Mani Chandra
Reddy

Production-grade orchestration system for multi-agent GenAI workflows with dynamic routing, error recovery, and observability.

Enterprise clients needed reliable orchestration for complex agentic workflows involving multiple LLM calls, tool usage, and human-in-the-loop steps. Existing solutions lacked proper error handling, observability, and cost controls.

Unified deployment framework enabling seamless switching between AWS Bedrock managed models and custom SageMaker endpoints based on cost, latency, and accuracy requirements.

Organizations needed flexibility to use managed Bedrock models for cost efficiency while maintaining custom fine-tuned models on SageMaker for specialized tasks. Manual switching was error-prone and required code changes.

Comprehensive evaluation framework for RAG systems that measures retrieval quality, context relevance, answer accuracy, and hallucination rates across diverse query types.

Standard RAG evaluation metrics (BLEU, ROUGE) don't capture retrieval quality, context relevance, or domain-specific accuracy. Teams lacked actionable insights to improve RAG systems.

End-to-end MLOps pipeline for GenAI models: automated training, evaluation, A/B testing, and deployment with CI/CD integration and comprehensive monitoring.

ML teams struggled with manual deployment processes, lack of reproducibility, and insufficient monitoring. Model updates required days of manual work and had high failure rates.

Enterprise workflow automation connecting n8n orchestration with Canva API for automated design generation, content personalization, and multi-channel distribution.

Marketing teams needed to generate hundreds of personalized designs for campaigns. Manual design creation was time-consuming and error-prone, limiting campaign scale and personalization.