Agents that serve customers at scale - without losing the human touch.
D2C and retail brands with high support volumes and complex operations can resolve the majority of routine interactions autonomously, deliver genuinely personalised experiences, and scale operations during peak periods - without scaling headcount.
The challenge
Why AI for Retail & eCommerce?
Retail and eCommerce is where AI agent technology has its clearest, most immediate commercial case. Support queues that can't scale. Personalisation that's actually generic. Ops reporting that takes hours to compile while the data is already stale. The technology exists to solve all three - but most deployments fail for the same reason: they try to automate everything and fall apart on edge cases. Customers learn not to trust the bot. CSAT falls. The project gets rolled back. The right approach is to start with escalation design, not resolution design. Build the human handoff so well that when the AI does fail, the customer barely notices - and the human agent can resolve in 90 seconds instead of eight minutes. Then the resolution rate improves naturally, because customers start trusting the channel.
Common pain points
Support queues that never clear
10,000+ queries per day for a mid-size eCommerce brand. The majority are routine - order status, returns, refund policies - but each one takes human time. When volume spikes during a sale or product launch, queues spiral, first-response times collapse, and CSAT falls.
Escalations that arrive with no context
When existing bots fail - which is often - they hand off to human agents with nothing: no order context, no conversation history, no sense of what was already tried. The customer repeats themselves. The agent starts from zero. Every escalation takes eight minutes instead of two.
Return and refund policies too complex for rule-based bots
Policies vary by product category, purchase channel, promotional tier, and geographic jurisdiction. A rule-based bot fails the moment a customer's situation falls outside the exact pattern it was trained on - which, in real eCommerce, is constantly.
Personalisation that's actually just popularity ranking
Most 'personalisation engines' recommend the same top sellers to everyone with minor category filters. Real personalisation requires reasoning over individual browse history, purchase patterns, stated preferences, and context - not just segment membership.
Operations data that's stale by the time it's useful
Ops teams get raw overnight reports. By the time a stockout is spotted, escalated, and acted on, it's cost two to three days of lost revenue. Real-time ops intelligence changes the economics of inventory management entirely.
Use cases
Agent workflows for Retail & eCommerce
Real workflows we design, build, and deploy - not theoretical concepts.
Intelligent Support Agent
Trigger
Customer submits a query via chat, email, or messaging platform
Workflow
Parse intent and extract key entities (order ID, product, issue type) → retrieve live order and transaction data via OMS API → query RAG knowledge base for applicable policy → assess resolution eligibility within defined thresholds → respond with resolution or initiate workflow (refund, replacement, escalation) → on escalation, generate a context handoff card with full case assembly
Outcomes
60–70% of routine queries resolved without human intervention. Escalations arrive with full context - agents resolve in 90 seconds, not 8 minutes.
CSAT improvement of 15–25 points. First response time from hours to under 2 minutes.
Systems involved
- OMS / Shopify
- Policy knowledge base (RAG)
- CRM
- Payment / refund API
- Live chat or email platform
Human oversight
Refund decisions above a defined threshold require human approval. Escalation option always available to the customer.
Returns & Refund Processing Automation
Trigger
Customer initiates a return request
Workflow
Validate purchase eligibility against return policy for that product, channel, and date → check return window and condition criteria → auto-approve within policy bounds or flag for review → generate return label and confirmation → update inventory and trigger refund or store credit → log for finance reconciliation
Outcomes
Eligible returns processed and confirmed in under 2 minutes. Human review focused on exceptions and disputed cases only.
Returns processing time reduced 80%+. Fraud detection layer catches anomalous return patterns.
Systems involved
- Returns management platform
- OMS
- Payment gateway
- Warehouse management system
- Finance system
Human oversight
High-value returns, fraud-flagged cases, and disputed items always routed to human review.
Real-Time Personalisation Engine
Trigger
Customer browsing session, search query, or homepage visit
Workflow
Build a session context from browse history, past purchases, and stated preferences → retrieve product candidates from the catalogue → rank by predicted relevance, margin, and availability → serve personalised recommendations with explanatory context → update preference model from click and purchase signals
Outcomes
Conversion rate improvement on recommended products. Reduced time-to-purchase for returning customers.
Typical uplift: 12–18% increase in average order value on AI-recommended items versus category-default.
Systems involved
- eCommerce platform (Shopify, Magento)
- Product catalogue
- Customer data platform
- Recommendation engine
- Analytics pipeline
Human oversight
No sensitive profiling categories used. Recommendations visible to customer and explainable on request.
