ContentCompass
Overview
ContentCompass is a data-driven trend and competitive intelligence platform built to speed up media and content production. It helps a content team see which topics are rising fast, understand what competitors are covering, and intelligently reuse existing video/document archives against those trends.
What it does
- Tracks trends in real time — pulls Google-Trends-style interest data and scans RSS feeds for news and agenda signals
- Analyzes competitors — examines which headlines and topic tags similar outlets are using, and flags where competitors are concentrating coverage
- Searches your archive semantically — matches existing content against rising trends by topic meaning, not just keywords
- Generates production recommendations — combines trend momentum, archive matches and competitive gaps into editorial decision support: suggested headlines, A/B title variations and target platform
- Visualizes everything on a live dashboard — trend list, new alerts, archive-match cards, a competitor activity panel, and ROI / views / estimated revenue indicators
What it analyzes
- Trend data: which topics and keywords are gaining popularity fast
- Competitor content: what similar outlets are publishing and where they concentrate
- Archive content: how well past pieces fit newly rising trends
- Publishing opportunities: which trends existing content could be quickly repurposed for
- Performance forecasts: potential views and revenue from repurposed content
Who it helps
News editors, social media content teams, video production planners, media agencies and content operations managers — anyone who needs to turn “what’s trending right now” into a production decision.
How I built it
The backend runs on FastAPI with live updates pushed to the dashboard over WebSocket, so the interface refreshes without a page reload. The project is organized into a main.py that serves the API, the WebSocket connection and static files; a services layer with separate modules for trend detection, competitor analysis, archive search and recommendation generation; and a frontend (index.html, app.js, styles.css) that renders the live dashboard. The whole app starts with a single command: python run.py.
Tech Stack
Python, FastAPI, WebSocket, Google Trends-style data, RSS, semantic search, HTML/CSS/JavaScript
A Closer Look
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