Paras Arts AI Data Agent
An AI-powered data agent that enables natural-language analysis of Paras Arts business data stored in MongoDB through a controlled tool-based architecture.
Overview
Paras Arts AI Data Agent is a restricted AI-powered assistant designed to interact with and analyze structured business data from the Paras Arts platform. The agent allows natural-language questions about data such as orders, artworks, services, testimonials, FAQs, messages, and newsletter records. Instead of giving the AI unrestricted database access, the system uses predefined tools and controlled operations to retrieve and analyze permitted information. The project combines generative AI, backend APIs, database querying, and tool-based agent architecture to demonstrate how AI agents can be safely integrated with real-world application data.
Problem
Analyzing application data directly through database queries can require technical knowledge and can become time-consuming when users need quick answers to business questions.
Solution
The Paras Arts AI Data Agent provides a natural-language interface for querying and analyzing permitted business data. A controlled tool layer sits between the AI model and MongoDB, restricting what information the agent can access and what operations it can perform.
Features
- 01Natural-language data queries
- 02AI-powered business data analysis
- 03MongoDB data integration
- 04Tool-based agent architecture
- 05Controlled database access
- 06Order data analysis
- 07Artwork data analysis
- 08Services analysis
- 09Testimonials and FAQ analysis
- 10Newsletter and message analysis
- 11FastAPI backend
- 12CORS-enabled API
- 13Restricted data exposure
- 14No arbitrary database operations
- 15No unrestricted deletion operations
- 16Structured AI responses
Technologies
Development details
The agent was developed using Python with FastAPI as the backend API layer and MongoDB as the data source. The AI model interacts with the database through a predefined set of tools rather than receiving unrestricted database access. The system separates the AI reasoning layer, API layer, database inspection, and tool functions. Access restrictions were implemented to prevent the agent from exposing sensitive administrative information, customer contact details, addresses, image URLs, or performing arbitrary database deletion and modification operations.
Key learnings
- Building AI agents with Python
- Working with Gemini models
- Agentic AI architecture
- Tool calling and function-based AI workflows
- Connecting AI systems with MongoDB
- Building APIs using FastAPI
- Designing restricted database access
- AI security and data-access boundaries
- Environment variable management
- CORS configuration
- Separating AI logic from backend services
- Working with real-world application data
- Designing safer AI-powered data assistants