Web-Media Platform
Built a comprehensive web-media platform with CRM integration, video conferencing, and analytics capabilities, growing from junior to mid-level engineer throughout the engagement
Overview
A web-media platform that combined CRM functionality, video conferencing, presentation hosting, analytics dashboards, and email campaign management into a unified solution for media professionals and content creators.
Problem
Media professionals were juggling multiple disconnected tools for client management, video meetings, content hosting, and audience analytics. The lack of integration meant manual data transfer between systems, inconsistent audience insights, and an inability to connect content engagement metrics back to specific client relationships in the CRM.
Constraints
- AWS-centric infrastructure with strict cost management requirements for a growing startup
- Video and audio content required reliable transcription for accessibility and searchability
- CRM integration needed to support bidirectional data sync without creating data consistency issues
- Platform needed to handle concurrent video conferences while maintaining acceptable quality
Approach
We built the platform as a Spring Boot application deployed on AWS, leveraging managed services wherever possible to minimize operational overhead. AWS Transcribe handled automatic transcription of video and audio content, S3 provided scalable media storage, and RDS hosted the relational data. The CRM integration was designed as a loosely coupled sync mechanism with conflict resolution to handle bidirectional updates.
Key Decisions
Used AWS Transcribe for automated video and audio transcription
Building an in-house transcription service was far beyond our team's scope and expertise. AWS Transcribe provided accurate, scalable transcription with support for multiple languages and reasonable pricing for our volume.
- Google Cloud Speech-to-Text API
- Open-source speech recognition with Mozilla DeepSpeech
Adopted AWS Lambda for event-driven processing of media uploads and transcription jobs
Media processing workloads were inherently bursty. Lambda eliminated the need to maintain dedicated processing servers and provided natural scaling during upload spikes, with costs directly proportional to actual usage.
- Dedicated EC2 instances with auto-scaling groups
- AWS ECS Fargate tasks triggered by S3 events
Docker-based local development environment mirroring the AWS deployment
With 7 developers working on different features, we needed consistent development environments. Docker Compose replicated the core services locally, reducing environment-related bugs and onboarding time for new team members.
- Direct local installation of all dependencies
- Shared remote development environments
Tech Stack
- Java 8-11
- Spring Boot
- Docker
- JDBC Template
- AWS S3
- AWS RDS
- AWS Lambda
- AWS Transcribe
Result & Impact
- 50,000+ videos and presentations hostedMedia Files Processed
- 94% average accuracy across supported languagesTranscription Accuracy
- Processing 200,000+ emails per monthEmail Campaign Delivery
- Scaled from 500 to 5,000 active users during my tenurePlatform Growth
The unified platform eliminated the tool-switching overhead that media professionals experienced daily. Analytics that previously required manual data collection across 3-4 tools became available in real-time dashboards. The automatic transcription feature made video content searchable and accessible, which became an unexpected differentiator in the market.
Learnings
- AWS managed services significantly reduce operational burden for small teams, but vendor lock-in is a real concern that should be mitigated with clean abstraction layers
- Bidirectional CRM sync is inherently complex. Designing a clear conflict resolution strategy upfront saved us from numerous data consistency issues later
- Growing from junior to mid-level on a project taught me the value of asking for code reviews early and often, rather than building in isolation
Technical Deep Dive
The CRM integration was the most architecturally interesting component of the platform. Rather than building a tight coupling between our system and the external CRM, we implemented a sync engine that operated on an event-sourced model. Every change in our system or the CRM generated an event, and a reconciliation service processed these events to determine which system held the authoritative version of each field. Conflict resolution rules were configurable per field type: for example, contact information changes in the CRM always took precedence, while engagement metrics from our platform were considered authoritative. This approach allowed us to maintain data consistency without requiring real-time locking between the two systems.
The media processing pipeline leveraged AWS Lambda heavily. When a user uploaded a video to S3, an event notification triggered a Lambda function that extracted metadata, generated thumbnails at multiple resolutions, and queued a transcription job with AWS Transcribe. A second Lambda function handled transcription completion callbacks, parsing the results into a searchable format and indexing them alongside the video metadata. This serverless approach kept our processing costs proportional to actual usage, which was critical for the startup’s budget constraints, and it scaled effortlessly during periods of heavy upload activity.
Working on this project over two years as I progressed from a junior to mid-level developer gave me a broad perspective on the full development lifecycle. I started with simpler tasks like building REST endpoints and writing JDBC Template queries, and gradually took on more complex responsibilities including the Lambda-based processing pipeline and Docker-based development environment setup. The email campaign module, which I owned toward the end of my tenure, taught me about the practical challenges of bulk email delivery, including bounce handling, throttling, and deliverability monitoring.