AI-driven tools are becoming increasingly prevalent across various industries, streamlining processes from simple graphic design and translations to advanced document, email, and database analysis. In this article, we will present a practical business application of an AI assistant in action.

AI Agents have a wide range of applications, and their full potential is still being discovered. The main advantages of AI-powered assistants include:

1. Automating Routine Processes

AI agents can handle repetitive tasks such as customer inquiries, document analysis, and data management. By automating these processes, businesses can reduce operational costs and improve efficiency.

 

2. Personalized Customer Interactions

By analyzing data, AI agents can provide personalized recommendations and tailored offers, enhancing customer engagement and improving overall user experience.

 

3. Speed and Availability

AI operates 24/7, delivering instant responses and real-time support. This is particularly valuable in industries that require quick reaction times, such as e-commerce, finance, and logistics.

 

4. Advanced Data Analysis

AI-powered agents can process vast amounts of data in a short time, identifying patterns and correlations that support better business decision-making.

 

5. Optimizing Decision-Making Processes

With predictive modeling, AI assists in demand forecasting, risk management, and supply chain optimization, helping organizations make more informed decisions.

 

6. Seamless Integration with Existing Systems

Modern AI solutions can be easily integrated into existing ERP, CRM, and analytics platforms, enhancing their capabilities and improving overall system efficiency.

A Practical Example of AI Agent Use in the Transport Industry

AI agents can be applied across various industries, including banking, sales, and human resource management. In this text, we will focus on a freight forwarding company that handles anywhere from a few to dozens of shipments daily.

 

 

Freight forwarders deal with constant communication and the verification of numerous documents. Each of these tasks takes time—a resource that is often in short supply—making errors more likely when the workload is high.

 

 

How can time management be improved? By automating repetitive and predictable tasks. This is where an AI Agent comes in. Here’s an example of an AI assistant we developed, powered by Google’s Large Language Model, Gemini.

 

One possible application is the following scenario:

 

 

A freight forwarder receives an email that should include an insurance policy along with proof of payment. The AI Agent automatically, without needing to be prompted, checks whether the email contains the required attachments. If they are included, it proceeds to verify the following details:

In the Insurance Policy:

• • • Policy number
    • Insurance period and whether it is currently valid
    • Insured party details, including tax identification number and address
    • Bank account number for premium payment

In the Payment Confirmation:

• • • Payment reference
    • Amount
    • Bank account number
    • Payment date
    • Whether the transfer corresponds to the submitted policy (e.g., based on the reference, account number)

The AI Agent then transfers the extracted data into a designated Excel file, which is continuously updated. The data file can be formatted accordingly, for example, by highlighting entries in red where the insurance policy is invalid or the payment has not been verified. 

In this simple way, instead of searching through their inbox for the right emails, the freight forwarder can check the Excel file to see if the documents have been received from a specific sender and whether they are correct. This saves a significant amount of time and ensures data accuracy.

 

There are many ways to further develop our AI Assistant. It can be integrated with other tools, such as Slack or other communication platforms, to send notifications about missing documents or generate automated email responses. An AI-powered agent can be tailored to the specific needs of a company, a department, or even an individual role.

Do you want to explore the possibilities of AI Agents?​

Schedule a meeting. We’d be happy to discuss the possibilities.

SCHEDULE A MEETING

Artykuł Meet Your Personal AI Agent: A Case Study for a Freight Forwarding Company pochodzi z serwisu Inero Software - Software Consulting.

At Interizon Day 2023 Waldemar Kołub co- founder of Inero Software presented a success story about using Neuronal Networks to optimize the production process.

Farm Frites Poland is leading producer of potato products, supplying products to, among others, one of the largest fast-food restaurants in the world. The company has been operating for over 30 years, and its headquarters is located in Lębork. The modern machinery park consists of three production lines.It employs over 270 people, and the quantity of potatoes used for production over a year exceeds 250,000 tons.

“The problem our client had was to estimate the production yield based on the parameters of the raw input material, so how much of the final product we can obtain from the given amount of the source material, or, to put it in layman’s terms, how much fries we can get from a potato. And it turns out that it is not an easy question to answer.” –  Waldemar Korłub mentioned at his presentation.

Challenges for the development od the company was:

DATA OF THE RAW MATERIAL:

• Contracting for raw material supplies is planned several months in advance and constitutes a significant element of the enterprise’s manufacturing process and pricing policy.
• Estimation of yield, i.e., the difference between the weight of raw material and the final product expressed in percentages (EPY), allows for adjusting the raw material price to the expected result.

PRODUCTION OPTIMIZATION:

• Identification of raw material characteristics that most significantly affect the yield (EPY)?
• Indicate what yield can be expected based on the given raw material?

“So there are a lot of parameters that describe the source material. All in all, there are over 40 different physical traits that need to be taken into account that might influence the final. Yield with the set of parameters. We perform exploratory data analysis to find correlations between the parameters and the final results. By doing that, we are able to identify the parameters which influence the final results the most. Also, we looked for correlations between the parameters which allowed for dimensionality reduction in the problem space and, as a result, it allows for speeding up the machine learning process.”

Machine learning is method used for “feeding” machines with data so that based on this data and their own errors, they learn the rules to solve a given problem. It can be assumed that this is a role reversal, where previously, by defining the rules and utilizing the data, we sought answers.

“With a well-defined set of parameters, we could move on to model research. We’ve tested several different machine learning approaches, including regression models, decision trees, support vector machines, and neural networks. In the end, it turned out that the best results were achieved with neural networks, with the mean error at around 1.2% for the validation data.“

LEARNING

Learning is based on providing training examples (historical data) in the form of:

• Average raw material per run: Green, Rusty, OWG, Color (actual values)
• Average yield per run: EPY (e.g., 45%)

The network minimizes the error between the actual value of EPY (45%) and what it predicts itself (e.g., 54%). The metric used is MSE (Mean Squared Error).

IMPLEMENTATION AND VALIDATION

The model was implemented in the client’s infrastructure. Thanks to the introduction of the solution, it was possible to minimize the forecast error to just 3%. This means that accurate estimates can already be made at the stage of contracting raw material supplies.

“This preliminary model research was conducted on a subset of the dataset, and the model that was decided to be the best was later on deployed in our client’s infrastructure and we performed the full training using the complete set of production data from historical production runs. The final model was deployed to production with the mean squared error below 3% for the real-world production runs.“

This solution was developed according to our build, operate, transfer model. “We build the solution using our knowledge and expertise in the first step. Then we helped with setting up the infrastructure and the processes to meet the operational requirements, and finally, the product was transferred to the client, who receives the full ownership of the solution. With this process, we are not only trying to create solutions for individual problems but rather to create a platform for the ongoing development of innovations in the environment of our clients.”

At Inero Software, we believe that software goes beyond just the technical; it’s also about people, business, and delivering on sustainable long-term objectives. By leveraging the BOT model, we establish a lasting innovation framework within our client’s operations, enabling them to set and achieve long-term business goals through the power of innovation.

Waldemar Korłub, Eng. PhD

CO-FOUNDER INERO SOFTWARE

waldemar.korlub@inero.pl

Artykuł Case study: Deploying an AI-based solution at Farm Frites Poland pochodzi z serwisu Inero Software - Software Consulting.