Illustration of a processing approach that can be adapted to your organisation and existing tools.
Email remains a primary channel for receiving requests, orders, and information in most organisations. Incoming messages must be read, understood, classified, and routed to the right person or system.
When done manually, this work consumes valuable time and generates routing errors or oversights. The daily email volume in departments serving as a single entry point — sales contact, support, sales administration — quickly becomes difficult to manage manually. AI-powered email processing automates this triage, reducing response times and eliminating routing mistakes.
Email is a universal communication tool, but it was not designed for workflow management. Each message arrives in a single inbox, mixing requests of very different natures — orders, inquiries, complaints, support requests — with no imposed structure.
The lack of standardisation makes automated processing difficult. Useful information (order number, request type, urgency) is not always clearly identifiable. Attachments transit without being linked to the relevant file or order, complicating downstream management.
Large language models bring a unique capability to email processing: they understand the intent and content of messages, not just keywords. An LLM can determine whether an email is an order, a complaint, or a request for quotation based on full context understanding.
This approach adapts to varied writing styles without requiring predefined templates. The system learns to recognise request types, extract key data (order references, contact details, amounts), and determine urgency and routing — all from the message content. As new request types emerge, the system can be updated with new examples rather than new rules.
The email processing pipeline operates automatically on incoming messages:
1. Email capture: messages are retrieved from one or more monitored mailboxes via IMAP or API 2. Content analysis: the system analyses the body, subject, sender, and attachments to understand the request 3. Classification: the email is categorised by type (order, inquiry, complaint, support), urgency, and department 4. Data extraction: relevant information is extracted — order numbers, customer references, amounts, product codes 5. Routing and recording: the email is forwarded to the appropriate person or team, and extracted data is recorded in the CRM or ticketing system 6. Acknowledgment: an automatic acknowledgment or reply can be sent if configured
Uncertain emails can be routed to a human supervisor for manual classification, ensuring no request is lost.
AI email processing addresses many operational scenarios:
Deploying email processing requires attention to several factors. Email diversity is the main challenge — each correspondent writes differently, and the system must handle this variety reliably.
Organisations using AI email processing report significant operational gains:
An effective email processing solution combines several technologies:
Q: Can the system process email attachments? R: Yes, attachments can be analysed in addition to the message body. Attachment processing is configured according to the types of files received and the information to be extracted.
Q: How does the system determine the appropriate recipient? R: Routing can be based on multiple criteria: message content (request type), sender (customer, supplier, prospect), product concerned, or urgency level. Routing rules are defined according to the company's organisation and can include escalation paths.
Q: Can the system automatically reply to emails? R: Depending on needs, the system can generate acknowledgements of receipt, automatic replies for simple requests, or forward the request to the appropriate department. The scope of automatic replies is defined with the organisation.
Q: Does the system learn from manual corrections? R: Yes, feedback mechanisms can be implemented so that when a human reclassifies an email, the system learns from the correction, improving future classification accuracy.
To explore complementary approaches, see: - Invoice automation — for processing invoice emails and attachments - Document classification — for sorting email attachments by type - Data extraction — for extracting structured data from email content and attachments - Knowledge base — for making email-based knowledge searchable across the organisation
Every company has its own processes, constraints and tools. The examples presented on this site serve to illustrate what can be envisioned in different contexts.
