Problem
Creators and experts often suffer from audience fragmentation. A book on Amazon, a podcast on Spotify, and a scattered WordPress blog mean disjointed analytics, lost engagement, and zero cohesion.
Constraint mapping
Dr. Anne Hussain Platform
A unified digital platform consolidating a book store, podcast network, and personal blog, layered with an AI-powered RAG system over her entire content corpus.
Product screens
Consolidated Storefront
A single hub for purchasing books and accessing premium courses.
AI Chat Assistant
RAG-powered chat strictly grounded in her published podcast and blog corpus.
Podcast Network Sync
Automated ingestion pipeline mapping new RSS episodes directly to the vector DB.
AI Chat Assistant
RAG-powered chat strictly grounded in her published podcast and blog corpus.
Consolidated Storefront
A single hub for purchasing books and accessing premium courses.
Podcast Network Sync
Automated ingestion pipeline mapping new RSS episodes directly to the vector DB.
Execution Snapshot
The strongest signal first, then the operating context around it.
Build Narrative
A clean story from constraint to shipped outcome.
01
Build
I engineered a unified web platform that integrates a bookstore, a podcast network, and a blog, and topped it off with an AI assistant that uses RAG to answer user questions using only her published content.
System design
Outcome
A highly differentiated expert platform that monetizes attention effectively and acts as a powerful portfolio piece for future consultancy work.
Production outcome
Framing
Defining the product and the operating constraints.
The goal was to move away from third-party renting (Medium, Spotify, Amazon) and build owned infrastructure. By centralizing the data layer in Prisma and Supabase, I built robust sync scripts. Every time she publishes a new blog or podcast, it is instantly vectorized and made available to her RAG assistant.
Systems Index
Next.js
TypeScript
OpenAI
RAG
Stripe
Prisma
Key features in scope
Consolidated Custom Storefront
Automated RSS Podcast Network Sync
RAG-Powered Custom Knowledge AI Bot
Premium Content Gates via Clerk & Stripe
Role and product posture
Role: End-to-end full-stack developer: product strategy, UI/UX design, Next.js application, podcast syncing algorithms, and the AI embeddings pipeline.
Category: Digital Platform / AI
Engineering
Building the core system and choosing where to be opinionated.
I engineered a unified web platform that integrates a bookstore, a podcast network, and a blog, and topped it off with an AI assistant that uses RAG to answer user questions using only her published content.
Systems Index
Next.js
TypeScript
Tailwind CSS v4
Framer Motion
Clerk
Next.js API Routes
Prisma
Stripe
Architecture choices
Next.js App Router for dynamic routing and edge-optimized fetching
Supabase pgvector database for storing AI embeddings of all posts and transcripts
OpenAI embeddings and generation endpoints for the RAG chatbot interface
Stripe and custom checkout logic for selling digital/physical books seamlessly
Clerk integration for premium subscription/member access routing
Key decisions
Automated the podcast syncing pipeline so new episodes on RSS immediately index into the vector DB.
Created a unified design system that makes different content types (books, blogs, podcasts) feel cohesive.
Bounded the AI assistant strictly to her content corpus to prevent hallucinations on medical/expert advice.
Hardening
Turning the build into something resilient enough to matter.
A highly differentiated expert platform that monetizes attention effectively and acts as a powerful portfolio piece for future consultancy work.
Systems Index
Showcases the ability to drive real-world business outcomes (network gigs, referrals) through engineering.
Demonstrates deep competence tying external APIs (Stripe, RSS, OpenAI) into a single cohesive UI.
Highlights a strong product-sense focus on audience consolidation and monetization.
Results after shipping
Centralized all traffic streams, drastically reducing bounce rates.
The AI assistant feature stood out, leading to referrals from other professionals wanting digital twins.
The polished unified podcast interface directly contributed to her securing a larger podcast network deal.
Constraints
The AI assistant needed guaranteed source-truth grounding to avoid dispensing wildly incorrect medical logic.
Podcast ingestion needed to be automated, parsing large audio files without massive latency.
The storefront had to handle physical product shipping states and digital downloads.
Lessons
What the build taught me.
01
RAG over an individual's specific corpus is one of the highest ROI AI features you can build for creators.
02
Unified platforms generate a compounding effect on traffic that fragmented silos cannot match.
03
Investing in bespoke platform design is a measurable signal of quality to professional networks.
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