We Built an AI That Does 80% of Podcasters' Work — Here's How
PodCastAI
AI-native podcast growth platform · planning → viral clips → distribution
📌 Why PodCastAI?
Podcasters today juggle 7+ tools: Notion (planning), Audacity (edit), Headliner (clips), Buffer (social), Spotify dashboards… it's chaotic and kills creativity.
PodCastAI unifies everything — an AI co‑pilot that plans episodes, extracts viral moments, distributes across 8+ platforms, and predicts audience growth. Built for Bharat's booming creator economy.
🇮🇳 AWS AI for Bharat Hackathon
⚡ The program
AWS + Hack2Skill launched a nationwide movement: learn generative AI (Bedrock, Lambda, SageMaker), then build. ₹40 lakh prize pool, mentorship from AWS experts. PodCastAI emerged from the building phase — designed for Indian creators, in all languages.
🎯 Our alignment
1,500+ new podcasts/month in India. Regional creators need AI that understands context. PodCastAI’s pipeline supports multilingual transcription (Hindi, Tamil, Telugu) and clip generation — exactly the “AI for Bharat” vision.
👥 Team PodCastAI
Yash Tagunde
Project Manager · DevOps Engineer
- AWS infrastructure: CloudFront, S3, Lambda, Bedrock, CloudWatch
- CI/CD · auto‑scaling · performance (FCP < 1.5s, LCP < 2.5s)
- Sprint planning, architecture roadmap, 99.9% uptime design
Tanmay Khedekar
Developer · AI/ML Engineer
- LLM integration (prompt engineering, Claude 3 on Bedrock)
- Frontend: Next.js, Tailwind, glassmorphism system
- AI pipeline: transcription → hook detection → clip rendering
🧠 Core AI modules — deep dive
📅 Planning & Launch
Defeats blank‑page paralysis: AI suggests trending topics, optimal publish dates, and guest ideas based on listener demographics. Integrated Kanban: Idea → Research → Recording → Editing → Launch.
- AI topic generator: “top 5 myths about AI in 2026” + localised versions
- Launch timeline with milestone tracking, countdown
- Engagement predictor: estimated downloads, shares, retention curves
86%
predicted reach increase if episode includes “AI tools” + “Bharat”
🎙️ Episodes Management
Central library with AI‑enriched metadata. Automatic transcription (AWS Transcribe) + chapter markers + sentiment analysis per segment. Every episode becomes searchable, quotable.
⚡ Authority Engine
After uploading an episode, the engine detects “viral hooks” — moments with emotional spikes, debate, or surprise. Automatically renders platform‑optimised clips (subtitles, sizing, captions).
- 🎬 YouTube Shorts / Instagram Reels / TikTok — one‑click export
- 📝 LinkedIn & Twitter threads from transcript highlights
- 🔬 A/B test hook variants (different captions, titles)
🌐 Distribution Hub
Stop switching between 6 platforms. Schedule and publish everything from PodCastAI. Auto‑repurpose: long‑form video → audiogram → social posts.
Unified content calendar + cross‑platform analytics (reach, engagement, follower growth).
☁️ System Architecture (AWS native)
Every component is serverless, auto‑scaling, and cost optimised.
User → CloudFront → Next.js (S3) → API Gateway → Lambda (orchestrator)
↓ ↓
Amazon Bedrock Lambda (clip generator)
↓ ↓
DynamoDB / RDS S3 (media)
↘____________________↙
CloudWatch
AWS service deep‑dive
| AWS service | Role in PodCastAI |
|---|---|
| CloudFront | Global low‑latency delivery (dashboard & media) |
| S3 | Store raw audio, generated clips, thumbnails |
| Lambda | Serverless inference triggers, orchestration |
| API Gateway | REST endpoints for frontend ↔ AI layer |
| Bedrock | LLM access (Claude, Llama) for topic/hook generation |
| Transcribe | Speech‑to‑text for podcast episodes |
| DynamoDB / RDS | User profiles, episode metadata, analytics cache |
| CloudWatch | Monitoring, latency alerts, auto‑scaling triggers |
🧬 AI pipeline (podcast → clips)
⏱️ Entire pipeline finishes in <4 min for 1h episode (parallel Step Functions).
🎨 Design system — glassmorphism · premium contrast
🎯 Primary
#BA92FF – creativity, AI, trust
Supporting
🔤 Typography
Space Grotesk (headings)
Inter — body text, very readable
✨ Micro‑interactions
- hover cards glow purple (#C7A6FF)
- AI processing skeleton wave
- smooth card transitions
📱 Mobile‑first & performance SLAs
FCP < 1.5s LCP < 2.5s
Breakpoints: 640px / 768px / 1024px / 1280px. Touch targets > 48px.
| Metric | Target |
|---|---|
| Task completion | >95% |
| System usability score | 80+ |
🚀 Roadmap: beyond hackathon
Q3 2026
Real‑time AI assistant for live recordingQ4 2026
Sponsor matching engine2027
Mobile app (React Native) + offline🎯 Conclusion: built with AI, powered by AWS
PodCastAI eliminates context switching, saving creators 10+ hours/week.
This project embodies the AWS AI for Bharat Hackathon — building scalable, inclusive AI for India's creators.
📐 Full system context (C4)
┌────────────┐ ┌──────────────┐ ┌─────────────────┐
│ Browser │ ──→ │ CloudFront │ ──→ │ Next.js (S3) │
└────────────┘ └──────────────┘ └─────────────────┘
↓ ↓
┌───────────────┐ ┌──────────────┐
│ API Gateway │ ←────── │ Lambda① │
└───────────────┘ └──────────────┘
↓ ↓
┌─────────────────┐ ┌─────────────────┐
│ Bedrock (LLM) │ │ Lambda② clip │
└─────────────────┘ └─────────────────┘
↓ ↓
┌─────────────────┐ ┌─────────────────┐
│ DynamoDB · RDS │ │ S3 media │
└─────────────────┘ └─────────────────┘
All monitored via CloudWatch · auto‑scaling · 99.9% availability target.
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