Start with the facts.
Products, customer conversations, policies, and brand guidance give an agent the context to work with. It should retrieve the information relevant to the task and make missing information apparent.
An engineering lab · Paris
Leafine builds AI teammates for commerce.
We’re starting with Lea: an assistant that helps merchants work with their store, support their customers, and prepare campaigns.
Our ambition is to give the people behind a brand more capacity to act on what they know: to serve more customers, pursue more ideas, and build a business that keeps its character as it grows.

The problem
A customer asks a question on Instagram. The answer depends on a product in Shopify, a delivery policy in a document, and a promise someone made yesterday.
A campaign starts with an idea. Making it useful means checking the catalogue, choosing the products, preparing the message, and deciding what should reach customers.
The team connects these pieces. They find the facts, explain the context, check the output, and move the work forward.
As the business grows, those small acts of coordination fill the day. The people who understand the brand best become the people every task depends on.
Their knowledge is valuable. Their attention is finite.
The opportunity
AI makes it possible to work with language, interpret a request, and use tools to prepare an answer or an action.
That opens a different possibility for commerce teams: describe the work you need done, and have an assistant gather the relevant information and prepare something you can use.
For that to help, the assistant needs to understand the business behind the request.
A useful product recommendation depends on the actual catalogue. A customer reply depends on the relevant conversation and policy. A campaign depends on what the brand sells and what it wants to achieve.
The opportunity is to bring that understanding into the work itself.
Our approach
We build around three requirements.
Products, customer conversations, policies, and brand guidance give an agent the context to work with. It should retrieve the information relevant to the task and make missing information apparent.
A product selection. A response. A campaign draft. Work should arrive in a form that a person can inspect, improve, and carry forward.
Preparing work and taking an external action carry different responsibilities. The team needs to understand what has been proposed, what requires a decision, and what has actually happened.
These requirements guide how we connect models, business information, and product workflows.
Our first product
Ask about products in your connected store and use that information to prepare the next piece of work.
Let customers describe what they’re looking for and explore relevant products from your catalogue.
Turn a marketing request into a draft your team can inspect and refine.

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Our ambition
The people behind a brand know things that no general-purpose model knows by default: why a product exists, how customers describe it, which promises matter, and what good service looks like.
We believe that knowledge should inform more of the work the business does.
A question from a shopper can reveal something a product page needs to explain. A support conversation can inform a future campaign. A successful piece of work can give the team a better starting point next time.
This is the direction we’re building toward: commerce software that helps a business carry its understanding forward.
The company
Leafine is an engineering lab building AI teammates for e-commerce brands.
Commerce gives our work a concrete standard. Products have real attributes. Customers have specific questions. Campaigns need review. Actions have consequences.
We bring engineering and product work together around those details, starting with Lea.
If you run a brand, build these systems, or want to help shape how they work, we’d like to hear from you.

Build with Leafine
Start with the work already waiting: a customer question, a catalogue to understand, a campaign to prepare.