Evaluating the transition from MVP to a scalable, Agentic AI architecture for startup analysis.
Initial version was a tightly coupled application with synchronous processing. While functional, it struggled with timeout issues during long LLM analysis tasks and was difficult to extend with new features.
Recognizing the need for scalability, we architected a 'Modular Monolith'. This decoupled the analysis logic from the API layer and introduced asynchronous task queues to handle intensive AI workloads.
We moved to an Agent-based approach. Specialized agents (Team, Finance, Market) now run independently, orchestrated by a central engine. This improved accuracy, error handling, and allowed for parallel processing.
Built to handle complex AI workloads with reliability and speed
A suite of focused agents (Team, Finance, Market, etc.) that specialize in analyzing specific aspects of a startup pitch deck.
Built on Celery and Redis to handle long-running AI tasks asynchronously, preventing timeouts and ensuring system stability.
WebSocket integration allows users to see detailed analysis progress in real-time as different agents complete their tasks.
A structured architectural approach that balances the simplicity of a monolith with the flexibility of microservices.
Seamless integration with existing databases (MongoDB) to fetch 'deck_metadata' and store structured agent results.
Flexible AI core capable of switching between models (Gemini 2.0, OpenAI) for optimized performance and cost.