Inventory & Ops Intelligence Briefing
Trigger
Daily scheduled run or real-time anomaly threshold breach
Workflow
Query inventory, sales velocity, and supplier data → detect anomalies (stockout risk, overstocking, slow-moving SKUs) → cross-reference with upcoming promotional calendar → draft an ops briefing with prioritised actions and responsible owners → deliver via Slack or email with data tables included
Outcomes
Ops team gets an actionable daily briefing in their inbox. Stockout risks flagged 5–7 days before they would have been spotted manually.
Stockout incidents reduced. Overstock capital reduced. Ops analyst time redirected to acting on insights, not compiling them.
Systems involved
- ERP / inventory management
- Sales data warehouse
- Supplier APIs
- Promotional calendar
- Slack / email
Human oversight
Recommended actions require human approval before supplier orders or pricing changes are executed.
Proactive Order & Delivery Communication
Trigger
Shipping event (dispatch, delay, out-for-delivery, failed attempt)
Workflow
Monitor carrier API for status events → assess whether the event requires proactive communication → generate personalised message with order-specific details and next steps → deliver via customer's preferred channel → pre-empt support queries by answering the question before it's asked
Outcomes
WISMO ('Where is my order?') queries reduced 40–60%. Customer anxiety reduced before it generates a support ticket.
Support volume reduction during delivery windows. Higher NPS scores for post-purchase experience.
Systems involved
- Carrier APIs (DHL, Royal Mail, UPS)
- OMS
- Email / SMS / push notification
- Customer preference store
Human oversight
Communication frequency caps applied per customer. Delivery failure communications always include a clear resolution path.
Our approach
How we work in Retail & eCommerce
Retail and eCommerce AI fails in two directions: it tries to automate everything (breaks on edge cases, CSAT falls) or it's too conservative to make any difference. We design from the escalation path outward. Before we write a single line of resolution logic, we build the human handoff experience. Every conversation that the AI can't resolve must arrive at a human agent with the order context, conversation history, policies retrieved, and agent confidence score already assembled - so the agent resolves in 90 seconds instead of 8 minutes. Resolution rate is a metric we track. Escalation quality is the design principle. When you get that right, resolution rate improves naturally as customers start trusting the channel.
Escalation design before resolution design
We design the human handoff experience first - every escalation arrives with order context, conversation history, and uncertainty reasoning pre-assembled. This makes the AI safer to deploy and dramatically improves human agent productivity.
Policy versioning as an engineering concern
Return policies change by season, tier, and geography. We build time-aware knowledge bases that apply the policy in effect at purchase time - not today's version. This alone prevents a significant class of incorrect refund decisions.
Per-category confidence thresholds
Refund decisions need different confidence requirements than FAQ answers. We calibrate and evaluate each category separately, preventing a high-confidence FAQ from masking an overconfident refund recommendation.
Alert fatigue prevention built in
In proactive communication, fewer high-quality messages outperform volume every time. We build configurable daily caps and relevance thresholds that prevent customers opting out of genuinely useful notifications.
Our non-negotiables
What we never do in Retail & eCommerce AI
Trust is built by constraints as much as capabilities. These are ours.
We never allow the AI to make final refund or compensation decisions above a defined threshold without human approval
We never design systems that hide or obstruct a customer's route to a human agent
We never share or use customer data across client deployments - every installation is fully isolated
We never go live without per-category confidence calibration and a defined evaluation dataset
We never deploy a support AI without a documented escalation path for every query category
Proven results
What we've delivered in this space
Numbers from real engagements - not estimates or benchmarks from someone else's project.
Autonomous resolution rate
Booking.com: queries resolved without human intervention, up from a 20% baseline on the previous chatbot system.
First response time
Same deployment: first response time dropped from a 4-hour average to under 2 minutes post-deployment.
Customer satisfaction
CSAT improvement measured at 90 days versus pre-deployment baseline across the same query volume.
Recommended services
What we typically build for Retail & eCommerce teams
Questions we always get
Common questions from Retail & eCommerce teams
Proven results
What we've delivered in Retail & eCommerce
Real outcomes from real projects. See how we've helped retail & ecommerce teams automate workflows, ship faster, and scale with AI.
Ready to scope a Retail & eCommerce AI project?
Book a 30-minute discovery call. We'll tell you what's feasible, what's realistic, and what to build first — with a clear timeline and cost estimate.