StreamPulse - AI-powered livestream

Turn Livestream Chaos into Revenue

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Inspiration

Livestream selling is booming, but the workflow is exhausting and highly fragmented:

  • Pre-stream: Sellers waste hours manually researching market trends, competitor pricing, and writing scripts.
  • Mid-stream: Pure chaos. Managing hundreds of live comments is overwhelming, causing high-intent buyers to get buried under spam.
  • Post-stream: Sellers lose thousands of dollars in "unconverted value" because they can't manually track or follow up with engaged leads who dropped off before checkout. We built StreamPulse—an end-to-end AI Copilot that automates this entire pipeline. Our goal is simple: let sellers just turn on the camera and focus on closing sales. ## What it does StreamPulse is a comprehensive AI Copilot for livestream e-commerce, broken down into 3 phases:
  • Pre-Stream (Data & Scripting): Powered by the Tinyfish API, the system automatically crawls real-time market trends, competitor data, and audience preferences. It then uses this fresh data to generate dynamic, tailored sales scripts.
  • Mid-Stream (AI Real-time Engine): Classifies comment intents, auto-replies to repetitive questions, tracks live audience sentiment to adapt the seller's script on the fly, and deploys a "Secret DM Chaser" to send hidden coupons to high-intent buyers.
  • Post-Stream (Analytics): Delivers deep analytics, specifically calculating and providing actionable strategies to recover "Unconverted Value" (leads who engaged but didn't buy). ## How we built it We engineered a robust, low-latency microservices architecture:
  • Frontend: React & Tailwind CSS for the real-time studio dashboard.
  • Backend: JavaScript (NestJS) handling the core business logic and orchestration.
  • AI & Data Engine: We utilized the Tinyfish API for real-time web crawling and market data ingestion. This fresh data feeds directly into our custom RAG pipeline. To power the generative AI, we integrated the OpenRouter API, allowing us to seamlessly route requests to the most optimal LLMs based on the task (e.g., low-latency models for live intent classification, and high-reasoning models for script generation). Post-live analytics are processed via Python Pandas. ## Challenges we ran into Handling the sheer volume of concurrent live comments without rate-limiting or crashing the LLM pipeline was our biggest hurdle. We had to heavily rely on Redis Queues to throttle, filter out spam, and prioritize high-intent messages. Additionally, integrating the Tinyfish API to fetch relevant, noise-free market data required rigorous prompt engineering to ensure our RAG pipeline generated accurate sales hooks. ## Accomplishments that we're proud of We successfully built a fully functional, end-to-end AI pipeline within the hackathon timeframe. We are especially proud of our "AI Real-time Engine" which dynamically adapts the seller's script based on live audience sentiment. Furthermore, translating raw chat data into a tangible business metric—the "Unconverted Value"—proves the immense commercial viability of our product. ## What we learned We learned that in real-time e-commerce AI, context and speed are everything. Marrying high-performance in-memory stores (Redis) with real-time crawling (Tinyfish) and advanced GenAI reasoning is a game-changer. We also realized how powerful UI/UX is when translating complex AI backend processes into an intuitive "Copilot" dashboard for non-technical sellers. ## What's next for StreamPulse - AI comment management Direct integrations with major platforms like TikTok Shop and Facebook Live. Our ultimate vision is to evolve StreamPulse into a fully autonomous AI Avatar Co-host with voice synthesis, capable of running 24/7 sales streams and automatically retargeting "unconverted" leads post-stream.
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