The Challenge
Content teams spend hours manually researching what performs and why. The founder wanted a platform that automates this: analyze existing content libraries, detect patterns in high-performing content, track trending topics across the industry, and generate complete strategies with specific recommendations, formats, and publishing schedules.
What I Built
Five interconnected AI modules: an onboarding flow that profiles the user's goals and audience, AI-generated strategy blueprints tailored to the user's niche, a trend engine with real-time data collection that identifies emerging topics early, pattern analysis that surfaces what works across large content libraries, and a style analyzer that extracts the user's unique voice from their existing content.
Technical Approach
The platform uses Next.js with server-side rendering for fast page loads, Supabase for authentication and real-time data, and multiple AI models for different analysis tasks. The trend engine collects and processes data from multiple sources, scoring topics by relevance, timeliness, and audience fit. Strategy outputs are generated using multi-step AI pipelines that consider user history, audience data, and current trends.
Iterating on AI Quality
AI accuracy was the biggest challenge. Early versions produced generic, surface-level output. Through rapid iteration with the founder's feedback, I refined the prompts, added context layers, and built a multi-format recommendation system. The platform went from producing basic suggestions to generating specific, actionable plans that users could execute immediately.
Outcome
Full platform launched publicly on schedule. The founder successfully onboarded early users and later brought on a technical co-founder to take over ongoing development. The codebase was clean enough for a seamless handover. The project demonstrated a clear MVP pattern: build fast, iterate on quality, ship on time, hand over cleanly.
Tech stack
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