AI for E-commerce & Retail

Your competitors personalize every touchpoint. Your customers notice when you don't.

DTC founders, e-commerce directors, online retailers doing $1M-50M/year, omnichannel operators. Amazon set the bar for personalization and speed. AI is how you compete without Amazon's budget.

ROI Calculator →
The Challenge

Where e-commerce & retail firms lose time, money, and deals.

Customer support volume scales with revenue and you can't hire fast enough

Every order generates potential tickets — where's my package, wrong size, damaged item, return request. At scale, this becomes a 20-50 person operation. Response time directly correlates with customer lifetime value, and most brands are too slow. Every hour a ticket sits unanswered, the customer's loyalty drops.

Personalization is expected but almost nobody does it well

Amazon set the bar. Customers expect recommendations, personalized emails, dynamic landing pages. Most brands blast the same message to everyone because building real personalization requires data infrastructure they don't have. That gap is costing you conversions every day.

Inventory is either too much or too little

Overstock ties up cash and leads to markdowns that kill margins. Stockouts lose sales and damage brand trust. Most e-commerce companies forecast demand using spreadsheets and intuition, not actual predictive models. You're guessing with real money.

Marketing spend is increasingly inefficient

CAC is rising across every channel. Brands spend thousands on ad creative that underperforms because they can't test fast enough. The winners are the ones producing and iterating on creative at 10x the speed — which is exactly what AI enables. If you're still running the same three ads for a month, you're burning budget.

Before / After

How a weekday hour runs, before and after AI.

Support, stock and ads — from firefighting to running themselves.

Before AI~6 hr response · gut-feel PO
  1. Support backlog grows through peak hours
  2. "Where is my order?" handled one by one
  3. Purchase orders placed on last month's feel
  4. Same 3 ad creatives burn budget for weeks
  5. At-risk customers churn before anyone notices
With AI~seconds · forecast-driven PO
  1. AI resolves 70% of tickets in seconds
  2. Order status, returns, sizing answered instantly
  3. POs generated from SKU-level demand forecasts
  4. Creative variants tested and rotated daily
  5. Churn-risk customers pulled back with personalized offers
Automations

What AI actually does for e-commerce & retail.

AI-Powered Customer Support at Scale

AI agents that handle product questions, order tracking, returns, sizing help, and upsells naturally. Respond in seconds, not hours. Escalate complex issues to your human team with full conversation context.

70% of tickets auto-resolved, 90% faster response

Real Personalization, Not Fake Personalization

Personalized recommendations, emails, and landing pages based on browsing behavior, purchase history, and real-time context. Not 'customers also bought.' Actual intelligence that adapts per visitor.

15-25% increase in AOV

Demand Forecasting & Inventory Intelligence

Predict demand by SKU using sales trends, seasonality, marketing calendars, and external signals. Auto-generate purchase orders. Stop guessing how much to buy.

40% less overstock, 60% fewer stockouts

AI Creative Generation & Ad Optimization

Produce and test ad creative at 10x the speed. AI generates variations, optimizes targeting and bidding across channels, and reallocates budget to what's working in real-time.

30% improvement in ROAS

Customer Lifetime Value & Retention

Identify at-risk customers before they churn. Trigger personalized retention campaigns based on behavior signals. Focus spend on customers who actually stick.

25% improvement in retention
Ship as automations

These ship as stand-alone automations, too.

ROI Calculator

What would AI save your team?

Drop in your team size. See hours and euros saved per year, with your e-commerce & retail defaults already dialed in.

Working hours per week

40h per person

Departments

Toggle a department on and set its team size + loaded hourly cost.

Automation packs

Pick the automations you'd consider deploying.

Estimate confidence

Low = conservative first-3-months. High = stabilized after 6+ months.
Hours saved per week
92 h
≈ 4,232 hours per year
Annual € saved
€136,160
Team hourly cost × hours saved · conservative band
Breakdown by department
  • Customer Support · 60h/wk · €77,280/yr
  • Operations · 20h/wk · €36,800/yr
  • Marketing · 12h/wk · €22,080/yr
Payback period
Against a €12.500 reference build
< 1 month
Open full ROI calculator →
Case Studies

Proof, not promises.

A few of the e-commerce & retail teams we've built with.

E-commerce · DTC Brand· DTC apparel brand, $8M/year

From 20-person support team to 8 — handling 3x more volume

A DTC brand doing $8M/year was drowning in support tickets and bleeding money on ads with declining returns. We built AI support agents that handle 70% of tickets automatically — order tracking, returns, sizing questions — with instant responses. Combined with AI-driven ad optimization and CLV scoring, CAC dropped 45%.

“Our customers get answers in seconds now. Our team finally gets to work on hard problems, not where-is-my-order.”

— Head of CX
AI supportAd optimizationCLV
70%
Tickets auto-resolved
$3.80 → $2.10
CAC
3x
Support volume absorbed
E-commerce · Marketplace Seller· Home goods, multi-channel

Demand forecasting that ended the overstock spiral

A multi-channel home-goods brand was stuck alternating between markdown fire sales and embarrassing stockouts. We built SKU-level demand forecasting that pulls sales, promotions, seasonality and marketing calendars to generate purchase orders automatically. Cash tied up in slow-movers dropped; best-sellers stopped going to waitlist.

Demand forecastingInventory
−42%
Overstock inventory
−60%
Stockout incidents
+18%
Gross margin
E-commerce · Beauty Brand· Independent beauty brand

Personalization that lifted AOV without touching the PDP

A beauty brand wanted more from their traffic without replatforming. We added a personalization layer across recs, emails and landing pages driven by browsing context and purchase history. Same traffic, more purchased, higher-value baskets.

PersonalizationRetention
+22%
AOV
+14%
Repeat purchase rate
0
Replatform required
FAQ

Common questions about AI in e-commerce & retail.

How does AI support handle nuanced product questions without sounding robotic?+

We train AI agents on your product catalog, sizing guides, materials, care instructions, brand voice, and common customer questions. They understand context — if someone asks 'will this fit?' the AI considers the specific product, sizing data, and the customer's order history. Complex or sensitive issues automatically escalate to your human team with full conversation context. Customers can't tell the difference.

Can this integrate with Shopify/WooCommerce/custom platforms?+

Yes. We've built integrations with Shopify, WooCommerce, Magento, BigCommerce, and custom platforms. The AI layer connects via APIs and webhooks, so it works with your existing stack. Most integrations are live within 2-3 weeks.

How is AI personalization different from what Klaviyo or Shopify already offers?+

Platform-native tools use basic rules — segment by purchase count, recommend by category. AI personalization considers browsing behavior, time of day, purchase history, cart contents, seasonal trends, and real-time signals to serve genuinely relevant suggestions per visitor. The difference shows in conversion rates: basic tools lift AOV by 3-5%, AI personalization lifts it by 15-25%.

What data do we need to get started with demand forecasting?+

At minimum: 12 months of sales data by SKU, and your marketing calendar. The more data you have — seasonality patterns, promotional history, supplier lead times — the better the forecasts. We can start with limited data and improve accuracy as we collect more.

What's the ROI timeline for e-commerce AI?+

Support automation shows measurable results in the first 2 weeks — ticket volume drops immediately. Ad optimization and personalization show impact within 2-4 weeks. Demand forecasting needs one full seasonal cycle to reach peak accuracy (3-6 months). The support win alone typically pays for the entire engagement.

Ready to bring AI into your e-commerce & retail business?

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