Multi-LLM financial analysis platform
An end-to-end platform that combines market data, automated analysis pipelines, and several language models to produce decision-ready financial reports.
The problem
Financial research often requires analysts to collect fragmented market information, compare signals, and turn it into a consistent report. The objective was to automate the repetitive work without hiding the reasoning behind the result.
Architecture and responsibilities
Kamal designed and built the full platform, from the interactive Next.js interface to Python and Node.js services, asynchronous processing, storage, authentication, and cloud deployment.
- Orchestrated OpenAI, Claude, and Mistral models for sentiment analysis, report generation, and recommendations.
- Built multi-layer ETF and equity filters, automated data pipelines, and real-time scoring.
- Used Celery and Redis to keep long-running analysis outside the request path.
- Secured distributed services with JWT authentication and containerized the system with Docker.
Outcome
The resulting workflow reduced manual research time by more than 70% while preserving a structured, reviewable output. The project demonstrates how Kamal connects AI experimentation to a complete production-oriented product architecture.