AI for eCommerce that sells the product you actually have
eCommerce AI fails in a specific way: it invents an attribute, a customer buys on it, and the return and the chargeback both land on you. Everything here is built to be grounded in the catalog.
What changes here
Artificial Intelligence in eCommerce is not the same engagement
Search and recommendations grounded in the catalog
Semantic search over your own product data, so a shopper asking for something in plain language reaches the SKU that exists rather than the nearest keyword match.
Catalog enrichment with a human gate
Generated descriptions, attributes, and alt text produced at volume — then reviewed before publication, because a fabricated specification is a returns problem with a paper trail.
Support deflection on the questions that repeat
Order status, returns policy, and sizing answered from your own systems and policy pages, with anything about a payment or a complaint routed to a person.
The failure mode worth designing against
The risk in retail AI is not a data breach, it is an unsupported claim at scale. A generated line that overstates a material, a certification, or a compatibility is a consumer-protection exposure across every listing it touched.
- Generated copy grounded in supplier data, with the source attribute retained beside it
- A review step before publication for anything describing safety, materials, or compatibility
- Card data kept entirely outside the AI path, so PCI DSS scope does not expand
- Personal data in support conversations handled under your existing privacy notice, including CCPA and PIPEDA rights
The work itself
Full artificial intelligence pageLLM applications and RAG over your own data
Retrieval-augmented systems on your documents, tickets, and records — chunking, embeddings, a vector store, and citations, so answers can be traced back to a source.
Machine learning models and forecasting
Classification, scoring, and demand or churn forecasting trained on your history, with feature pipelines and retraining scheduled rather than remembered.
Document, voice, and workflow automation
Intake, extraction, classification, and summarization wired into the systems the work already lives in, with a human review step where the cost of being wrong is high.
Evaluation sets and regression testing
A scored test set built with your subject-matter experts and run on every change, so a prompt or model swap has to prove it improved things.
Guardrails, grounding, and audit trails
Input and output filtering, retrieval grounding, refusal paths, and logged prompts and responses — the record you need when someone asks why it said that.
Latency, token cost, and model routing
Caching, batching, and routing cheap work to smaller models, with spend and p95 latency on a dashboard rather than a surprise invoice.
eCommerce questions we get asked
Something more specific? Send us the situation and we’ll answer it straight.