Generative AI: eCommerce and Retail

Generative AI in eCommerce and Retail

AI that sells the products you actually have, at the price and stock level you actually hold. We build product content pipelines, shopping assistants and support agents that read from your PIM, OMS and ERP instead of guessing, with an evaluation harness and human approval wherever money or brand claims are involved. Senior engineers own the design, AI coding agents carry the volume, and every line is reviewed by a human.

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Key highlights

The use cases that pay back first in retail are product content at SKU scale, order and returns support, and search that understands intent.
Every agent answers from your live systems: PIM attributes, OMS order status, carrier tracking and stock, so it cannot recommend a product you cannot ship.
Refunds, discounts and published product copy pass through approval rules and a human, not straight from the model to the customer.
We measure each feature against a golden dataset before launch and against conversion, deflection or handling time after it.
A proof of concept usually takes about 4 weeks; a production agent wired into your stack usually takes 10 to 16 weeks.

Why retail AI projects stall, and what is worth solving

Retailers rarely fail with AI because the model is weak. They fail because the model cannot see the order, the stock or the attribute it needs, and nobody can prove the feature moved a number.

Thousands of SKUs with thin content. Supplier copy, half filled PIM attributes and missing size or material data. A generic generator fills the gaps with invented specifications, which turns into returns and misleading claims.

Search that misses intent. A shopper types waterproof running shoe for wide feet and keyword search returns socks. Synonyms, attributes and fit notes exist in the catalog but nothing connects them to the query.

Support queues full of where is my order. Order status, delivery and returns questions dominate contact volume. FAQ chatbots cannot see the OMS or the carrier, so they escalate anyway and the customer waits twice.

Chatbots that invent policy. A bot that promises a refund window or discount you do not offer creates a real obligation. A Canadian tribunal has already held an airline to a refund policy its chatbot made up.

Data split across PIM, OMS and ERP. Product data in Akeneo, orders in Shopify, stock in the ERP, returns in a third tool. A demo built on a PDF export works once and goes stale the next day.

Pilots judged on demos, not conversion. No A/B test, no evaluation set and no cost model for peak trading. When token spend spikes over Black Friday nobody can say whether the assistant earned it.

Use cases

Generative AI use cases we build for retail

Each use case is grounded in your own systems, measured against a golden dataset, and gated by approval rules where it touches money or published claims.

Product content at SKU scale

Document extraction pulls attributes from supplier sheets, spec PDFs and images into your PIM schema, then an LLM drafts titles, descriptions and channel feeds for your site, Google Merchant Center and marketplaces. Every claim must map to a PIM attribute, and merchandisers approve in Akeneo or Salsify.

Conversational and semantic search

An LLM turns natural language queries into structured filters for Algolia or Elasticsearch, hybrid retrieval handles the long tail, and CLIP embeddings add search by image. Results come only from in stock catalog items, never from the model's memory.

Order and returns support agent

A tool using agent with read access to Shopify or your OMS, carrier tracking and a RAG index of your policies. It answers order status, starts returns within your rules and hands refunds above a threshold to a human in Gorgias or Zendesk with the full context.

Merchandising and demand copilot

Forecasting models produce the numbers, an LLM explains them. Planners ask why sell through dropped in a region and get a text to SQL answer over the warehouse with the query shown, read only, alongside the forecast that drove the replenishment plan.

Review and feedback intelligence

Summaries of reviews and returns reasons per product, defect and fit themes routed to buying and quality teams, and moderation of user content. We summarise genuine reviews; we never generate them.

Agent ready catalog

Clean structured product data, schema.org Product markup and an MCP server over catalog, price and stock so AI shopping agents can read you accurately. We assess emerging checkout protocols such as OpenAI and Stripe's Agentic Commerce Protocol and Google's AP2 before you commit to one.

