerce
June 30, 2026
The customer scans the QR code on the packaging and opens the chat: "Is this batch affected by the recall?" "Show me the workshop where this is sewn." "Does your GOTS certificate cover this t-shirt or only the yarn?" A misconfigured bot responds either too little (loss of trust) or too much (confidential supplier name, internal batch number without context).
EuroCommerce defines the digital product passport as a file accessible to consumers, businesses, and authorities via QR, covering origin, composition, and compliance (EuroCommerce, DPP 2026). Defacto Labs recommends three layers: PDP summary, stable proof page, and structured fields linked to the actual batch (Defacto Labs, food provenance 2026).
This guide #312 covers the product traceability AI chatbot: what data to show, when, and with what proof. It complements provenance support (#311) from the perspective of bot automation, batches, DPP, and display matrix.
Summary
Why must the traceability bot filter what it shows to the client?
Internal traceability (ERP, supplier COA, costs) is not customer traceability. Showing a batch number without explanation creates more doubt than "I'll check with our sourcing team."
Three risks of a poorly calibrated bot
Over-disclosure: subcontractor factory name, margin, ERP code
Under-disclosure: "made in Europe" without THREE-LABEL (#311)
Hallucination: invented certificate, country not legally validated
DPP Opportunity
Fabrity estimates that chatbots powered by DPP data respond instantly regarding recycling and origin, improving satisfaction (Fabrity, DPP e-commerce 2026). Tracehub stores DPP in Shopify metafields: the bot reads, it does not reinvent (Tracehub, DPP Shopify 2026).
Principle #312
TRACE-DISPLAY Matrix: each traceability field = public tier, post-purchase auth, or never customer. The bot only displays the tier authorized for the session context.
How does it differ from provenance support #311, history #258, and durability #225?
Five neighboring pieces of content, five data levels.
Support origin (#311)
Origin (#311): ORIGIN-SOURCE, ORIGIN macros, human THREE-LABEL. The #312 orchestrates the bot display: batches, DPP QR, certificates, recall.
Purchase history (#258)
History (#258): auth, need-to-know, trust. The #312 applies need-to-know to the traceability fields and batch lookup by order.
Sustainability (#225)
Sustainability (#225): environmental claims, ECGT substantiation. The #312: traceability evidence (scope certificates, batch COA) without impact arguments.
Materials (#166)
Materials (#166): INCI, composition. The #312 links batch → composition in case of recall or safety concern.
Promise bot (#298)
Promise bot (#298): override ctx_values. The #312 provides the public TRACE corpus for traceability ctx_values.
Batch support (#327)
Manufacturing batches (#327): BATCH-LOOKUP, BATCH-RECALL, CS macros. The #312 automates trace_batch_order and trace_recall_check.
Promise #312
TRACE-DISPLAY, TRACE-BOT-INTENT, order batch lookup, DPP/QR, TRACE-BOT-01 prompt, recall mode, KPI.
What is the TRACE-BOT-INTENT mapping for the traceability chatbot?
The TRACE-BOT-INTENT taxonomy classifies bot traceability requests vs generic origin.
13 bot traceability intents
trace_origin_public: country, public workshop (from ORIGIN-SOURCE #311)trace_supplier_story: producer, cooperative, named farmtrace_cert_scope: GOTS/Oeko-Tex: finished product or yarn only?trace_cert_link: batch number certificate linktrace_dpp_qr: scan product passport contenttrace_batch_order: which batch for my order #{order}?trace_recall_check: is my batch affected by recall?trace_chain_tier: spinning, tannery, mill (Tier 2)trace_proof_document: COA, lab report (if public)trace_compare_batch: difference batch A vs B (rare, B2B light)trace_authenticity: authentic product, anti-counterfeitingtrace_unknown: undocumented data → honest templatetrace_handoff: press, confirmed recall, health dispute
Routing priority
trace_recall_check and trace_batch_order require order auth. trace_origin_public accessible to PDP visitor. Customer service dispute intent blocks all proactive traceability recommendations.
