Use cases
Find a job for your AI agent
Start with work your team already does. Each example shows the task, what Maplith contributes and where a person decides.
Handle returns according to your policySupport · The agent checks the order, the delivery date and your returns policy before it replies, and asks when the policy requires it.Read the use case →Find ads promoting products that are sold outMarketing · Compare stock and advertising data from your exports; pausing an ad is a separate, approved step.Read the use case →Research and draft your next LinkedIn postMarketing · The agent checks sources, proposes a topic and prepares a draft; a person approves every post before it goes out.Read the use case →Review dependency updates against your team's rulesDevelopment · A written rule separates the updates that meet the normal checks from the ones a person decides, on our own repository.Read the use case →Explain why a payout does not matchFinance · A proposed workflow that would trace the difference between a payout and the books before someone changes them.Read the use case →Turn sales calls into follow-up draftsSales · A proposed workflow that would turn a call summary into a draft with agreed next steps for the rep to review and send.Read the use case →
Browse by team
Each team page curates the tasks that matter to that team, with one worked example and links to the pages that explain them.
MarketingHow the AI agent a marketing team already uses stops paying for what cannot be sold and sees where the discount settings break the policy the business wrote down: each workflow a before and after, shown on fictional companies’ exports.Open the team page →Customer serviceHow the AI agent a support team already uses answers by the business’s own policy, asks before it refunds and, with live rules, tells a customer about a late parcel before they ask: each workflow a before and after, shown on fictional companies’ exports.Open the team page →Product and catalogueHow the AI agent a catalogue team already uses sees why returns climb on a product, and where the same product goes by two codes or two prices, shown on fictional companies’ exports.Open the team page →FinanceHow the AI agent a finance team already uses finds the invoice that never matched its quote, the invoice still waiting for payment and the refund nobody booked: each workflow a before and after, shown on fictional companies’ exports.Open the team page →Operations and deliveryHow the AI agent an operations team already uses sees the work that was handed out with no delivery after it, shown on fictional companies’ exports, next to a purchase order that arrived after the shelf was empty.Open the team page →Sales and CRMHow the AI agent a sales team already uses sees one customer split across several records, and a CRM that is days behind the customer, shown on fictional companies’ exports.Open the team page →People and resourcingHow the AI agent that plans the work sees the rates kept in a spreadsheet and the assignments made in chat: each workflow a before and after, shown on a fictional company’s exports.Open the team page →
Worked examples
Three fictional companies whose exported data we work through in public. Nothing in their data comes from any customer, and every figure on their pages names the file it rests on.
Maplith for e-commerceA whole web shop worked through, from sold out to a support case, on the exports of a fictional shop.Read the e-commerce example →Acme Commerce · fictionalA fictional company, a web shop selling phone and tablet accessories. Its situations are in twelve months of exported data, checked against the files by a script with no model involved. These pages say what is in the data, and publish no score for what a scan of it finds.See the example →LinguaNord ApS · fictionalA fictional company, a translation agency. The findings and processes quoted from it come from a recorded scan of its exports. There is no public map or key for it.See the example →Fjeldrev Outdoor ApS · fictionalA fictional company, a web shop selling outdoor gear. The findings quoted from it come from a recorded scan of its exports. There is no public map or key for it.See the example →
A use case explains the problem and the result. The matching guide explains the systems, access, rules and steps to set it up. See the guides →
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Bring one task from your business to a demo, and we look at what your agent would need.