Nearly 80% of federal IT budget is spent sustaining systems built decades ago.

Dense government RFPs required manual cross-referencing across regulatory and technical domains, with heavy SME reliance creating bottlenecks, quality inconsistencies, and missed deadlines. We built an LLM + RAG engine trained on past submissions to extract requirements and draft compliant responses automatically, applying NLP parsing to map mandatory evaluation criteria to historical proposal evidence, and deployed it on a fully hosted GCP platform with full source-evidence traceability for audit review.

Fragmented data systems across the state network lacked a unified platform for population health analytics, disease surveillance, and reporting, and legacy infrastructure limited the state's ability to respond to public health events. We unified state-wide reporting into a centralized platform with a standardized ingestion, transformation, and analytics layer, engineered automated dashboards for real-time surveillance, and modernized digital service portals for healthcare partners.

Manual intake of procurement documentation and citizen applications was creating severe administrative backlogs, and compliance reviews were slow, periodic, and lacked a clear audit trail. We deployed a no-code, multi-agent framework with configurable workflows for procurement, compliance, and multilingual citizen services, embedding human-in-the-loop controls at configurable confidence thresholds and audit logging that captures every agent input, output, and confidence score.