Case Studies

🎯 Problem

A customer support team was drowning in repetitive WhatsApp messages, spending 4+ hours daily on questions that could be answered automatically.

🔍 Approach

Integrated WhatsApp Business API with a FastAPI backend and OpenAI GPT-4, building a context-aware conversational agent with Redis-backed session memory.

🏗️ Architecture

Webhook receives WhatsApp messages → FastAPI processes → Redis retrieves conversation context → GPT-4 generates response → WhatsApp API sends reply. All in under 2 seconds.

⚠️ Challenges

• •Maintaining conversation context across sessions
• •Staying under 2-second response latency
• •Handling WhatsApp rate limits gracefully
• •Message deduplication to prevent double responses

📚 Lessons Learned

• ✓Redis session caching is non-negotiable for context-aware chatbots
• ✓Webhook idempotency keys prevent duplicate processing
• ✓GPT-4 system prompts need exhaustive testing with real user inputs
• ✓Fallback to human agent must be seamless and fast

🎯 Problem

A business needed accurate Turkish speech transcription with speaker identification and automatic meeting summaries, but off-the-shelf tools had poor Turkish accuracy.

🔍 Approach

Built a streaming transcription pipeline using OpenAI Whisper fine-tuned approach with custom post-processing NLP chain for Turkish language accuracy.

🏗️ Architecture

Audio stream → chunking layer → Redis queue → Whisper inference workers → NLP post-processing (diarization, sentiment, summary) → WebSocket push to client.

⚠️ Challenges

• •Real-time processing with Whisper latency constraints
• •Speaker diarization in Turkish with limited training data
• •GPU resource management for cost efficiency
• •WebSocket reconnection and stream recovery

📚 Lessons Learned

• ✓Audio preprocessing (noise reduction, normalization) is as important as the model
• ✓Chunk overlap strategy is critical for accurate sentence boundaries
• ✓GPU spot instances with failover reduce costs by 60%
• ✓Speaker embeddings need continuous learning from user feedback

🎯 Problem

A company had 12+ manual data entry processes between CRM, ERP, email systems, and communication tools, consuming 20+ person-hours per week.

🔍 Approach

Built a visual workflow automation platform with 50+ service connectors, enabling business teams to create automations without engineering involvement.

🏗️ Architecture

React workflow editor → Node.js execution engine → JSON DAG workflow storage → distributed worker pool → execution logs and monitoring dashboard.

⚠️ Challenges

• •Reliable retry logic for long-running multi-step workflows
• •Rate limiting across 50+ different external API providers
• •Schema evolution without breaking existing workflows
• •Real-time execution visibility for non-technical users

📚 Lessons Learned

• ✓Event-driven architecture with dead letter queues handles failures gracefully
• ✓Visual error indicators are more valuable than detailed logs for business users
• ✓Idempotent workflow steps prevent data duplication on retries
• ✓A well-designed connector SDK accelerates adding new integrations 10x