AI infrastructure for ecommerce

Ecommerce personalisation lives or dies on memory. A recommendation agent that forgets a customer between sessions is back to guessing from a single basket, when the value is in everything it should already know, what they've bought, what they browsed, what they returned, what they care about. At the same time the product side is a data problem: catalogues, reviews, and feedback at a scale no one reads through by hand. If you're building product or customer intelligence tools, the infrastructure you need handles both, persistent customer memory and catalogue processing at scale.

This page is for teams building ecommerce intelligence tools. Exabase gives you customer memory across sessions, catalogue and review processing through Extract, and per-customer isolation through Bases. Each customer gets a persistent, isolated picture that personalisation can build on, and the product data becomes something your tools can actually use.

What you can build

Ecommerce tools tend to be one of a few shapes, each on infrastructure that already exists.

A personalised recommendation agent that remembers a customer's preferences and purchase context across sessions and recommends from the full picture, not just the current basket, per-customer memory in isolated Bases.

A product intelligence tool that processes catalogues, reviews, and feedback at scale and makes them searchable and analysable, the document extraction at scale pattern applied to product data.

A customer support agent that knows a customer's order history and past issues, so it doesn't ask them to repeat what the store already knows, the customer support agents pattern.

A review and feedback search tool that lets a team query what customers are saying across thousands of reviews by meaning, Deep Search over processed feedback.

Ecommerce problems, solved

The problems ecommerce builders run into are specific, and each has an answer.

Customer context that resets every session. Personalisation needs persistence. Memory holds a customer's preferences, purchase history, and context across sessions, and keeps it current through contradiction resolution, so when a customer's tastes shift the agent reflects the change rather than recommending from a stale picture.

Per-customer personalisation at scale. Each customer's picture has to be their own, and there are a lot of customers. Bases make isolation structural, one per customer, from a single API call, so personalisation is built only from that customer's data and scales without partitioning logic. It's the multi-tenant memory pattern.

Catalogues, reviews, and feedback at volume. Product data is vast and arrives in every format. Extract turns catalogues, reviews, and feedback into clean, structured, searchable text through one API, at the scale ecommerce generates.

Finding signal in customer feedback. Deep Search finds by meaning across reviews and feedback, so a team can surface what customers are saying about a theme regardless of the exact words used, and search holds quality as the volume grows.

The infrastructure underneath

Four primitives carry most ecommerce tools. Memory holds customer preferences and purchase context across sessions. Extract processes catalogues, reviews, and feedback at scale. Bases isolate per customer from a single API call for personalisation. Deep Search finds signal in product and feedback data by meaning. One API key, rather than assembling memory, extraction, and isolation yourself.

Personalisation that compounds per customer

An ecommerce tool on this foundation gets better at serving each customer the longer they shop. A recommendation agent meeting a customer for the first time has only the current session; after months it knows their preferences, history, and patterns, and recommends from the full picture. Because the memory self-organises, that accumulated context stays accurate as it grows rather than turning into noise, and per-customer isolation scales from one API call, so the tool personalises for millions of shoppers without the separation getting fragile. The product-data side compounds too: every catalogue and review processed adds to a searchable, analysable body that the cost of processing is paid for once. A customer with a rich history has a real reason to stay, because the personalisation is built on context that doesn't transfer to a competitor starting cold.

Get started

Start with the getting started guide, then the use-case pages that match what you're building: long-term memory for any agent for customer context, multi-tenant memory for SaaS for per-customer personalisation, document extraction at scale for product data, and customer support agents for support. There's a free tier to build against.

FAQs

Does a recommendation agent remember a customer across sessions?

Yes. Memory holds a customer's preferences, purchase history, and context across sessions and keeps it current through contradiction resolution, so the agent recommends from the full picture rather than the current session alone.

How is each customer's data kept separate for personalisation?

Each customer gets their own Base, a structurally isolated environment, so personalisation is built only from that customer's data and one customer's picture never bleeds into another's. It's the multi-tenant SaaS pattern.

Can it process product catalogues and reviews at scale?

Yes. Extract turns catalogues, reviews, and feedback into clean, structured, searchable text through one API, at the volume ecommerce generates.

Can a team search customer feedback by meaning?

Yes. Deep Search finds by meaning across reviews and feedback, so a team can surface what customers say about a theme regardless of exact wording, and it holds quality as volume grows.

Does it keep up when a customer's preferences change?

Yes. The new preference supersedes the old through entity resolution, so the agent reflects the customer's current tastes rather than recommending from a stale picture.

Is this a finished ecommerce product or something I build on?

Something you build on. Exabase is the infrastructure, customer memory, catalogue processing, isolation, and search, and you build the recommendation, intelligence, or support tool on top.