Tag Efficiency
Automatic Invoice Processing by AI
Our fifth case study is about our AI powered Automatic Invoice Processing. It addresses a critical need in the building construction industry, particularly in the realm of financial and accounting operations. This case study highlights how we transformed a time-consuming manual process into a streamlined, automated solution.
Case Study Details:
About the Client:
The customer is a leading solution and service provider in the building construction industry. Their service offerings include financial and accounting back-office operations, specifically data entry and processing of supplier invoices into a Financial ERP system.
Business Challenge:
After Optical Character Recognition (OCR) processing, the task of assigning cost elements and accounts to supplier invoices was previously a manual process, requiring detailed analysis of descriptions. This labor-intensive task was prone to errors, and posed challenges in terms of efficiency and scalability. Accurate cost assignments are essential for gaining visibility into project costs and cost breakdowns.
Solution:
We implemented a Natural Language Processing (NLP)-based recommendation system designed to extract article information from the invoice descriptions and recommend the appropriate account to be assigned. The solution leveraged Generative AI models from Azure OpenAI and utilized Vector databases to automate the task.
This AI-driven approach not only improved efficiency but also helped reduce manual errors in account assignments.
Tools & Technology:
For our AI powered Automatic Invoice Processing solution we used:
- LLM (Large Language Models)
- NLP
- Azure OpenAI
- Feedback loops implementation
- EC2, FastAPI, Python, React JS
- S3, SQL Server
Results:
- Optimized supply chain processes, leading to greater operational efficiency.
- Reduced costs and minimized waste, ensuring more accurate financial records.
- Increased revenues and profitability by streamlining the invoicing process.
At Pratham Software GmbH, we specialize in creating AI-driven solutions that tackle industry-specific challenges, automate processes, and boost profitability. This is just another example of how our AI expertise can transform your back-office operations and make your business more efficient.
Stay tuned for more innovative case studies as we continue to demonstrate how AI can be harnessed to bring tangible business value. Interested in transforming your financial operations with AI? 💡 Contact us today to explore how Pratham Software GmbH can help streamline your business processes.
More Case Studies
Apart from today’s case study about Train Rolling Inspection & Alerting by AI please check:
Train Rolling Inspection & Alerting by AI
Here is part 4 of our ongoing AI case studies. Pratham Software GmbH is thrilled to showcase our next AI-driven solution, Train Rolling Inspection & Alerting by AI. Our expertise continues to power innovations across various industries, and today we focus on a groundbreaking project in the railway sector. In this case study, we partnered with the world’s largest railway network to develop a cutting-edge solution for improving train safety and operational efficiency through automation.
Case Study Details:
About the Client:
The customer is the largest railway network in the world, operating thousands of trains daily. The Train Rolling Safety Evaluation (TRSE) system plays a critical role in ensuring both passenger safety and asset security. Traditionally, this has been a manual operation involving visual inspection to detect faults in moving trains near stations, prompting corrective actions.
Business Need:
With the rollout of faster trains like Vande Bharat, detecting faults manually was becoming increasingly impractical. As train speeds increased, the need for automatic fault detection became essential to maintain safety and efficiency.
Solution:
We designed an innovative solution using track-side cameras and advanced computer vision models to automatically collect and analyze visual data, detecting faults in real-time and raising alerts instantly.
Software tools
A Proof of Concept (POC) was successfully carried out to demonstrate the solution’s effectiveness, and based on its success, a pilot program is now planned.
The solution was powered by:
- AWS Kinesis Stream
- AWS Kinesis Firehose
- AWS S3
- AWS SageMaker
- AWS Ground Truth
- Rolo Flow, Yolo, MXNet, TensorFlow
Results
- Improved passenger safety through real-time fault detection.
- Reduced travel time, ensuring trains operate efficiently.
- Reduced maintenance time due to early detection of issues, allowing for proactive action.
At Pratham Software GmbH, we are dedicated to delivering AI-driven solutions that tackle complex industry challenges and drive innovation. Our AI expertise is helping businesses enhance safety, streamline operations, and unlock new efficiencies.
Stay tuned for more case studies as we continue to highlight how AI can reshape industries. Is your business ready to embrace the future with AI? 💡 Get in touch with us to explore how Pratham Software GmbH can help you leverage AI for operational excellence. Apart from today’s case study about Train Rolling Inspection & Alerting by AI please check our case study 1 about Automatic Sampling of BIM Description and case study 2 about Loan Default Early Warning and case study 3 about a centralized Chatbot with Azure OpenAI
