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Dashboard with KPIs and parsed emails – automatyzacja przetwarzania korespondencji z AI

95% reduction in response time to transport offers through the use of AI

OUTCOMES

Results of partnership

85% automation

Artificial intelligence automatically reads and processes 85% of incoming mail.

95% shorter
response time

The client can now quickly identify and respond to attractive transport offers.

Analytics data

Since the database is connected to BI tools, the client can keep a constant eye on current market trends.

PROJECT

Genesis and business expectations

Our client, a large company in the TSL sector, was receiving huge volumes of e-mail correspondence sent to their main inbox. They wanted to be able to sort through these messages without having to involve any employees; they understood that, in a deluge of junk mail, they might potentially miss out on an attractive transport offer.

The idea was to create an automated solution that would fish out important transport offers (along with contact details) from this mass of e-mails. In addition, the solution would then use these data to analyse current pricing trends on the spot market.

PROJECT TIME

1 month

INDUSTRY

TSL

COUNTRY

Poland

SERVICES AND SOLUTIONS

TECHNOLOGIES

Logo Amazon Web Services – chmury obliczeniowej oferującej usługi AWS.

Amazon Web Services

Logo AWS Lambda – serwerless computing w chmurze Amazon.

AWS Lambda

Logo AWS DynamoDB – szybkiej i skalowalnej bazy danych NoSQL.

AWS DynamoDB

Logo AWS EventBridge – usługi przesyłania zdarzeń i integracji aplikacji.

AWS EventBridge

Logo AWS Bedrock – usługi umożliwiającej tworzenie aplikacji z generatywną AI.

AWS Bedrock

Logo AWS SQS – usługi kolejkowania wiadomości w chmurze Amazon.

AWS SQS

Client and email list in AI system – automatyzacja przetwarzania korespondencji z AI
The client

Who have we helped?

Our client is a leading transport and logistics company with 30 years of experience in the management of the passenger, truck and light truck vehicle distribution chain in Europe and Asia.

Client and email list in AI system – automatyzacja przetwarzania korespondencji z AI
REQUIREMENTS

Business challenges

  • Managing huge volumes of unstructured correspondence: the client was receiving hundreds of e-mails every day, which were difficult to sort through manually to identify attractive transport offers.
  • Multiple languages: messages were being sent in different languages, which required the client to use advanced natural language processing (NLP) tools for content recognition and analysis.
  • Lack of communication standards: a variety of message styles and formats fomented chaos, making it even more difficult to automatically isolate crucial information.
  • High processing effectiveness: the client aimed at achieving a very high level of processing effectiveness (target: 85%) so as to minimise the need for human intervention.

The TSL industry still largely relies on email communication, which for some companies means handling vast amounts of correspondence and data for verification. A common issue is delayed responses to transport offers or losing them in the clutter of other messages.

 
Together with our client, we developed an AI-based solution that automatically reads, structures, and processes data. This allows the company to respond faster to attractive offers without having employees spend valuable time on repetitive and monotonous tasks.

ACTIONS

Project execution process

Identifying business needs during a session with the client.

AI solution with automated email analysis and KPI indicators – automatyzacja przetwarzania korespondencji z AI
Solution

Scope and delivery model

Our solution is based on artificial intelligence, which automatically processes e-mail messages to isolate transport offer information. Extracted offers, along with contact details, can then be used to draw up reports, analyse data and feed into an integrated TMS system.

 

In addition, we delivered a simple web app that allows the client to comfortably browse through the offers that are found. Users can filter them by date or sender and export any data of interest to an Excel file. If, for any reason, the process produces an error, this information will also be displayed in the app.

 

In this way, the client can quickly browse through potential transport offers and lose no time in responding to the most attractive ones.

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