How to build the TRACE-DISPLAY matrix: public, auth, internal?
The TRACE-DISPLAY matrix assigns each field to a bot display tier.
Tier A: public (PDP, anonymous visitor)
THREE-LABEL made/assembled/designed (#311)
Material origin summary
Certifications + scope (e.g. GOTS product level)
Link to page /pages/sourcing or DPP landing
Producer story if marketing approved
Tier B: post-purchase auth (OTP or logged-in)
Batch/lot code linked to customer order
Production date / expiry date if food
Specific lot recall status
COA summary if policy permits
Tier C: never customer (internal bot routing only)
Supplier cost, margin, MOQ
Contractually non-public factory name
ERP codes, internal PO numbers
Unresolved negative audit notes
Digit Software: supplier traceability useful if batch receipt flows toward finished goods (Digit, product traceability 2026). Bot Tier B reads this link order → batch → authorized public fields.
Example food SKU mapping
Tier A: coffee origin Colombia, roasting Lyon. Tier B: lot L2026-034, expiry date 12/2026 for order #4521. Tier C: FOB price per kg, intermediate exporter name.
What evidence and certificates can the bot cite without overpromising?
The bot traceability evidence follows strict scope rules, aligned with #225 anti-greenwashing.
Certificate citation format
Organization: certifier name
Number: cert ID if public
Scope: yarn / finished product / factory site
Validity: expiration date
Link: registry URL or public PDF
Bot prohibitions
"Certified sustainable" without organization. Extending yarn certificate scope to the finished product if not validated. Inventing batch numbers.
Defacto evidence layer
Stable URL evidence page + structured fields + batch reference (Defacto 2026). Bot cites Tier A summary + "see full proof" link rather than a 40-page PDF dump.
Copy trace_cert_scope type
"Our GOTS certificate no. {id} covers the finished product (assembly + labeling), not just the yarn. Validity: {date}. Evidence: {url}."
How to manage batch lookup, orders, and product recall modes?
The lookup lot bot connects Shopify orders to the batch record without exposing the entire inventory.
Flow trace_batch_order
Auth via email or logged-in session
Lookup order → line items → metafield
batch_codeor lot fulfillmentDisplay: lot, production date, link to lot page if it exists
If missing: "lot not indexed on this order, contact us with a photo of the label"
Mode trace_recall_check
EZQR describes dynamic lot QR: immediate recall banner if the lot is affected (EZQR, FSMA traceability 2026). Bot: input lot (photo OCR or manual entry) → compares active recall list → return/refund instructions macro RECALL-TRACE. FDA: TLC not mandatory on consumer label, but QR link recommended (FDA, traceability lot code 2026).
Mode trace_authenticity
Scan GS1 Digital Link QR → bot confirms authenticity if serial matches registry (Tracehub pattern verification). Handoff if serial is unknown or already registered elsewhere.
How to structure the TRACE-BOT-01 prompt and the traceability corpus?
System block TRACE-BOT-01 300-400 words, extension #163.
Prompt blocks
Role: traceability guide, not supply chain detective
TRACE-DISPLAY: respect Tier A/B/C section 4
Corpus: ORIGIN-SOURCE #311 + metafields trace_* + evidence pages
Certificates: 5-line format section 5, never out of scope
Recall: if trace_recall_check is positive → RECALL macro + handoff
Unknown: trace_unknown template, sourcing ticket if recurrent
Shopify Metafields sync
trace_tier_a_json, trace_certificates, trace_dpp_url, trace_evidence_url, batch_code order-level. Indexed via Shopify bot training, corpus cleaning (#103).
Which chat widgets and DPP QR codes should be deployed on PDP and post-purchase?
The multichannel traceability journey aligns physical QR and digital chat.
