AI & Machine Learning × Retail & E-Commerce
Production-grade AI agents for e-commerce operations: customer-service triage, sales-acceleration assistants, post-purchase recovery flows, and merchandising copilots. Built by CORTEX dept leads with the eval frameworks, tool-use boundaries, and human-in-the-loop discipline that keeps agents shipping behaviour you'd defend.
The problem
E-commerce buyers come to AI agents with a real ask: handle 60% of customer-service volume autonomously, accelerate the conversion funnel, recover abandoned cart and post-purchase drop-off, and surface merchandising decisions without adding headcount. The pattern that fails them is a bolt-on chatbot dressed up with three retrieval calls: confident, plausible, but not actually doing work. The first time it commits a refund it shouldn't, the program is over.
Production e-commerce agents need three things commodity chatbots don't have: a tool-use boundary (what actions the agent can take, with explicit approval gates), an eval framework (how we know it's getting better, not regressing), and a human-in-the-loop pattern that scales (when does an agent escalate, and how does the agent learn from the human's edit). We design those in from day one, not as a phase-2 cleanup.
Where it ships
Concrete applications where ai & machine learning unlocks measurable value inside retail & e-commerce delivery constraints.
62%
tier-1 deflection
Handles tier-1 e-commerce service: order status, returns, shipping changes, simple refunds within policy. Tools: order DB read, return-policy logic, refund issuance with approval gate, escalation to human.
+18%
conversion lift
Customer-facing AI that answers product questions, recommends configurations, and handles the long tail of pre-purchase friction. Bounded to product catalog + policy; never invents inventory.
Outbound flows for delivery exceptions, return-window reminders, replacement-vs-refund decisioning, and re-engagement. Tools: shipping APIs, customer-comms, RMA system.
Internal agent for merchandising teams: SKU performance summarization, markdown recommendations, and competitive-pricing lookups. Bounded to read-only and recommendation; final calls stay human.
Generates product descriptions, alt text, category copy, and SEO meta from PIM source data. Quality-gated through eval rubrics; never auto-publishes. Human review on every batch.
Agent for ops teams: order-flag investigation, fraud-signal triage, vendor-shipment lookup, and case-summarization. Sits inside Zendesk / Gorgias / customer-service tooling, not as 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: candidate use cases, brand-risk surface, customer-impact tier per use case, and existing tool / data inventory. Output is a prioritized roadmap with tool-use boundaries and human-in-the-loop checkpoints designed before build.
Build the eval set before the agent. Real customer interactions, ground-truth labels, and the metrics that matter (accuracy, refusal rate, escalation rate, customer-satisfaction). Agent gets shipped against the eval, not against the demo.
Tool-use whitelist with approval gates on consequential actions (refund > $X, account-data export, order modification). Per-call logs with prompt + tool calls + decisions + reviewer. Drift monitoring against the eval set.
Human-in-the-loop pattern explicit per use case: when does the agent escalate, how does the human review the escalation, how does the human's correction feed the eval. Weekly review cadence; monthly model-update cadence.
Capabilities
Stack
Compliance overlay
Every retail & e-commerce engagement carries the evidence collection that procurement and audit teams expect on day one.
Agents never see PAN data. Refund actions go through tokenized payment flows; the agent issues the refund command but doesn't handle the card data. Logged actions exclude sensitive payment fields.
Per-customer data access bounded by customer consent and identity. Data-export and deletion flows tested. Audit logs retained per regulatory requirements.
Tool-use whitelist with approval gates on consequential actions. Eval rubrics include brand-voice tests; outputs that violate fail the eval, not just QA. HITL discipline keeps the human in control of customer-impact decisions.
Selected work
Where this fits
Common questions
Shopify's built-in features are good for the standard merchant. They ship behaviour out of the box. Custom AI agents win when the use case is specific (your refund policy is non-standard, your fraud rules are unique, your brand voice matters), the volume is high enough to justify build over buy, or the tool boundary needs auditable approval gates that off-the-shelf doesn't expose. Below those triggers, use the platform features.
Three guardrails. (1) Retrieval-grounded: the agent answers from product catalog / policy / order data, not from open generation. (2) Refusal-first: if the data isn't in scope, the agent escalates rather than guesses. (3) Eval-checked: brand-voice and accuracy rubrics in the eval set catch regressions before they ship. Hallucinations are a design problem, not an inherent property of LLMs.
It can issue refund commands; it doesn't handle card data. The agent calls the payment processor's refund API with a token; the processor handles PAN data. We design the tool boundary so PCI scope doesn't expand into the agent runtime. The agent stays out-of-scope.
Eval set + regression suite. Every model change, prompt change, or tool change runs against the eval set with explicit pass thresholds. Below threshold, the change doesn't ship. Drift monitoring on production traffic catches issues the eval missed; new failure cases get added to the eval.
On customer-service triage, mid-market retailers typically see 50–70% tier-1 deflection at launch, climbing to 75–85% after six months of eval iteration. CSAT holds or improves vs. human baseline (humans are slower; the agent is fast and consistent within scope). Agent cost is typically 5–15% of human-handled equivalent volume: economics work above ~30,000 monthly tickets.
Discovery + eval-set build: 3–4 weeks, $60K–$120K. Production agent build (single use case): 3–5 months, $250K–$700K. Multi-agent estate (service + sales + ops): 6–10 months, $700K–$1.8M. Managed services post-launch: $20K–$80K monthly retainer covering eval iteration, drift monitoring, and model updates.
Talk to us
A senior engineer plus the CORTEX department lead joins the first call, both with prior retail & e-commerce delivery experience.