We Build AI Agents & Automations
for E-commerce Businesses.
You have every customer's purchase history, browse behaviour, and lifetime value. You're still sending the same email to all of them. Walmart gave every store associate an AI agent. Amazon's Rufus is live for hundreds of millions of shoppers. Home Depot's entire cloud strategy is built around demand forecasting AI. The question is which builds move the needle for your business first.
AI Agents E-commerce Companies Are Deploying in 2026
Walmart rolled out four AI super agents. Amazon's Rufus and Target's ChatGPT app are live for hundreds of millions of shoppers. Home Depot's entire Google Cloud partnership is built around demand forecasting AI. Every one of these can be built for your business.
Handles natural-language product discovery, comparison, and checkout — understanding questions like "I need a gift for someone who runs marathons, budget £50" and returning specific product recommendations with reasoning. Reduces search abandonment and increases basket size from discovery sessions compared to standard search.
Processes return and exchange requests end-to-end — validates eligibility against policy, issues RMA authorisations, makes refund decisions within set parameters, scores return requests for fraud signals, and triggers the appropriate fulfilment action. Customer service team handles exceptions. The routine return is fully automated.
Ingests sales history, seasonality, promotional calendars, trend signals, and competitor data to generate restocking recommendations, markdown timing suggestions, and out-of-stock risk alerts — updated continuously rather than on a monthly planning cycle. Lower carrying costs, fewer stockouts, better margin.
Monitors competitor pricing across specified categories in real time, identifies pricing gaps and opportunities, adjusts your prices within guardrails you define, manages promotional calendars, and models the margin impact of proposed promotions before they go live. Pricing becomes a managed asset rather than a quarterly review.
Resolves order enquiries, delivery questions, product information requests, and account issues end-to-end — without a human agent for routine cases. Accesses live order data, tracking information, and your full product catalogue to answer accurately. Escalates to a human only for genuinely complex or sensitive cases.
Manages PO reconciliation, invoice matching, dispute resolution, and supplier communication automatically across your vendor base. Flags discrepancies, chases outstanding items, and escalates genuine disputes to the buying team. Operations team handles exceptions and relationships — the agent handles the paperwork chain.
Implementations & Automations
Structured builds with defined scope, timeline, and starting price.
Acquisition & Retention
The customer lifecycle layer — from first purchase to repeat buyer to VIP. The most direct path to LTV improvement without increasing CAC.
Customer data split across Shopify, Klaviyo, and a spreadsheet. No unified view of a customer's full history. Lifecycle sequences generic and one-size-fits-all.
Unified customer view across all data sources. Lifecycle automation triggered by actual purchase behaviour, not time intervals. Repeat purchase rate improves measurably within 90 days.
70–80% of shopping carts abandoned. Recovery sequences generic — same email, same timing, same discount regardless of who left, what they left, and why they might come back.
Recovery sequences personalised to the specific product, the customer's history, and the optimal send timing for their behaviour pattern. Recovery rate consistently outperforms static sequences.
First purchase to second purchase conversion rate low. Post-purchase communication generic — same thank you email, same review request, nothing personalised to what they actually bought.
Post-purchase sequence calibrated to the specific product category and repurchase cycle. Second purchase prompted at the right moment. Repeat purchase rate improves within the first quarter.
Top 10% of customers generating 40% of revenue with no dedicated programme. They get the same experience as everyone else. Eventually a competitor gives them one.
High-LTV customers identified automatically and enrolled in a differentiated programme. Early access, personalised recommendations, dedicated support. Retention rate for your best customers improves significantly.
Operations & Fulfilment
For e-commerce businesses where operational efficiency determines whether growth is profitable or just expensive.
Support team handling 200 tickets a day where 160 are "where is my order", "can I return this", and "do you have this in size L". Every ticket gets the same human time regardless of complexity.
Routine order and return queries resolved automatically with live data. Support team handles the complex and sensitive cases. CSAT maintained or improved. Support cost per order drops measurably.
Buying decisions made on last season's data and gut feel. Overstocked on slow movers, understocked on fast ones. Markdown timing reactive rather than planned. Margin eroded by predictable, preventable mistakes.
Restocking recommendations generated automatically. Stockout risk flagged weeks in advance. Markdown timing optimised to clear inventory at the best possible margin. Buying becomes data-driven.
Marketing Intelligence
For e-commerce businesses that want to use customer data properly — personalisation, segmentation, and social proof that drives conversion rather than vanity metrics.
Product recommendations showing the same bestsellers to everyone. No personalisation based on browse history, purchase behaviour, or affinity signals. "Customers also bought" using basic collaborative filtering from 2015.
Recommendations personalised to each individual's actual behaviour. Average order value improves. Conversion rate on recommendation clicks increases. Amazon attributes 35% of revenue to this — the underlying logic is now accessible to independent brands.
Hundreds of reviews coming in monthly. Nobody reading them systematically. Product issues appearing in reviews 3 months before anyone in the business hears about them through another channel.
Reviews clustered automatically by theme and sentiment. Product issues surfaced immediately. UGC identified and tagged for marketing use. Merchandising decisions informed by what customers actually say.
Upsell opportunities missed because the post-purchase moment is handled by a generic thank-you sequence. Complementary products known, but never presented at the right time.
Upsell and cross-sell presented at the optimal post-purchase moment, calibrated to the specific purchase and the customer's history. Revenue per order increases without touching the checkout flow.
Channel & Partner Management
For e-commerce businesses managing multiple sales channels, affiliate programmes, or influencer relationships alongside their core DTC operation.
Entire customer base segmented into 3 or 4 broad groups. Everyone in "Female, 25–35" gets the same email. The data to do far better exists — it just isn't being used.
Behavioural micro-segments built automatically from actual purchase and browse data. Communication personalised to each segment's specific profile. Campaign performance improves across every metric that matters.
Influencer relationships managed over email threads and spreadsheets. Attribution inconsistent. No way to tell which partnerships are actually driving revenue versus vanity metrics.
All partnerships tracked in one system with proper attribution. Performance ranked automatically. High-ROI partnerships deepened. Low-ROI ones identified before the next renewal.
Not sure which scope to start with?
Most e-commerce businesses start with cart recovery or post-purchase automation, see the revenue impact in weeks, then scope the full lifecycle layer.
Starter
One focused build. Cart recovery, review intelligence, or post-purchase sequences — results before you commit to anything bigger.
Growth
Full lifecycle automation connecting acquisition, retention, and reactivation — plus at least one AI agent. LTV starts improving in the first quarter.
Enterprise
Custom agents, demand forecasting, personalisation engine, and deep integrations across your full stack. For e-commerce businesses scaling aggressively.
They built exactly what we needed without overcomplicating it. The system has been running for months without us having to touch it.
Every e-commerce build is led by one of us directly.
E-commerce implementations involve real-time data, multiple platform integrations, and customer-facing logic where errors cost revenue immediately. Shivam Kapoor and Sonam Malhotra are active on every project — you get people who've built these systems before, not a team learning on your live store.
Know exactly what your store
needs to build. In 30 minutes.
Free audit. We map which agents and implementations move the needle for your specific business, in what order, and what outcome to expect.
What e-commerce businesses ask us — answered straight.
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