AI Integration for 'AssetRegister' Web-Tool
Budget: ₹1,500 – ₹12,500 INR
I want to introduce AI in our in-house web-based tool called 'AssetRegister'. AssetRegister is a data-driven tool that integrate with multiple sources of different categories viz - devices, users, purchases etc. - and then compare all the sources based on the common primary key for the same category and then provides a list view showing which primary key record is available in what all sources and do this kind of work.
We're using the following -
Spring Boot microservices (for Backend)
React frontend (for Frontend)
MS SQL (for database)
We need AI-Developer to help integrate with Springboot services.
Users for these applications will be organisations and their own teams (<50 users).
Now - what AI can do for our users is -
1. Deep analysis of this list/comparative-view and also the individual datasources - on various parameters and KPI's that it thinks are relevant - and if the KPI provided is not relevant, the users can share their feedback back to it and it improves its analysis further. The analysis is better to be in the form of some interactive charts or dashboards.
2. Provides an interactive chat session that can help users to interact with the data what is all present in the tool - in the very natural languge.
3. The AI should also be able to provide recommendations on the data and all the improvement areas from data and the integration or any other points of view.
4. The AI may also look through the internet and provide some additional data information / metadata which otherwise is not available in any of the connected datasource.
What other detials would you need, we can chat.
We're using the following -
Spring Boot microservices (for Backend)
React frontend (for Frontend)
MS SQL (for database)
We need AI-Developer to help integrate with Springboot services.
Users for these applications will be organisations and their own teams (<50 users).
Now - what AI can do for our users is -
1. Deep analysis of this list/comparative-view and also the individual datasources - on various parameters and KPI's that it thinks are relevant - and if the KPI provided is not relevant, the users can share their feedback back to it and it improves its analysis further. The analysis is better to be in the form of some interactive charts or dashboards.
2. Provides an interactive chat session that can help users to interact with the data what is all present in the tool - in the very natural languge.
3. The AI should also be able to provide recommendations on the data and all the improvement areas from data and the integration or any other points of view.
4. The AI may also look through the internet and provide some additional data information / metadata which otherwise is not available in any of the connected datasource.
What other detials would you need, we can chat.