Industry
AI and machine learning for e-commerce
Short answer
An online store gets the most from AI in five places: recommendations and search ranking for conversion, demand forecasting for stock, assistants and agents for support cost, document processing for supplier paperwork, and fraud scoring for chargebacks. Start with the one where the cost is largest and best measured; SwankyForge publishes the price range for each and proves the gain on your own data first.
Problem → system
Find the line that costs you most. Each system has its own page with the timeline and price range.
| Problem | System | Measured by | Typical price |
|---|---|---|---|
| Visitors browse but do not buy; average order value is flat | Recommendations and search ranking | Conversion, revenue per session, items per order | €14k–€42k |
| Bestsellers run out while slow lines fill the warehouse | Demand forecasting and order recommendations | Forecast error, stockouts, stock held | €10k–€28k |
| Support answers the same questions about delivery and returns | RAG copilot over policies and order data | Tickets answered without a person, reply time | €8k–€18k |
| Refunds, cancellations and address changes wait in a queue | AI agent with approval rules | Requests closed end to end, handling time | €13k–€38k |
| Supplier invoices and delivery notes are typed in by hand | Document processing | Documents with no manual entry, error rate | €7k–€42k |
| Chargebacks grow and good customers get declined | Fraud scoring | False alarms at the same catch rate, chargeback rate | €7k–€42k |
Where to start
- 1
Start where the cost is measured
If you already know what stockouts or support cost per month, that line will show a result fastest, because the baseline exists.
- 2
Prefer the line with the most data
Recommendations need clicks and orders; forecasting needs sales history. The system with the richest history usually wins the first slot.
- 3
Check whether a simpler fix exists
A curated bestseller list, a reorder rule or a better FAQ page sometimes moves the number for a fraction of the cost. We check that before proposing a model.
Related case
- Reference design
Four analytics tools disagreed about the same numbers. Now there is one set everyone trusts.
- Reference design
Planning ran on a spreadsheet and one growth rate. Stock now follows actual demand.
- Reference design
Every invoice was read and typed in by hand. Four in five now go through untouched.
- Reference design
Analysts spent their day on false alarms. The review queue is now a third of the size.
Questions
Is AI worth it for a small online store?
Often not yet. With a small catalogue or low traffic, curated lists, a popularity sort and simple reorder rules perform about as well. The case for a model grows with catalogue size, traffic and the cost of the problem.
Do we need to replace our e-commerce platform?
No. The systems work next to Shopify, Magento, a custom store or a marketplace account, through their APIs and data exports.
What data does an online store need for AI?
Orders, the product catalogue and site events for recommendations; sales and stock history for forecasting; help-centre articles and tickets for a support assistant. Months of history are enough to start most of them.
How do we know the AI system actually helped?
Every project starts with one agreed metric and a baseline. Recommendations are proven in an A/B test; forecasts are scored against your current method on past periods before go-live.
Tell us about the task.
The first call is free. If this system is not the right answer for your numbers, we say so.