Turn business processes into reliable, measurable GenAI automation — avoiding "AI for AI’s sake."

• Hybrid Architecture & Design: Decompose workflows to strictly separate GenAI from deterministic logic, delivering containerized technical blueprints (Docker/K8s) for seamless handover.
• Quantitative Evaluation: Replace subjective "vibes" with golden datasets, synthetic test generation, and automated regression pipelines to prove reliability.
• Production Readiness: Implement safety guardrails, drift monitoring, and robust failure patterns to ensure the system evolves without breaking.

Typical Deliverables: End-to-end architecture diagrams, deployment specifications, evaluation suites, and risk/fallback strategies.

End-to-end development of production-grade ML/DL systems for text, audio, and affective computing — from experiment design and training to deployment and safe continuous improvement.

• Rigorous experiment design: modeling choices, metrics/robustness criteria, baselines & ablations, and strong hyperparameter optimization strategies
• High-quality data pipelines: preprocessing, dataset QA, audio/text quality measurement, and targeted augmentation (where appropriate)
• Reproducible model development: classical baselines → LSTM/GRU → transformer encoder(-decoder) stacks, plus tracking, checkpointing, and resume-safe training
• Production deployment & lifecycle: Docker-first serving, versioned APIs, regression gates, monitoring (drift/quality/cost), safe retraining, and rollback paths