Automate orders—even with free text, attachments, and unclear product details
automaited processes orders from emails, PDFs, Excel files, scans, and images, maps unclear details to the right items in context, and escalates only real exceptions to your team.
The effort is not in capturing data—it is in understanding the order
Orders rarely arrive neatly structured. Customers order by email, in attachments, with old SKUs, inconsistent labels, or references to previous jobs. The manual work is in mapping, checking, and deciding—not in typing.
Old, incorrect, or missing product numbers
Orders in free text or straight in the email body
Inconsistent customer and product naming
Manual back-and-forth, clarifications, and ERP cleanup
How automaited processes orders with domain accuracy
Understand the intake
The agent reads orders from emails, attachments, PDFs, Excel files, scans, and images and identifies which pieces of information belong together.
Match customers and context
Names, email addresses, customer IDs, references, history, and open cases are reconciled—even with different spellings or incomplete data.
Map items in context
Old, missing, or wrong line items are resolved using master data, descriptions, variant logic, and order history—so you know not just what the document says, but which product was meant.
Validate and hand off
Quantities, units, prices, variants, and rules are checked. Clear cases move forward automatically; unclear ones are handed off cleanly for review.
With automaited, edge cases become routine
automaited does not only read document text—it also uses master data, order history, rules, tolerances, and system context. That way it evaluates not just what came in, but what was meant in a business sense.
Customer orders with an outdated SKU
The agent recognizes legacy numbers and maps them to the current item using master data and change history.
The order is only in the email body
Even without an attachment or form, order content from the email text is recognized and processed in a structured way.
“Same as last time”
Connected order history pulls in prior cases and finds the right reference.
Variants described in words
Phrases like “the large model in blue” are resolved via variant logic and master data.
Interpret quantities and units
Inputs like “2 pallets” or “500 units in 10-packs” are converted to the correct order quantity.
Customer or reference data inconsistent
Different spellings, shorthand, or outdated customer numbers are detected and mapped correctly.
Fill gaps from context
When details are missing, the agent fills them from master data, past orders, or stored customer rules.
Prepare or send follow-up questions
When clarification is needed, the agent drafts a follow-up—and can send it to the customer if you want.
Customer orders with an outdated SKU
The agent recognizes legacy numbers and maps them to the current item using master data and change history.
The order is only in the email body
Even without an attachment or form, order content from the email text is recognized and processed in a structured way.
“Same as last time”
Connected order history pulls in prior cases and finds the right reference.
Variants described in words
Phrases like “the large model in blue” are resolved via variant logic and master data.
Interpret quantities and units
Inputs like “2 pallets” or “500 units in 10-packs” are converted to the correct order quantity.
Customer or reference data inconsistent
Different spellings, shorthand, or outdated customer numbers are detected and mapped correctly.
Fill gaps from context
When details are missing, the agent fills them from master data, past orders, or stored customer rules.
Prepare or send follow-up questions
When clarification is needed, the agent drafts a follow-up—and can send it to the customer if you want.
Customer orders with an outdated SKU
The agent recognizes legacy numbers and maps them to the current item using master data and change history.
The order is only in the email body
Even without an attachment or form, order content from the email text is recognized and processed in a structured way.
“Same as last time”
Connected order history pulls in prior cases and finds the right reference.
Variants described in words
Phrases like “the large model in blue” are resolved via variant logic and master data.
Interpret quantities and units
Inputs like “2 pallets” or “500 units in 10-packs” are converted to the correct order quantity.
Customer or reference data inconsistent
Different spellings, shorthand, or outdated customer numbers are detected and mapped correctly.
Fill gaps from context
When details are missing, the agent fills them from master data, past orders, or stored customer rules.
Prepare or send follow-up questions
When clarification is needed, the agent drafts a follow-up—and can send it to the customer if you want.
Customer orders with an outdated SKU
The agent recognizes legacy numbers and maps them to the current item using master data and change history.
The order is only in the email body
Even without an attachment or form, order content from the email text is recognized and processed in a structured way.
“Same as last time”
Connected order history pulls in prior cases and finds the right reference.
Variants described in words
Phrases like “the large model in blue” are resolved via variant logic and master data.
Interpret quantities and units
Inputs like “2 pallets” or “500 units in 10-packs” are converted to the correct order quantity.
Customer or reference data inconsistent
Different spellings, shorthand, or outdated customer numbers are detected and mapped correctly.
Fill gaps from context
When details are missing, the agent fills them from master data, past orders, or stored customer rules.
Prepare or send follow-up questions
When clarification is needed, the agent drafts a follow-up—and can send it to the customer if you want.
Your team only reviews what still really needs clarification
Not every case should be decided automatically. When a mapping is ambiguous or a requirement cannot be met, the case is passed to a person with all relevant context.
Frequently asked questions
More than 300 companies already rely on automaited.
Why businesses choose us
Our Trusted AI Agents empower businesses to automate their workflows in the most efficient way. Our partners see significant gains in efficiency, accuracy, and customer satisfaction. Plus, we are a great team to work with.
automaited allowed us to make operational processes up to 70% more efficient.
Tao Tao
COO, GetYourGuide
The efficient implementation not only sped up our operations but also heightened accuracy and scalability, markedly improving client satisfaction without straining our resources.
Sascha Schneider
Strategic Projects, Roland Assistance
A Prime Example of showing that Hybrid Intelligence is looking at Machine Intelligence and Human Intelligence at the same time.
Prof.dr.ir. Wil van der Aalst
Chief Scientist, Celonis
