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

🎯 Problem

Field and location-based records were hard to interpret because they lived in disconnected files, lists, and notes. Teams could store data, but they could not easily see regional patterns, compare locations, or understand which area needed attention first.

🔍 Approach

Designed a CBS platform around a map-first workflow: location records, region filters, detail panels, reporting logic, and admin-managed content were brought into one clean operational dashboard.

🏗️ Architecture

Interactive map UI → region and layer filters → location detail panel → MongoDB-backed records → Next.js API routes → reporting-ready data model. GeoJSON-compatible structures keep the system ready for new layers and external GIS integrations.

⚠️ Challenges

• •Making spatial data useful without overwhelming non-technical users
• •Designing a data model that can grow with new map layers and record types
• •Balancing map interaction, admin editing, and reporting needs in one interface
• •Keeping the product simple while preserving enough structure for future GIS analysis

📚 Lessons Learned

• ✓A strong CBS product is not just a map; it is a decision system built around location context
• ✓Spatial data modeling improves both backend discipline and frontend product thinking
• ✓Clear filters, detail panels, and reporting flows make complex field data feel trustworthy
• ✓This work sharpened my ability to translate messy real-world operations into a polished full-stack product

🎯 Problem

Saha ve lokasyon bazli kayitlar farkli dosyalarda, listelerde ve notlarda daginik oldugu icin yorumlanmasi zordu. Veri tutulabiliyordu ama bolgesel egilimleri gormek, lokasyonlari karsilastirmak ve hangi alanin once ele alinmasi gerektigini anlamak pratik degildi.

🔍 Approach

CBS sistemini harita merkezli bir is akisi olarak tasarladim. Konumsal kayitlar, bolge filtreleri, detay panelleri, raporlama mantigi ve admin tarafindan yonetilebilir icerik tek bir operasyon panelinde birlestirildi.

🏗️ Architecture

Interaktif harita arayuzu → bolge ve katman filtreleri → lokasyon detay paneli → MongoDB destekli kayitlar → Next.js API route'lari → raporlamaya hazir veri modeli. GeoJSON uyumlu kurgu, yeni katmanlar ve dis CBS entegrasyonlari icin sistemi hazir hale getiriyor.

⚠️ Challenges

• •Konumsal veriyi teknik olmayan kullanicilar icin de rahat okunur hale getirmek
• •Yeni harita katmanlari ve kayit tipleriyle buyuyebilecek veri modeli tasarlamak
• •Harita etkilesimi, admin duzenleme ve raporlama ihtiyaclarini ayni deneyimde dengelemek
• •Urunu sade tutarken ilerideki CBS analizleri icin yeterli mimari derinligi korumak

📚 Lessons Learned

• ✓Güçlü bir CBS ürünü sadece harita değildir; lokasyon bağlamıyla karar aldıran bir sistemdir
• ✓Konumsal veri modelleme hem backend disiplinini hem de frontend ürün bakışını güçlendirir
• ✓Net filtreler, detay panelleri ve raporlama akışı karmaşık saha verisini güvenilir hissettirir
• ✓Bu çalışma, dağınık gerçek dünya operasyonlarını parlatılmış bir full-stack ürüne dönüştürme yeteneğimi çok iyi gösteriyor