OmniSight: Autonomous Disruption Radar

Inspiration Corporate espionage and competitive strategy are painfully manual. Strategy teams spend days scouring Google for pricing changes, hunting down job postings to guess roadmaps, and listening to hours of earning calls. We realized that by orchestrating the right AI APIs, we could compress 40 hours of manual intelligence gathering into 60 seconds. What it does OmniSight is an enterprise-grade "Disruption Radar." Type in a competitor's name and attach a raw audio file (like a keynote speech). An autonomous AI agent then: Conducts live, parallel web searches for hiring velocity and pricing changes. Deep-scrapes competitor websites to extract raw product data. Diarizes the uploaded audio to extract hidden business drivers. Synthesizes this into a dual-layered Radar Dashboard that benchmarks the competitor. How we built it Bright Data: Used to bypass captchas and scrape live data across three parallel vectors asynchronously. Speechmatics: Used for deep audio intelligence, executing speaker diarization on raw audio files. Cognee: Integrated into the agent runtime to cache competitor profiles in a knowledge graph, giving the agent historical memory. Gemini / Groq LLM: The "brain" that structures the unstructured signals into a strict JSON schema. Challenges Running deep web scrapes and audio transcription sequentially caused 5-minute load times. We solved this by using asyncio.gather to fire all Bright Data and Speechmatics requests simultaneously, cutting load times by 70%, and piping the AI's internal terminal feed directly to the UI.