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Introduction:
A recommendation engine, also known as a recommendation system, utilizes machine learning algorithms to analyze user behavior and historical data to make personalized suggestions. These suggestions can range from product recommendations on e-commerce platforms to content recommendations on streaming services. For example, in the context of AP invoice automation, a recommendation engine can significantly reduce the manual effort required to select the GL code for non-PO invoices.
AP Automation Use case: Recommendation for GL Code Prediction:
In the invoice creation process, users often select GL codes based on historical data such as vendor names, addresses, and item information. However, accessing and searching for this historical data in the ERP system can be time-consuming. Here, a recommendation engine can easily go through the data and suggest options to the user in real-time, saving a ton of time.
Easy Configuration with Kanverse.ai:
To build a recommendation engine using Kanverse.ai, you don't have to be an expert in AI or machine learning; you just have to configure it. If we consider the GL code recommendation example, all you need to do is go to the setup and create a new recommendation. Add all the attributes that you consider for selecting the GL code, for example, Vendor name, Location, Item description, and select GL code as your prediction attribute.
Simple Data Upload:
Kanverse.ai provides a user-friendly UI where you can upload all your historical data in the form of Excel or CSV files. This data forms the foundation for the recommendation engine's predictions. Once the data is uploaded, the recommendation engine is ready to provide real-time suggestions.
Seamless Integration:
Integrating the recommendation engine into your existing system is a breeze with Kanverse.ai's REST channel. You can easily fetch recommendations from the recommendation engine and display them within your application. This seamless integration ensures that users receive timely and relevant GL code suggestions as they process non-PO invoices. If you are already using Kanverse.ai for your AP invoice automation, implementing the recommendation engine is even easier. You just have to add a simple rule to allow users to show GL recommendations. This user-friendly setup ensures a smooth transition and enhances the overall automation experience.
Feedback Loop for Continuous Improvement:
As users interact with the recommendation engine and select GL codes, their feedback becomes valuable for refining the system further. KAnverse.ai allows you to easily capture this feedback through REST APIs, which can be used to update and enhance the recommendation engine. This continuous feedback loop ensures that the engine evolves and improves its predictions over time. If you are already using KAnverse.ai for your AP invoice automation, you just have to add simple rule.
Conclusion:
Incorporating a recommendation engine into your AP invoice automation process can revolutionize the way GL codes are predicted for non-PO invoices. With Kanverse.ai's user-friendly interface, effortless integration, and data-driven predictions, your team can save time and effort while ensuring more accurate and efficient invoice processing. Embrace the power of recommendation engines and unlock the full potential of your AP automation journey with KAnverse.ai.
Author:
Adesh Patel
Head of Engineering, Founding member, Kanverse.ai