Bot locations
PDP "Traceability" block: Origin, Certificates, View passport chips
Post-purchase email: "Questions about the origin?" deep link chat trace_batch_order
Page /pages/transparency: embedded bot traceability help hub
QR → chat handoff
QR GS1 Digital Link opens mobile DPP page. "Ask a question" button pre-fills SKU + scanned batch. TraceX: QR batch-specific updates recall without reprinting (TraceX, QR traceability 2026).
Consistency #311
THREE-LABEL identical across PDP, DPP, bot. Inconsistency → origin_inconsistency ticket #311 + corpus patch within 24 hours.
Which vertical playbooks for food, fashion, beauty, and batteries?
Bot traceability varies according to regulations and available data.
Food
Tier B: Expiry date, batch, ingredient origin. trace_recall_check is a priority. FSMA 204: internal records, consumer QR best practice. For human customer service on expiry date, spoilage, and PERISH-CLAIM, see perishable support (#313) and perishable bot (#314).
Fashion & Textiles
Tier A: Tier 1 factory if public, GOTS scope cert. Textile DPP planned for late 2027 (Cefic): prepare composition + origin fields (Cefic, DPP roadmap 2026).
Beauty
Batch = PAO traceability and recall. Cross-reference INCI #166. No health claims from trace COA.
Batteries (DPP pilot 2027)
Strict regulatory fields. Bot cites official DPP page, handoff if hazardous disposal question.
Which KPIs should be measured for the traceability bot?
Measure confidence and accuracy, not raw volumes of incoming questions.
Monthly KPIs
trace_bot_deflection: intents resolved without a human
trace_tier_b_lookup_success: batch found / authentication requests
trace_unknown_rate: trace_unknown / total trace intents
trace_cert_link_ctr: clicks on certificate proof
dpp_qr_to_chat_rate: QR scans → chat question
trace_handoff_rate: recall, press, dispute
CSAT trace conversations: target 4.6+
Quarterly Review
Audit 20 trace_unknown conversations → update TRACE-DISPLAY Tier A. Measure coverage of trace_* metafields on catalogue replenishment.
How does Qstomy display traceability without over-disclosure?
Qstomy runs TRACE-DISPLAY, TRACE-BOT-INTENT and real-time order batch lookup.
Capabilities
Tier A/B/C matrix enforced by intent
Certificate citation in scope format + link
Batch lookup by Shopify order auth
Recall mode compares batch against active list
Deep link QR DPP → contextualized chat
Handoff trace_handoff payload with evidence + batch
Encrypted DTC Scenario
DTC food brand, DPP pilot with 45 SKUs. Before Qstomy: 41 trace tickets/month, trace_unknown_rate 38%, 2 agent certificate over-promise incidents. After TRACE-DISPLAY + corpus: tickets 19/month, deflection 72%, trace_unknown 11%, 0 scope certificate incidents over 10 weeks.
See AI support, Shopify, demo.
Which playbooks should be used to deploy the traceability bot in four weeks?
Playbook 1: traceability fields audit (1 day)
PIM/metafields vs TRACE-DISPLAY Tier A/B/C inventory. Retrieve ORIGIN-SOURCE #311. List DPP gaps.
Playbook 2: matrix + evidence pages (2 days)
Draft public Tier A for top 20 SKUs. Publish /pages/sourcing or batch template pages. Link scope certificates.
Playbook 3: order → batch link (1 day)
Batch metafield on fulfillment or order line. Test trace_batch_order with 10 orders.
Playbook 4: prompt TRACE-BOT-01 + recall dry run (4 hrs)
Index corpus. Simulate trace_recall_check with dummy batch + trace_cert_scope 15 questions.
Playbook 5: PDP chips + QR test (3 hrs)
Activate widgets section 8. Mobile QR scan → verify chat consistency.
Useful linking
This week: list the 10 traceability fields your bot could read today. Classify them as Tier A, B, or C before activating a single "View traceability" chip on the PDP.
Enzo
June 30, 2026
