MACHINE LEARNING MODEL For the Diagnosis of Lung caner - MAYO CLINIC
We implemented a machine learning system to predict the clonal status of lung cancer samples. This was implemented in a CLIA enviroment.
Mate pair Illumina sequecing to create structural chromosomal variants. These variants were used by the model to make the prediction of the clonasl status.
Challenges and objectives
- Determining the clonal status of lung cancer samples can be extremely costly and lently process
Solution
To implement a Machine learning algorithm that can speed up the process to diagnosis.
Results
- A fully operational machine learning pipeline was implemeted that went from raw samples to diagnosis automatically
Automation of routine tasks in finance
Introducing a robotic processor to automate routine tasks in the finance department saved up to 30% of employees time.
Within the "Automation of Routine Tasks in Finance" project, our company implemented cutting-edge artificial intelligence technologies to enhance the efficiency of financial processes at a major financial institution.
Challenges and objectives
- Effectively automate routine financial operations to reduce human intervention.
- Increase accuracy in transaction processing and minimize the risk of financial errors.
- Reduce time spent on performing routine tasks.
Solution
We developed and implemented a machine learning-based automation system for processing routine financial operations, such as invoices, statements, and internal transactions.
Results
- Reduced time spent on routine tasks by 30%, allowing financial department employees to focus on more strategic aspects of their work.
- Increased accuracy in transaction processing by 25%, minimizing the possibility of financial errors.
- Improved overall efficiency of financial management through the optimization of time and resources.
Improving customer service in e-commerce
Implementing an AI-powered chatbot reduced response time to customer inquiries and increased satisfaction levels by 25%.
The project "Improving Customer Service in E-Commerce" was initiated to implement cutting-edge artificial intelligence technologies aimed at enhancing customer service levels in a major online retail store.
Challenges and objectives
- Improve responsiveness to customer inquiries.
- Optimize order processing and address potential delivery issues.
- Increase customer satisfaction and encourage repeat purchases.
Solution
We implemented a chatbot system with natural language processing capabilities for instant responses to customer queries and order processing.
Results
- Reduced customer response time by 40%, enhancing the overall service experience.
- Increased customer satisfaction levels by 25%.
- Improved conversion rates and boosted repeat purchases by 20%.