Your best salesperson, with a team of agents behind them.

Not a chatbot and not a CRM. a.Store understands the customer and recommends the right product from an enriched catalogue with its own taxonomy, shown as a native Meta storefront, a carousel or a webview, inside the conversation. Once the item is chosen, a.Commerce takes over and carries it to payment.

Book a demo See all five agents

What comes with it.

Enriched catalogue

Attribute enrichmentCustom taxonomySynced with your platform

Your assortment is read, completed and organised into a taxonomy. That is what separates a good recommendation from a keyword search.

Storefront inside the channel

The product appears where the conversation already is, in the format the channel handles best.

Recommendations that understand the request

“A dress for a beach wedding, under 400 reais, that arrives by Friday.” The agent separates hard constraints from preferences, and only recommends what exists and arrives in time.

Occasion and style
Price range
Size and variant
Delivery time

Try on

Virtual try-on happens in the conversation, before the decision, so the customer doesn't have to imagine the fit.

From the first message to the result.

01
The catalogue is prepared
Attributes enriched, taxonomy applied, price and stock synced with your platform.
02
The request is interpreted
The agent understands what the customer wants, phrased the way they would say it to a salesperson.
03
The storefront appears
A few good options, each with the reason it is there, in the native Meta catalogue, a carousel or a webview.
04
The choice becomes a cart
Once the item is chosen, a.Commerce takes over: cart, payment, confirmation.

On the channels you already have, plugged into what you already use.

Channels

InstagramWeb

Connects with

Shopify

VTEX

Nuvemshop

Magento

Salesforce Commerce Cloud

In your operation, the next day.

People who don't know the product's name still find the product.
Recommendations start accounting for delivery time and stock, not just relevance.
The whole assortment becomes reachable through conversation, not just what sits on the store's home page.

Behind every one of them

The anatomy of an agent.

a.Store is not a prompt with access to your store. It is seven parts, and it is the presence of all of them that separates an agent from a chatbot.

Memory

What is already known about the customer: what comes from CRM and CDP integrations, and what they told you themselves in earlier conversations, on any channel.

Knowledge base

Your policies, your catalogue and your documents as the source of every answer. With no source, the agent does not answer: it escalates.

Tools

The actions it can take in your systems: look up an order, build a cart, open a return, issue a charge.

Skills

Your operation's procedures, written the way the agent must follow them, not as a generic support prompt.

Harness

The environment that runs the agent, with limits, permissions and a trail for every step. This is where governance actually happens.

Reasoning

Thinking before acting: understanding which case this is before deciding which tool to use.

Evals

The tests that say whether a change made the agent better or worse, run on every change, and not once at rollout.

And what you see afterwards.

AI Assistant

Ask about your operation in plain language and get the cut you need, without building a report.

AI Report

The report written by the AI: what changed in the period and what explains the change.

Revenue

Revenue attributed to each agent and to the conversation that produced it.

Support Report

Support volume, resolution and escalation, broken down by reason.

FAQ diagnostics

What customers ask that your knowledge base still doesn't answer: the content backlog, found for you.

Questions that always come up.

Do I have to rewrite my catalogue descriptions?: No. Enrichment is part of the agent: attributes and descriptions are completed and organised into a taxonomy from the catalogue exactly as it is today.
How often is stock updated?: Price and availability are queried at answer time, straight from your platform. What the customer sees is what the store has at that moment.
Is the storefront WhatsApp's, or a screen of yours?: Both exist: the native Meta catalogue and the carousel use what the channel offers; the webview is a screen of ours, for when the assortment needs more room than the channel gives.
Does it work for a large catalogue?: That is where it pays off most: a large catalogue is exactly where a site's attribute search tends to fail. The practical limit is data quality, and enrichment exists to attack that.

One system. Five agents.

They share catalogue, orders and conversation memory. What one learns, the other already knows.

See a.Store in your operation.

A live demo with our team, driven by the context of your operation, not a generic deck.

a.Store walkthrough with cases from your segment
Design of your operation's priority use case
Rollout plan and integration with your stack