An e-mailbot is a tool for automatically handling large amounts of incoming e-mails. The bot analyses and processes the content of each e-mail and performs actions based on this information. It can be adapted to suit your needs and industry, and trained via our platform. It is continuously trained and improved via the tagging and language engine (NLU).
Our solution is designed to connect and co-ordinate alongside other software and systems.
We are devoted to ensuring that the interface is as user-friendly as possible. No coding or technical background is required to have an e-mailbot.
The e-mail bot can handle large volumes of incoming e-mails at once, around the clock, without breaks.
Get things done by allowing repetitive tasks
to take care of themselves, so you can focus on other areas.
Get an overview
Gain a good and well-organised overview –
all e-mails are classified completely automatically.
We analyse and adapt
We map your needs for you.
Please contact us to book a meeting to find out how
your business can benefit from an e-mailbot.
What can an e-mailbot do?
An e-mailbot processes content and performs actions. You decide what the e-mail bot will analyse and what it should do when. Below is an overview of what it can do.
Forward The bot forwards e-mails to people or systems
Respond It can respond to any enquiry
Extract data Extracts keywords
and information from the content
Connect to API Can be connected to systems
in order to perform actions
e-mails by type
Interpret and understand Analyses your content and learns what’s what.
Identify Identifies with the help of keywords
Filter and organise Organises your e-mails and filters out junk e-mail
What happens when the bot receives an e-mail?
To be able to handle the e-mail properly, the e-mail bot analyses the content via multiple layers of understanding:
1. What type of e-mail is it?
When the e-mail bot receives an e-mail, it immediately reads what type of e-mail it is. Example: Spam, invoice or customer enquiry.
2. To which category does it belong?
The bot identifies the essence of the e-mail, e.g., the subject of the complaint.
It retrieves information via keywords and machine learning,
and understands why the e-mail has been sent. Examples: Claim, travel expenses, ad sales.
3. What intents does it contain?
We use the word intent to explain an intention or purpose.
What is the intention of the sender? What do the sentences tell us?
Through training, the bot can learn to read the meaning of sentences and determine whether the e-mail has numerous intents. Example: “I would like to subscribe to your service” “Can I receive an offer?”
What entities are included?
The bot retrieves important information from what we call ‘entities’.
The term ‘entity’ is Latin for ‘that which is’, and is a collective term for ‘unit’. Examples: City, date, name, numbers, addresses and time.
Upload existing e-mails as training data. The bot can then trains itself and learn from existing e-mails in your inbox. It is important to map out the needs – what the bot should be able to understand and what actions you want it to perform.
Training via tagging
To train the language engine, we tag the existing e-mails.
We tell the bot the type and category of the e-mail, what intents it has and what kinds of entities are specified.
Once it has been trained with a certain amount of data, the e-mail bot will be able to analyse, interpret and understand the content itself.
You determine the rules and the action flow
Choose what the bot will do with the information.
Set rules and conditions for which actions are to be performed and when.
Our solution focuses on the automation of tasks and enquiries, and has been developed specifically for integrations with other systems.
For systems both with and without API.
A comprehensive solution which includes full support and assistance.
For systems in which integrations are not desirable, we will be able to utilise RPA technology Robotic Process Automation to give the email bot access and the ability to perform tasks.
Examples: CRM and case management systems
The e-mailbot analyses and makes decisions based on a combination of rules and language engine, also known as Natural Language Understanding.
The bot understands the content and improves as it learns.
Some of our e-mailbots
Sødde for Claims Link
Sødde for the claims settlement company Claims Link
Sødde can determine whether the received e-mail is a notification or a claims case. It recognises the type of case and also considers whether enough information is available to create a case for further processing.
It operates in a common e-mail mailbox along with the other colleagues on the line, and picks, reads, and categorises e-mails. It creates cases, archives files, and also prepares the case folders for further processing.
6 digital employees for OSM, a global maritime service provider
We have automated salary payments and provided an e-mail bot, document bot and multiple RPA (Robotic Process Automation) bots that take handle salary payments and customer billing. The digital employee is composed of a group of bots that have been linked together, and functions like a human accountant.
The captain sends sailors’ deductions, overtime hours, advance requests, and the vessel’s expense documents to an e-mail address.
Combine an e-mail bot with a chatbot and a document bot
Our modules can be assembled. When they are combined in this manner, we call them a digital employee.
A digital employee can perform several types of job functions and have different roles.
A digital employee is given access to relevant computer systems and understanding about what it must do. It is then ‘taught’ using relevant data and information, readying it to take its place among its colleagues.
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