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Made & hosted in Germany
With Konfuzio
Organize a shared e-mail inbox
Robotic Process Automation (RPA) and Artificial Intelligence (AI) automate the organization of your shared team mailbox. These technologies analyze emails semantically, extract data from texts and attachments and create automatic responses, allowing emails to be processed in seconds. Relieve your employees of time-consuming routine tasks.
Customer references
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How it works
Step by step
Manage team mailbox with AI
The Konfuzio solution supports you with use cases such as digital invoice receipt, orders by e-mail and customer support requests such as address changes.
Humans support AI in automation
New workflows are trained with the help of humans. The human decides which steps are necessary for which type of email and thus supports the AI on the way to a constant & automatic improvement of accuracy.
Linking the AI of Konfuzio with RPA
Our team of experts will connect our AI with an RPA solution such as Power Automate for you. We offer the Konfuzio AI app free of charge as a custom connector. From this point on, the AI learns with every incoming email.
Organize workflows for shared mailboxes
As soon as the AI is sure which steps are necessary after training, it automatically takes over additional workflows such as the classification of emails. Any special cases or emails that cannot be assigned automatically remain in the shared mailbox for manual processing.
Konfuzio API reads emails and attachments
The Konfuzio AI understands both email texts and attachments thanks to integrated OCR technology and even makes scans machine-readable. In this image example, you can see the SmartView, which enables experts to make granular markups and comments. The DVUI, on the other hand, offers a more user-friendly display and simple navigation for the end user.
Automatic e-mail response to check the data read out
By understanding the data individualization is possible. The extracted data is transferred to a form for verification and sent directly to the sender in a response email who can now confirm the correctness or add changes with one click.
Shared e-mail inbox in practice
3 Use cases.
Organize incoming digital invoices in your email inbox
Challenge
An invoice goes through many processing stages from the moment it arrives in the email inbox until it is paid - especially in large companies. Assigning invoices to the right department and the person responsible, approval processes and transfer to ERP systems are just some of the steps involved in invoice processing. The longer this process takes, the more time it takes.
Solution
An intelligent organization of the mailbox automatically recognizes email bills or invoice attachments. By reading the content, such as the invoice number, invoice issuer and service recipient, it is determined how the invoice will be processed further. The responsible person is automatically assigned for processing and data is transferred to ERP systems. The processing time is reduced considerably by the elimination of manual activities.
Intelligently manage orders in the email inbox
Challenge
In most cases, companies process orders directly in their internal systems if they have an online store. However, they process queries about shipping, delivery or the order itself manually via the email inbox. Reading the emails, assigning them to the correct order in the CRM system and responding to the inquiry are hurdles to the fastest possible order processing.
Solution
In order to respond as quickly as possible to the order and shipping status or individual order inquiries, companies use automatic data synchronization from the email and CRM system. The automatic assignment of orders and the creation of direct responses shortens the processing time for inquiries. Refunds, changes or status inquiries can be processed as quickly as possible, which increases customer satisfaction.
Respond individually to customer inquiries in the email inbox
Challenge
Even with good business development and process changes, many European companies have a backlog of thousands of emails in central mailboxes. This volume of customer inquiries cannot be processed quickly enough due to a lack of time and human resources - especially since it continues to grow every day.
Solution
By automatically analyzing, classifying and distributing emails, you improve response times and increase the quality of customer service. Personalized consultation with customers in particular meets the expectations of outstanding support. Using the data extracted from emails and attachments, you can create automatic reply emails and respond directly and individually to customer inquiries.
FAQ
Frequently Asked Questions
01.
What role do humans play in optimizing AI-supported email organization with Konfuzio?
Humans play a crucial role in setting up and fine-tuning AI-supported processes. By providing initial guidance on what steps are required for different email types, humans help the AI to design workflows. This human interaction allows the AI to continuously learn and improve its accuracy in data processing and task automation.
02.
What are the advantages of individual customer feedback in the automated e-mail process with Konfuzio?
With the ability to transfer extracted data directly into a personalized response email, customers can quickly provide feedback by confirming or correcting the data at hand. Interactivity not only improves data quality, but also promotes greater customer loyalty and satisfaction by giving customers the feeling that their inquiries are being dealt with personally and promptly.
03.
How does Konfuzio ensure that data processing is GDPR-compliant?
Konfuzio guarantees GDPR-compliant data processing through the use of servers in Europe, with the option of hosting in Germany. The Technical Organizational Measures (TOMs) and the GDPR-compliant data processing can be found in the GTC.
04.
What resources are available to developers in the Konfuzio Developer's Guide?
In the Konfuzio Developer's Guide, developers will find the API documentation and a YouTube API tutorial, the Konfuzio SDK with sample codes and source on GitHub, and for enterprise customers the Python Konfuzio Trainer module for creating their own AI models. There is also documentation for on-prem and private cloud setups as well as a server changelog for insights into future releases. Now to the Developer Guide.