Piotr Cieslak

Magic Piotr Cieslak

Unlocking efficiency and innovation through automation and AI.

Discover The Magic

Full Interview with Piotr

Full Interview with Piotr

Piotr's Amazing Song

Piotr's Amazing Song

Speaker Introduction: Piotr Cieslak

Speaker Introduction: Piotr Cieslak

Interview Highlights

Interview Highlights

Automation Workflows

Robot Navigation System

1
Start Node
2
Trigger Node (On Start)
3
Raspberry Pi Control Node
4
Navigation Algorithm Node
5
Obstacle Detection Node
6
Movement Command Node
7
Quality Control Node
8
Error Handling Node
9
Reporting Node
10
End Node

Automates the navigation of the robot throughout a defined space. Eliminates manual navigation, enhancing efficiency and ensuring smooth operations. Pain points alleviated include manual navigation efforts and inefficient movement.

Data Management for Hotel Booking System

1
Start Node
2
Trigger Node (On Schedule)
3
Data Fetch Node (API for Booking Data)
4
Processing Node (Data Cleaning)
5
Integration Node (Database Update)
6
Quality Control Node
7
Notification Node (Send Email Confirmation)
8
Log Data Node
9
Error Handling Node
10
End Node

Manages and updates hotel booking data efficiently. Reduces the likelihood of data inconsistency and improves accuracy in bookings. Pain points alleviated include data discrepancies and manual booking updates.

Drone Surveillance System

1
Start Node
2
Trigger Node (On Motion Detection)
3
Camera Feed Node
4
Image Processing Node
5
Facial Recognition Node
6
Alert Node (Notify User)
7
Movement Command Node (Activate Drone)
8
Quality Control Node
9
Error Handling Node
10
Logging Node
11
End Node

Automates surveillance and threat detection using drones. Minimizes the risk of intrusions and enhances security protocols. Pain points alleviated include manual monitoring and slow response to security events.

Machine Learning Model Training

1
Start Node
2
Trigger Node (On New Data Availability)
3
Data Fetch Node (Collect Training Data)
4
Processing Node (Data Preprocessing)
5
Training Node (Model Training)
6
Evaluation Node (Model Performance Assessment)
7
Quality Control Node
8
Notification Node (Inform Results)
9
Version Control Node
10
End Node

Facilitates the training of machine learning models based on new data. Enhances the model's accuracy, ensuring improvements in predictions and outputs. Pain points alleviated include manual data pipeline management and model update delays.