Solutions · Marketing

Maplith for marketing

Marketing spends in one system on the strength of facts that live in others: the stock is in the shop, the policy is in a document, the spend is in the ad platform. When they disagree, nobody sees it until the money has gone. Your agent can catch it — if it knows where each fact lives, what changed and whether it may act. Maplith gives it exactly that; your agent acts in your systems with your credentials.

Build your first map, free See the workflows

Acme Commerce is a fictional web shop built for our demo: the company, its people, its customers, the device brands, the suppliers and the competing chains are all invented, and nothing in the data comes from any customer. Fjeldrev Outdoor ApS is a fictional web shop built as a test company: the company, its people and its customers are all invented, and nothing in the data comes from any customer.

The workflowsfigures from the worked examples

Sold out, and the ads keep running: DKK 5,630 · 1,599 clicks · 0 conversions in 16 days

Acme Commerce · Fjeldrev Outdoor ApS

Where marketing’s facts liveAd platformShopDiscount settingsPolicies nobody opens

Between the systems

Where it goes wrong

Two disagreements between systems that no single system shows.

Spend on what cannot be sold

The ad platform keeps paying for clicks on a product the shop has sold out. In the worked example one campaign kept spending for sixteen days on a sold-out case, with no conversion.

Acme Commerce · in its exported data

Discount settings that break the written policy

In the worked example the policy says codes may not be combined and caps the discount at 20 %. Two codes were set as combinable, and four orders stacked them to 25 %. A scan of the exports raised it as a finding, citing the policy, the settings and the orders.

Fjeldrev Outdoor ApS · raised by a scan of its exports

Workflows

What your agent does about it

The workflow below is told in full on the e-commerce page, where its worked example lives. Its last step uses live rules, which are in early access and being built now; the rest ships today.

01In the worked example

Sold out, and the ads keep running

Stock reaches zero in the shop; the ad platform keeps paying for clicks on it.

DKK 5,630 · 1,599 clicks · 0 conversions in 16 days

Workflow 01

Sold out, and the ads keep running

Stock reaches zero in the shop; the ad platform keeps paying for clicks on it.

Without Maplith

Find the stock export and the ad platform export.
Guess how their rows join; the catalogue spells seven re-listed products differently.
Read a year of daily rows to notice the gap.
Start over tomorrow.

With Maplith

One rule, written once from the map’s own names.
A signal for the product when it starts matching.
The context to act in one call: the route, the playbook if the shop keeps one, the key’s policy.
A record, so the next judging does not raise it as though nobody had looked.

Your rulesStock on hand is zero and ad spend over the last day is above zero, per product.
SignalsA rule_fired signal for the product, with the figure behind each condition.list_signals
Task contextThe verified route from ad spend to stock, the playbook if the shop keeps one, the last time, and the suggested operation.get_task_context
The action gatePause the ad set: allowed, pending a person, or denied. The agent acts with the shop’s own credentials.request_action
Record and valueWhat was done, with the spend it stopped when the agent has that figure, so the next judging does not raise it as though nobody had looked.record_action
Live rulesThe shop’s own automation reports that a product reached zero; Maplith turns it into the same signal within minutes.early access

Worked example: Acme Commerce · told in full on Maplith for e-commerce

The full workflow, step by step

Without Maplith

Stock sits in the shop and the reporting database, spend sits in the ad platform, and nobody owns the space between. In the worked example a leather case was out of stock from 2026-07-14 to 2026-07-29, and one campaign spent DKK 5,630 on it across those sixteen days — 1,599 clicks, not one conversion — while the shop’s own marketing rules say to pause ads the day stock reaches zero. An agent without a map has to find both exports, guess how the ad platform’s rows join the stock (the catalogue spells seven re-listed products differently), read a year of daily rows to notice, and start over tomorrow.

With Maplith

Your rules
An admin writes the rule once, from the map’s own names: stock on hand is zero and ad spend over the last day is above zero — per product. A product is reached only through joins Maplith tested against the rows. The rule is judged when a scan finishes and, for a connected system, daily or weekly between scans; its first judging is its baseline, so a product already sold out then raises nothing, and a product fires when it starts matching.
Signals
list_signals returns a rule_fired signal for that product, with the figure behind each condition.
Task context
get_task_context returns the product, the verified route from ad spend to stock, the marketing rules if the shop keeps them as a playbook, what was done the last time, and the operation the rule suggests — pause the ad set — with the key’s policy for it.
The action gate
request_action to pause that ad set: allowed, pending a person, or denied. An allowed answer can carry a grant good once for that ad set; the agent pauses it in the ad platform with the shop’s own credentials.
Record and value
record_action records what was done — with the spend the ad platform says it stopped, when the agent has that figure — so the next judging does not raise it as though nobody had looked, and What you got counts it.
Live rules · early access
Instead of waiting for the next judging, the shop’s own automation — the kind a shop platform already runs when stock changes — reports only the observation that a product reached zero, and Maplith turns it into the same signal and task context within minutes.