How we deliver

From one use case to a trading season

01
Use case discovery and data readiness
We rank candidate use cases by value and by how ready the data is: PIM completeness, OMS and carrier API access, policy documents. Output: a written plan with the first use case, its success metric and its risks.
02
Evaluation harness and prototype
A golden dataset of real queries, SKUs and tickets with acceptance criteria, then a prototype tested against two or three candidate models so the choice is made on accuracy, latency and cost per task.
03
Guarded pilot with human in the loop
Live on a slice of traffic or a single category. Generated copy is approved before publishing, refunds above a threshold need a person, and every answer is logged for review.
04
Production rollout with monitoring
Ramp behind a feature flag with A/B measurement, per channel cost caps sized for peak trading, drift alerts on accuracy, and quarterly model reviews as new versions ship.

AI governance for retail

Most retail AI is not high risk under the EU AI Act, but consumer protection, advertising and privacy rules apply to whatever the model says on your behalf.

EU AI Act transparency (Article 50) GDPR and UK GDPR CCPA and CPRA FTC rule on fake reviews UK DMCC Act 2024 EU Omnibus Directive (reviews) PCI DSS v4.0 scope control Audit logs of every agent action

Customers know when they are talking to AI. The EU AI Act transparency duties for chatbots and synthetic content apply from 2 August 2026. Assistants identify themselves, generated or edited product imagery is labelled where required, and we document why each use case falls outside the high risk categories.

Generated content is still your advertising. The FTC rule on consumer reviews and testimonials prohibits fake reviews, including AI generated ones, and the UK DMCC Act 2024 bans fake reviews outright. Our pipelines never write reviews, product claims must trace to PIM data, and a merchandiser approves copy before it goes live.

Customer and payment data stay contained. Personal data is redacted before prompts where the task allows, models run under enterprise terms that exclude training on your data, and EU or UK hosting or self hosted models are available. Agents never see card numbers, so your PCI DSS scope does not grow, and every action they take is logged.

Models and tooling we use for retail AI

Portable model choice behind a thin abstraction, retrieval over your real catalog, and adapters for the commerce platforms you already run.

Claude, GPT-5 and Gemini Llama 4 and Qwen (self hosted) LangGraph and Anthropic Agent SDK MCP tools pgvector and Qdrant Algolia and Elasticsearch CLIP visual search Akeneo and Salsify PIM Shopify and Adobe Commerce APIs Gorgias and Zendesk AWS Textract Langfuse and LangSmith

FAQ

Common questions

How much does a generative AI project for retail cost?

It depends on scope, so we quote from a written estimate rather than a price list. Most clients start with a fixed price proof of concept for one use case, then move to a production build or a dedicated engineer. Model API costs are passed through at cost and capped per channel. Our pricing page lists current entry points, and we send a written scope, timeline and estimate within 48 hours.

Is our customer and order data safe with an LLM?

Yes, when it is designed for. We use enterprise model APIs that do not train on your data, redact personal data before prompts where possible, offer EU or UK hosting or self hosted models, and give agents read only access by default. Card data never reaches the model.

How do you stop the AI inventing product details or policies?

By grounding and checking. Product copy can only state facts that map to a PIM attribute, support answers come from your live OMS and a retrieval index of approved policies, and outputs are validated against schemas before anyone sees them. A golden dataset runs on every change, and refunds, discounts and published copy go through approval rules.

How long until a retail AI feature is live?

Usually about 4 weeks for a proof of concept and 10 to 16 weeks for a production agent. The range depends mostly on how clean your PIM data is and how easy your OMS and carrier APIs are to reach.

Should we buy an off the shelf AI tool or build our own?

Buy where your need is standard and build where the data or workflow is yours. Helpdesk AI add ons and PIM content features work well for generic tasks. When the agent needs your returns rules, your OMS, your stock logic and your brand voice together, a custom build on portable models usually fits better, and we often combine both.

Will AI generated product descriptions hurt our SEO?

Not if they are accurate and useful. Google's guidance says AI generated content is acceptable when it is helpful and not produced mainly to manipulate rankings, while mass produced low value pages fall under its scaled content abuse policy. That is why we ground copy in real attributes and keep a human approval step.

Keep exploring

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