6 weeks to go from beta to a fully compliant, AI-powered iOS launch on AWS

Short-form video platforms live or die on two things: safety and relevance. Without automated video moderation, App Store approval is out of reach. Without personalized discovery, users churn before they ever come back.

By building a production-ready video moderation and recommendation architecture on AWS, Chaa Chingg launched on iOS with enterprise-grade content safety and intelligent feed personalization, built in weeks.

Customer overview 

Chaa Chingg is a bootstrapped short-form video and social networking platform built to compete in the creator economy. Incorporated in Palo Alto and founded by Micky Rai, the platform runs on a fully AWS-native stack and targets a US-first audience looking for a fresh, community-driven alternative to established video platforms.

Preparing for a public iOS App Store launch, Chaa Chingg engaged Applify to design and implement the backend AI capabilities required for compliance and content discovery, without a platform rewrite and without a large ML team.

"Applify helped us turn a compliance requirement into a genuine product advantage. The video moderation and recommendation systems they built on AWS are the foundation everything else will be built on, this is the beginning of something great."

Micky Rai Founder & CEO, The Chaa Chingg Company

Challenges | Launching a video platform without moderation or personalization is not an option

Apple's App Store guidelines require demonstrable content safety controls before a video platform can go live. At the same time, user retention on short-form video depends heavily on the quality of the content feed from the very first session.

Chaa Chingg was in beta with 1,500-2,000 videos and no automated pipeline to handle either requirement at launch scale.

Text-only moderation with no video or audio coverage 

The existing moderation system could only analyze captions and descriptions. Video frames and audio tracks went completely unchecked, leaving significant compliance risk ahead of App Store submission.

A recommendation engine that couldn't learn 

Feed personalization relied on manual category tagging and positive-only interaction signals processed through a static Python script. There was no mechanism to understand content context, handle new users, or improve over time.

No semantic understanding of content 

The platform had no way to extract meaning from video captions or transcribed audio. Content was understood only at the surface level, limiting both moderation accuracy and recommendation quality.

Speed-to-launch pressure with a small team 

As a bootstrapped startup, Chaa Chingg had no runway for a multi-month ML build. The solution needed to be production-ready, cost-efficient, and extensible, without requiring a rebuild of the existing backend.

Solution | Production-ready video moderation and recommendations, built on AWS 

Applify designed and deployed a fully integrated AI/ML architecture within Chaa Chingg's existing AWS environment. The AWS content moderation solution required no platform rewrite and was built to be operational at launch, with a clear path to evolve as the user base grows.

Multi-modal video moderation pipeline 

A fully asynchronous video moderation pipeline was integrated directly into the existing upload flow, covering every content type, “visual, audio, and text”, without impacting user experience.

Visual content moderation 

Amazon Rekognition automatically scans every uploaded video and image for explicit, unsafe, or policy-violating visual content, flagging violations before content reaches any feed.

Audio moderation via transcription 

Amazon Transcribe extracts audio from uploaded videos and converts it to text. That text is then passed to Amazon Comprehend for analysis, closing the audio compliance gap entirely.

Deep contextual text analysis 

Amazon Comprehend analyzes captions, descriptions, hashtags, and transcribed audio for unsafe language, sentiment, and policy violations. For nuanced cases - coded language, contextual inappropriateness, subtle hate speech, Amazon Bedrock foundation models provide semantic understanding that goes beyond keyword matching.

Workflow orchestration with AWS Step Functions 

The full video moderation sequence, “visual, audio, and text”, is coordinated through AWS Step Functions in an asynchronous, retry-capable workflow. Results, confidence scores, and processing status are stored persistently in DynamoDB for compliance auditing and feed filtering.

Hybrid recommendation engine 

A recommendation system was implemented that improves feed relevance from day one while preserving the stability of the existing backend.

Semantic content understanding 

Amazon Bedrock processes video metadata, captions, and transcribed content to generate rich semantic embeddings - capturing themes, topics, and emotional tone. This allows the recommendation engine to understand content similarity at a conceptual level, not just by manual tags.

Collaborative filtering with Amazon Personalize 

Amazon Personalize was integrated in hybrid mode alongside the existing Python recommendation logic. It handles collaborative filtering based on user interactions, “likes, comments, shares, and saves”, and improves automatically as interaction data accumulates.

Cold-start resolution for new users 

For users with no interaction history, Amazon Bedrock analyzes early engagement signals and generates contextually relevant recommendations based on semantic content understanding, significantly improving first-session experience.

AWS-native infrastructure built for scale 

The solution runs on Amazon ECS with automated scaling policies designed to handle launch traffic and beyond. The architecture is sized for 1 million active users at year one and up to 5 million by year three, with an estimated monthly AWS cost of approximately $20,800 at scale.

Success metrics 

By combining AWS content moderation services with Amazon Bedrock's generative capabilities, Chaa Chingg achieved full App Store readiness and a meaningfully better content experience, without a platform rewrite and without building an ML team.

• 75% reduction in manual moderation effort: The automated AWS video moderation pipeline handles the vast majority of content review, freeing the team to focus on community management rather than frame-by-frame inspection.
• 40% higher engagement on recommended content: The Bedrock-enhanced recommendation system outperformed the baseline Python engine on content engagement from launch.
• 28% improvement in new user retention: Better cold-start recommendations during first sessions reduced early churn by delivering relevant content before sufficient interaction history was available.
• 3 months faster to launch: By leveraging AWS managed services and Bedrock foundation models rather than building custom ML systems, Chaa Chingg avoided the time and cost of training and maintaining proprietary models.
• Production-ready in 6 weeks: End-to-end implementation across architecture review, video moderation, and recommendation systems, delivered within a two-phase, six-week sprint.