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Led development of squawk-ai, building a real-time AI-powered trading news platform with WebSocket listeners, multilingual TTS, automated event filtering, and cloud deployment.

Led the development of an AI-powered, real-time trading news and events platform tailored for professional traders. The platform aggregates live financial news, translates it into multiple languages, and distributes it via text, audio, and social media, enabling fast and actionable insights. Managed the project end-to-end, including architecture, team coordination, technical decision-making, and cloud deployment.
The main challenge was building a real-time, AI-powered trading news platform capable of aggregating, translating, and distributing high-frequency financial news with minimal latency. To address this, I implemented WebSocket listeners for live data capture and a multilingual TTS pipeline to convert translated news into audio. Integrating AI-driven filtering and decision-making required designing robust NLP pipelines and rules to automatically determine which events should be broadcast, posted on social media, or included in reports. Cloud deployment and secure access control via Keycloak ensured scalability, reliability, and compliance with client requirements.
The platform delivers real-time, multilingual trading news and insights, enabling traders to react instantly to market events. Automated event filtering and AI-powered report generation reduced manual effort by over 70%, and the integrated AI translation eliminated the need for a specialist, lowering operational costs by approximately 60%. Accuracy improved across all reports, allowing premium users to make faster, data-driven decisions, while the Stripe subscription model supported a scalable monetization channel.