Case Studies & Deconstructed Projects
Real and deconstructed examples of how Kriv AI helps regulated organizations move from AI experiments to governed, production-ready systems.
Most organizations in healthcare and regulated mid-market are stuck in AI pilot mode. These case studies show how we diagnose the real bottlenecks, design governed architectures, and deliver agentic workflows that actually make it into production.
Featured Case Studies
From Stalled AI Pilots to a Governed MLOps Foundation in a Regional Hospital Network
Problem: A hospital network with multiple AI pilots, no shared governance, and no clear path to production.
Outcome: We delivered a readiness assessment, a prioritized roadmap, and an MLOps foundation that brought two key models into stable production.
De-Risking LLM-Based Clinical Documentation for a Mid-Market Health-Tech Vendor
Problem: A health-tech company had fine-tuned an LLM for clinical notes, but couldn't get it past legal and compliance review.
Outcome: We restructured their model governance, implemented audit logging, and delivered a HIPAA-aware deployment architecture that satisfied compliance.
Agentic Workflow for Compliance Reviews in a Mid-Sized Pharma Manufacturer
Problem: Manual compliance document reviews were taking weeks and creating bottlenecks in R&D timelines.
Outcome: Deployed governed AI agents for document pre-screening, reducing review cycles by ~40% while maintaining audit trails.
All Case Studies
Revenue Cycle Automation for a Multi-Site Clinic Network
Problem: Multiple RPA bots in revenue cycle with no governance, high error rates, and manual overrides.
Approach: Re-designed workflows as governed agentic automations using Zapier + LLMs, with human-in-the-loop.
Outcome: Reduced manual corrections by ~35% and provided audit-ready logs for compliance.
AI Readiness Assessment for a Biotech R&D Team
Problem: Data science team building models in silos with no shared infrastructure or governance framework.
Approach: Conducted comprehensive assessment across data, models, governance, and team capabilities.
Outcome: Delivered prioritized roadmap that unified fragmented AI initiatives under shared governance.
Standing Up MLOps for a Digital Health Startup
Problem: ML models deployed manually with no monitoring, versioning, or rollback capabilities.
Approach: Implemented lightweight MLOps stack with CI/CD, model registry, and production monitoring.
Outcome: Reduced deployment time from days to hours with full audit trail and drift detection.
Claims Pre-Screening Agents for a Regional Insurer
Problem: Claims processors overwhelmed with volume, inconsistent pre-screening, and compliance gaps.
Approach: Deployed AI agents for initial claims triage with human review for edge cases.
Outcome: Improved pre-screening throughput by ~50% while maintaining compliance standards.
HIPAA-Compliant Summarization for a Specialty Practice
Problem: Physicians spending hours on documentation with generic AI tools that weren't compliant.
Approach: Fine-tuned domain-specific LLM with HIPAA-aware architecture and audit logging.
Outcome: Reduced documentation time by ~25% with fully compliant, auditable AI assistance.
AI Governance Framework for a Regional Financial Services Firm
Problem: Growing AI usage with no centralized governance, risk assessment, or policy framework.
Approach: Established governance framework, risk register, and ongoing monitoring as a service.
Outcome: Proactive AI risk management with quarterly reviews and continuous compliance monitoring.
How to Read These Case Studies
Some case studies are based on real engagements (with details adjusted for confidentiality), while others are deconstructed composites from repeat patterns we see in the industry. All are anonymized and scrubbed of sensitive details, designed to illustrate patterns, not brag about logos.
- We avoid naming clients unless we have explicit permission—patterns matter more than logos.
- Deconstructed case studies come from common failure and success modes we see across the sector.
- Every story is framed around problem, approach, and measurable or directional outcomes.
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If one of these stories feels uncomfortably similar to your current situation—stalled pilots, governance gaps, or fragile automations—that's usually the right moment to talk.