# Client case studies ## Client case studies ### Fintech platform: fraud detection A payment processing company handling millions of transactions monthly needed to improve their fraud detection capabilities while controlling cloud costs. Their existing system used expensive third-party APIs that charged per transaction, resulting in monthly costs exceeding 45,000 USD. I designed and implemented a custom fraud detection model fine-tuned from a base language model using QLoRA on transaction narratives and metadata. The training infrastructure used AWS Batch with spot instances, reducing training costs by 70% compared to their initial SageMaker estimates. The inference system deployed on multi-model SageMaker endpoints handled 5,000+ requests per second with p95 latency under 100ms. The solution reduced fraud detection costs from 45,000 USD monthly to approximately 8,000 USD while improving detection accuracy by 12 percentage points. The client gained the ability to iterate on the model weekly using recent fraud patterns, something impossible with their previous vendor-locked solution. ### E-commerce: recommendation engine An online retail company with 2 million monthly active users needed personalized product recommendations but had limited ML engineering resources. Their previous vendor solution cost 12,000 USD monthly and provided little customization for their specific catalog and user behavior patterns. I implemented a fine-tuned recommendation system using LoRA adapters on top of a pretrained embedding model. The training pipeline processed their catalog data and user interaction logs to create domain-specific embeddings optimized for their product categories. The entire training process ran on spot instances, completing nightly retraining in under 2 hours at costs below 200 USD per run. The inference architecture used Lambda for sporadic traffic and auto-scaling SageMaker endpoints during peak hours. This hybrid approach reduced infrastructure costs by 65% while delivering recommendations with 18% higher click-through rates compared to the generic vendor solution. The client now controls the complete recommendation logic and iterates on improvements weekly. ### Healthcare: document processing A medical records management company needed to extract structured information from diverse clinical documents including physician notes, lab reports, and discharge summaries. Manual processing was expensive and slow, creating backlogs that impacted patient care. I developed a multi-task fine-tuned model using QDoRA that handled entity extraction, relation detection, and document classification simultaneously. Training used a carefully constructed dataset combining their proprietary documents with publicly available medical corpora. The fine-tuning approach allowed the model to learn their specific document formats while leveraging general medical knowledge. The system architecture incorporated a RAG (Retrieval-Augmented Generation) pipeline that retrieved relevant medical ontology information and similar historical cases to improve extraction accuracy. This hybrid approach combined the fine-tuned model's domain expertise with real-time knowledge retrieval, improving accuracy on rare medical terms and conditions by 23% compared to the fine-tuned model alone. The production system processed documents in batch using SageMaker Batch Transform jobs scheduled during off-peak hours to maximize spot instance availability. Processing costs dropped from approximately 8 USD per document (manual processing) to under 0.15 USD per document. Throughput increased from 200 documents daily to over 10,000 daily with higher accuracy than manual extraction. ### SaaS: multi-tenant text generation A B2B SaaS company providing content generation tools needed to serve multiple enterprise clients with customized model behavior per client. Each client required different tone, style, and domain expertise, but maintaining separate models for 50+ clients was economically infeasible. I implemented a multi-adapter architecture where a single base model shared across all clients loaded client-specific LoRA adapters at request time. The adapter switching mechanism cached frequently-used adapters in memory while loading less common adapters on demand. For clients requiring capabilities beyond the fine-tuned model, the system integrated with LLM APIs including OpenRouter, Claude, Gemini, and OpenAI through a unified interface with automatic fallback logic when specific providers experienced downtime. Infrastructure costs decreased by 85% while actually improving average latency by 22ms through better resource utilization. The architecture enabled onboarding new clients in hours rather than days by training new adapters without touching the base model. Client satisfaction improved due to more responsive model updates and better customization to their specific needs. ### Media: content moderation A digital media platform needed automated content moderation to handle growing user-generated content volumes. Their previous solution flagged excessive false positives, creating moderation queue backlogs and poor user experience. I fine-tuned a classification model using their historical moderation decisions, implementing aggressive data augmentation to handle edge cases. The model training used QLoRA to fit within a single A10G GPU, enabling rapid experimentation with different training strategies. The final model achieved 94% precision and 89% recall on their moderation guidelines, substantially better than the 78% precision of their previous system. The production deployment used Lambda for real-time moderation decisions on user posts, with automatic scaling handling traffic spikes during peak posting hours. This serverless approach eliminated idle infrastructure costs and automatically scaled to handle 10x normal traffic during viral events. Monthly moderation costs decreased from 28,000 USD to approximately 6,500 USD while processing volume doubled.