Marketing automation built on business data

Updated October 2, 2026

Marketing automation that can be tested

Marketing teams often see campaigns separately from catalogues, orders and stock. Reports are assembled by hand and the next action is based on an incomplete picture. Automation helps when it connects a specific signal to a specific decision: for example, flagging an advertised item that is out of stock or comparing campaign spend with orders.

We start with one use case and agree on its data source, decision owner and measurable outcome. If the data conflicts, we find the cause first and only then speed up content work.

Workflows we can connect

Catalogue, prices and stock

We identify the current product record and how changes reach a site, advertising and customer communications. We define what happens when an item is unavailable or prices conflict. Only then do we automate transitions with a clear source of truth.

Campaigns and outcomes

We connect activity, spend, enquiries and orders in a chain the team can understand. Impressions are not a substitute for sales. When the data is insufficient, we state which conclusions cannot be drawn.

Content and approval

An AI assistant can draft copy, segments or suggested actions. Brand rules, legal constraints and publication remain with the human team. Approval sits inside the workflow, before anything is published.

How we launch the first use case

We discuss a decision the team makes repeatedly and the time it consumes. We check the available catalogue, order and campaign data. Then we define what should become faster, more accurate or more transparent. A limited pilot is tested with people before deciding whether to integrate it into daily work.

For stores, we are building our own product, Sotish: an AI marketer prepares promotions, ads and mailings from the store's data, and the owner approves each launch. Custom development suits a distinct process.

What matters before implementation

The team needs data permissions, update rules and a decision owner. Duplicate orders, cancellations, returns and delayed data transfers must be tested. Otherwise automation can spread a bad conclusion very quickly. Sometimes systems analysis or team training is a better first step than a new platform.

Frequently asked questions

Is generative AI always necessary?

No. A rule, an integration and a useful report may solve the task. We add AI when it addresses a specific problem and its output can be checked.

Which integrations are available for stores in other markets?

It depends on the market and the store's systems. We work in English and Russian with teams across markets. Integration availability and data requirements are assessed separately for each project.

Describe one recurring marketing workflow and the systems involved: hello@majento.ai.