Case Studies & Deconstructed Projects.

Problem: Multiple RPA bots in revenue cycle with no governance, high error rates, and manual overrides.

What we did: Re-designed workflows as governed agentic automations using Zapier + LLMs, with human-in-the-loop.

Result: Reduced manual corrections by ~35% and provided audit-ready logs for compliance.

Problem: Data science team building models in silos with no shared infrastructure or governance framework.

What we did: Conducted comprehensive assessment across data, models, governance, and team capabilities.

Result: Delivered prioritized roadmap that unified fragmented AI initiatives under shared governance.

Problem: ML models deployed manually with no monitoring, versioning, or rollback capabilities.

What we did: Implemented lightweight MLOps stack with CI/CD, model registry, and production monitoring.

Result: Reduced deployment time from days to hours with full audit trail and drift detection.

Problem: Claims processors overwhelmed with volume, inconsistent pre-screening, and compliance gaps.

What we did: Deployed AI agents for initial claims triage with human review for edge cases.

Result: Improved pre-screening throughput by ~50% while maintaining compliance standards.

Problem: Physicians spending hours on documentation with generic AI tools that weren't compliant.

What we did: Fine-tuned domain-specific LLM with HIPAA-aware architecture and audit logging.

Result: Reduced documentation time by ~25% with fully compliant, auditable AI assistance.

Problem: Growing AI usage with no centralized governance, risk assessment, or policy framework.

What we did: Established governance framework, risk register, and ongoing monitoring as a service.

Result: Proactive AI risk management with quarterly reviews and continuous compliance monitoring.

Problem: Prior auth denials consuming 20+ staff hours per week with inconsistent submission quality.

What we did: Deployed AI agents to pull payer-specific criteria, pre-fill forms, and flag edge cases for human review.

Result: Reduced auth processing time by ~60% and denial rates by ~30% within 90 days.

Problem: Fragmented EDC exports, manual reconciliation, and 48-hour lag before data was queryable.

What we did: Built a streaming ingestion pipeline from 3 EDC systems into Unity Catalog with automated reconciliation.

Result: Reduced data availability lag from 48 hours to under 2 hours with full lineage and audit trail.

Problem: Legal team spending 12+ hours per contract cycle on repetitive redline review and clause lookup.

What we did: Implemented LLM-based contract intelligence with clause extraction, risk scoring, and redline suggestions.

Result: Cut average contract review time by ~55% and surfaced risk flags attorneys previously missed.

Problem: Patient messaging volume 3× outpacing care coordinator capacity, leading to delayed follow-ups.

What we did: Fine-tuned a domain-specific LLM for triage classification and draft response generation with coordinator sign-off.

Result: Coordinators handled 2× message volume with average response time down from 8 hours to 90 minutes.

Problem: Board asked for documented AI risk posture within 60 days ahead of EU AI Act enforcement.

What we did: Delivered role-based AI use policies, risk classification tool, and an ongoing governance calendar.

Result: Passed internal legal review in 45 days; governance calendar adopted across 4 business units.

Problem: Fraud team manually reviewing 400+ daily alerts with a 15-day average time-to-escalation.

What we did: Built an AI triage layer that scored alerts against 30+ behavioral signals and auto-escalated high-risk cases.

Result: Reduced time-to-escalation from 15 days to under 24 hours; investigators focused on 20% highest-risk cases.