Problem

A county in NY faced challenges in managing their bidding process for procurement of goods and services. The existing process was manual and time-consuming, resulting in delayed procurement and increased costs. Moreover, there needed to be a way to track the performance of vendors and assess the quality of products and services procured by the county.

To address these challenges, the county government upgraded its bidding process by implementing an AI-based bidding software that would automate the process and ensure that the best vendors were selected for procurement.

Challenge

The main challenge faced by Torcue Digital in this project was migrating the existing bidding process from multiple legacy platforms like Asp.Net, Java, and AS400 to a new mobile and Tab's first AI cloud platform. In addition, the project involved migrating a large amount of data and integrating multiple systems into a single platform.

Another challenge was developing an AI engine to choose the best bidding and blocklist vendors with a history of delivering bad projects. In addition, the AI engine needed to be highly accurate and learn from past bidding data to make better decisions in the future.

Torcue Solution

To address the challenges faced by the county government, Torcue Digital proposed a solution that involved the development of AI-based bidding software that would automate the entire procurement process.

The development process involved using Agile methodologies, with regular sprints and feedback from the county government. This ensured that the software development process was efficient and effective, and the county government had complete visibility into the development process.

The first step in the project was to migrate the existing bidding process from multiple legacy platforms to a new mobile and Tab's first AI cloud platform. To achieve this, Torcue Digital created a software architecture diagram that showed the migration process and new development.The  diagram consisted of multiple layers, including the user interface layer, the application layer, the data layer, and the AI layer.

The result was an AI-based bidding software that automated the entire procurement process for the county government in NY. The software was able to select the best vendors for procurement, resulting in improved quality of products and services. Moreover, the software could blocklist vendors with a history of delivering poor-quality products or services, ensuring that the county only worked with the best vendors.

The user interface layer provided an easy-to-use interface for county staff to manage the bidding process. The application layer handled the bidding process, while the data layer was responsible for storing and retrieving data from the cloud-based database.

The AI layer was the most critical part of the software architecture diagram, as it was responsible for automating the bidding process and selecting the best vendors for procurement. To achieve this, Torcue Digital developed an AI engine that analyzed bidding data and learned from past bidding patterns to make better decisions in the future. The AI engine also could blocklist vendors with a history of delivering poor-quality products or services.

Conclusion

In conclusion, Torcue Digital successfully developed an AI-based bidding software for the county government in NY, which addressed the challenges faced by the county in managing its procurement process. The software was able to automate the bidding process, resulting in improved efficiency and cost savings for the country. 

Before the implementation of the AI-based Bidding Software, the bidding process for projects in the county was highly manual and prone to errors and corruption. Vendors would submit bids via paper or email, which county officials would manually review. This process was time-consuming and prone to human error, with the potential for corruption and favoritism in awarding contracts.

The project's success was due to Torcue Digital's expertise in AI-based software development and its commitment to delivering the best solutions for its customers. The use of Agile methodologies ensured that the development process was efficient and effective, and the legacy platforms were migrated to the new cloud-based platform using a microservices architecture, with each legacy platform being migrated to a separate microservice. The microservices communicate with each other through APIs.

With the new software, the bidding process is fully automated and transparent. Vendors submit their bids through the software, which automatically evaluates the bids based on pre-defined criteria and selects the best one. In addition, the AI engine ensures no human intervention in the selection process, reducing the risk of corruption.The software also maintains a blocklist of vendors with a history of poor performance, ensuring that only high-quality vendors are awarded contracts.

The new platform uses a mobile and tab-first approach, with a responsive user interface that adapts to different device sizes. The user interface uses React and Redux, with Bootstrap and Material UI components for styling.

This method not only saves taxpayers money by avoiding costly project failures but also helps to maintain trust in the county's procurement process by ensuring fair and objective vendor selection.

The backend uses Node.js and Express, with a MongoDB database for storing bid data and vendor information. The AI engine is built using Python and TensorFlow, with bid data fed into the engine to train it on what constitutes a good or bad bid. The engine then uses this information to automatically select the best bid and blocklist vendors with a history of poor performance.

Overall, implementing AI-based Bidding Software has resulted in a more efficient, transparent, and fair bidding process, helping save taxpayers money and reduce corruption in awarding contracts.

Overall, the architecture is designed to be scalable and resilient, with multiple instances of each microservice running behind a load balancer to ensure high availability. Microservices also allow for easier maintenance and upgrades, as each service can be updated independently without affecting the rest of the system.