RAJ KUMAR G

An agentic AI system unifying twelve isolated clinical data sources (12-lead ECG, echocardiogram, MRI, CT, Holter monitoring, implantable device telemetry, wearables, lab values, clinical notes) into a single decision layer. It offers a foundation-model synthesis with grounding constraints tied to the source.

I implemented multi-agent orchestration using the Model Context Protocol (MCP) to enable safe, controlled tool use. It also includes a planner-executor architecture with memory across agent steps, human-in-the-loop checkpoints at high-stakes decisions, and audit-ready execution traces for every agent action.

The stack used included Python, FastAPI microservices, a React front end, PostgreSQL, an agentic orchestration layer, and cryptographically signed reproducible decision provenance. The solution demonstrates the multi-agent, multi-modal, and governance patterns that enterprise AI engineers typically specify only on paper.

This project entailed a per-series method selection across an extensible forecasting model library. LLM-based routing decides which forecasting method to apply per series based on data characteristics, seasonality, and history. It also includes multi-model orchestration with prompt engineering for the routing layer.

The solution offers a React front end for analyst-facing interaction, a FastAPI back end, and continuous online updates rather than periodic full retraining cycles. Production discipline included drift monitoring, fallback paths, and observability on every model decision.

Measured outcomes included 15 to 40% accuracy lift over single-model baselines and 70 to 85% cost reduction vs mainstream forecasting stacks. The pattern applies to healthcare cost forecasting, member utilization prediction, and care management caseload projection.

An evaluation framework with provenance, reproducibility, and defensibility. It offers an LLMOps observability and evaluation layer that audits every LLM call and external API call across an AI platform. It classifies each call by risk level (deterministic, advisory, decisive) and publishes a reproducible trust score per code path.

The system was validated on three production codebases: 28 call sites were audited with zero critical defects identified, and a 99.3% portfolio average trust score. The evaluation framework entailed discipline enterprise healthcare AI systems required for HIPAA-aligned deployment, model risk management, and FDA-style clinical decision support documentation.