AI & Machine Learning × Retail & E-Commerce
Intelligent automation for retailers, marketplaces, DTC brands, and omnichannel operators: order-ops, returns and RMA, fraud triage, supplier and PO orchestration, content generation, and exception-handling across the back office. Built by CORTEX dept leads to scale beyond single-bot RPA into the orchestration layer that runs an actual operation.
The problem
Retail and e-commerce operators come to intelligent automation with a familiar problem set: order exceptions piling up faster than ops can clear them, returns and RMA processing that drags margin, fraud-review queues that miss the real cases, supplier and PO emails that nobody indexes, and content generation that bottlenecks the merchandising team. The pattern that fails them is single-purpose RPA bots and disconnected vendor tools: fragile, opaque, and unable to absorb peak-season variance.
Production retail automation needs three things commodity tools don't have: document and conversation understanding that handles real supplier / customer / fraud-signal variance, decisioning calibrated to your policies (not a vendor's), and orchestration that wires into Shopify / NetSuite / Salesforce / your WMS without bolt-on middleware that rots. We design for those from day one, and we ship with the peak-season runbook the merchant team will actually use.
Where it ships
Concrete applications where ai & machine learning unlocks measurable value inside retail & e-commerce delivery constraints.
70%
exceptions auto-resolved
Triages order exceptions across address, payment, fraud, fulfillment, inventory: extracts the cause, decisions within policy, escalates rest. Tools: order DB read/write, payment APIs, WMS integration, customer-comms.
55%
RMA cycle-time reduction
Customer-facing return intake, eligibility decisioning, inspection-routing, refund / replacement orchestration, fraud-flag catch on serial returners. Touchless on standard returns; exceptions to ops review.
Fraud-signal aggregation, transaction triage, chargeback evidence packaging (Visa / Mastercard / network rules), representment drafting. Risk analyst reviews and signs the high-value cases.
Inbound supplier email / EDI processing: PO acknowledgment matching, ship-notice indexing, invoice-PO-receipt three-way match. Auto-resolves matched cases; routes mismatches to AP with the variance explanation.
Generates product copy, alt text, category descriptions, SEO meta from PIM source. Quality-gated through eval rubrics; never auto-publishes. Merchandising reviews on every batch. Feed-syndication automated downstream.
Behind-the-agent automation: case enrichment, refund-issuance with approval gate, exception-routing, post-resolution feedback aggregation. Inside Zendesk / Gorgias / Kustomer; not a separate UI.
How we engage
Each phase has a deliverable, an owner, and an acceptance criterion calibrated to retail & e-commerce delivery.
Two-week discovery: order-flow mapping, exception taxonomy by volume and margin impact, peak-season variance analysis, existing tool inventory (Shopify / NetSuite / Salesforce / WMS / payment / fraud). Output is a roadmap that prioritizes processes where document drift, judgment, or peak-season scale make point tools fail.
Eval set built from real (de-identified) cases labeled by your senior ops leads, not the vendor's defaults. Decisioning calibrated to your refund policy, your fraud thresholds, your return rules. Metrics: extraction accuracy, decisioning agreement with senior reviewer, false-resolve rate, peak-load latency.
Per-decision lineage logged; PCI scope kept out of the agent runtime via tokenized payment APIs. Peak-season runbook: auto-scale paths, manual-fallback queues, SLA monitoring, hourly performance reporting through Black Friday and end-of-season. Reasoned-failure modes designed before they happen.
Weekly performance review with ops in the first 90 days; monthly thereafter. Peak-season pre-readiness review. Quarterly policy-recalibration as your refund / fraud / return rules evolve. The cadence isn't optional in a fast-moving retail operation; we build for it.
Capabilities
Stack
Compliance overlay
Every retail & e-commerce engagement carries the evidence collection that procurement and audit teams expect on day one.
Agents and automation flows never see PAN data. Refund actions go through tokenized payment APIs; the processor handles card data. Logged actions exclude sensitive payment fields. PCI scope stays where it is. We don't expand it accidentally.
Per-customer data access bounded by purpose-of-use and consent. Data-export and deletion flows tested. Retention windows configured per jurisdiction. Audit logs available for DSAR fulfillment.
Tool-use whitelist with approval gates on consequential customer-impact actions (refund > threshold, customer-comms, account modification). Eval rubrics include brand-voice tests. Bias-and-disparity monitoring on fraud and content automation flagged to the merchant for review.
Selected work
Where this fits
Common questions
RPA records-and-replays clicks and breaks when a screen drifts. Off-the-shelf retail automation handles standard merchant patterns; the moment your refund policy or fraud rules diverge, you're back to manual. Intelligent automation combines document and conversation understanding, decisioning calibrated to your policy, and orchestration that integrates with your stack, not a parallel one. We often modernize existing RPA estates rather than rip them out: keep the stable click-replay parts, lift the judgment-and-document parts into intelligent automation.
Yes, because we design for it from week one. Peak-season runbook: auto-scaled inference paths, manual-fallback queues with documented SLAs, hourly performance dashboards through the peak window, and a CoE that staffs the peak-season operations cadence. The systems that fail in November are the ones that weren't pressure-tested in October. We pressure-test in October.
PCI scope is engineered out of the agent runtime. Refund actions go through tokenized payment APIs; the agent issues a refund command, the processor handles PAN. Logged actions exclude sensitive payment fields. We design the tool boundary so PCI doesn't expand into the LLM layer, and we provide the QSA-ready architecture documentation showing how.
Brand-voice testing is built into the eval rubrics: outputs that violate fail the eval, not just QA. Approval gates on consequential customer-impact actions (refunds above threshold, account modifications, outbound communications). HITL on every batch of generated content before it publishes. Customer-impact metrics (CSAT, return rate, complaint volume) tracked monthly to catch any drift in customer experience.
Order-exception orchestration: 60–80% auto-resolution on the standard mix; ops productivity 2–3×. Returns / RMA: 40–60% cycle-time reduction; serial-return fraud catch up 30–50%. Fraud triage / chargeback: 30–50% representment win-rate improvement. Content generation: 5–10× merchandising team output. Economics work above ~50,000 monthly orders or earlier when the operation is breaking on peak-season scale.
Discovery + eval-set build: 3–4 weeks, $60K–$140K. Production build (single use case): 4–7 months, $300K–$900K. Multi-process estate (orders + returns + content + supplier): 8–14 months, $1.0M–$2.8M. Managed services post-launch: $25K–$110K monthly retainer covering eval iteration, peak-season readiness, drift monitoring, and policy recalibration.
Talk to us
A senior engineer plus the CORTEX department lead joins the first call, both with prior retail & e-commerce delivery experience.