What we've built
Governed AI, built under real constraints.
One named client engagement and sixteen governed builds across Healthcare, Life Sciences, Insurance and Finance. Each one is architected around the regulatory framework it would actually live under, not a generic reference diagram.
16
Governed builds
4
Regulated verticals
6
Cloud platforms
12+
Compliance frameworks
Client engagement
mSupply built an AI-native engineering team in four weeks.
Wholesale & industrial distribution · nine acquisitions · Microsoft Fabric
Leadership lacked the CEO-level analytics the integrated business needed to see itself clearly. Their engineers were strong .NET, C#, React and SQL practitioners with no structured way to adopt AI in how they actually build software.
We ran a hands-on enablement program built to leave the capability inside the team, and in parallel built five CEO-level dashboards on their own Fabric platform end to end through Claude Code. A new report now goes from request to deployed in about an hour.
Read the full case study“Claude is a massive time saver.”
4 weeks
Claude Code enablement, March 2026
~15
engineers trained to build with Claude Code
5
CEO-level dashboards on Microsoft Fabric
~1 hour
from request to a deployed executive report
The builds
Sixteen builds, each one architected for its regulator.
These are Kriv AI builds, architected and stood up on synthetic, production-representative data rather than a named client's records. Where figures appear, they describe the build. The mSupply engagement above is delivered client work.
Azure HealthGov Platform
Healthcare AI programs stall because governance is bolted on afterward: PHI reaches models unprotected, lineage is unprovable, agents run unaudited, and compliance cannot sign off on any of it.
Governed AI platform for clinical and operational data, multi-agent architecture with audit trails and role-based access built in from day one.
- Seven-domain Databricks medallion lakehouse under Unity Catalog, with PHI tagging, row-level security and column masking
- Microsoft Presidio de-identifying clinical notes before any model sees them
- Purview auto-labelling PHI and tracking lineage end to end
- Seven governed AI agents under a control plane, with red-teaming
- Defender and Sentinel HIPAA monitoring, plus a Responsible AI dashboard with fairness and SHAP explainability
- 7 domains
- ~664K records in a governed medallion lakehouse
- 7 agents
- under one governed control plane
- 6 dashboards
- hospital ops through governance audit
Patient Engagement & Interoperability Hub
FHIR-native interoperability hub with AI-powered patient engagement, integrates with EHR systems and surfaces governed clinical insights.
Revenue Cycle Intelligence Platform
End-to-end revenue cycle automation, AI agents for prior authorization, claims adjudication, denial management, and payer intelligence.
Clinical NLP Governance Toolkit
On-premises NLP pipeline for clinical notes, discharge summaries, and structured data, audit-ready with de-identification and governance controls.
Clinical Trial Analytics & Compliance
Clinical trial data lands in silos — EDC, lab feeds, site systems — so analysis is slow and manual, PHI is easy to over-expose, and every number has to be defensible to a regulator under 21 CFR Part 11.
AI-accelerated clinical trial data analysis with full 21 CFR Part 11 compliance, electronic records, signatures, and audit trails built natively.
- EDC exports, lab feeds and site files landing in an S3 data lake, normalized to FHIR R4 in AWS HealthLake
- Amazon Comprehend Medical removing PHI at ingest, before any analysis runs
- AWS Lake Formation scoping access per role, down to row and column
- SageMaker and Athena working enrollment, site-performance and dropout-risk signals
- CloudTrail logging every query for the Part 11 audit trail, with QuickSight study dashboards on top
- FHIR-native
- trial data unified on AWS HealthLake
- PHI-safe
- de-identified before analysis
- Part 11-ready
- audit trail on every query
Pharmacovigilance & Drug Safety Hub
Automated adverse event detection and signal management, NLP across literature, spontaneous reports, and clinical data with regulatory-ready outputs.
Real-World Evidence & Drug Discovery
Real-world evidence platform combining EHR, claims, and genomic data, governed data sharing with federated query and privacy-preserving analytics.
Manufacturing Quality & Batch Analytics
AI-powered batch release and quality deviation detection, real-time process analytics with deviation prediction and regulatory reporting automation.
Claims Intelligence & Fraud Detection
Claims operations drown in manual triage, but automation without owners and logs is a compliance risk, not a win.
Multi-model fraud detection combining graph analysis, NLP, and behavioral signals, explainable AI outputs aligned with state regulatory requirements.
- Claims intelligence platform on Azure Synapse with automated triage where it is safe to automate
- A named owner for every workflow, so no decision is unattributable
- An append-only log behind the whole pipeline, reviewable after the fact
- Explainable scoring, so an alert can be defended to a regulator rather than just reported
- 0.963
- fraud model AUC-ROC on synthetic, production-representative claims
- 47/47
- automated tests passing
- 6
- governed executive dashboards
Underwriting Automation & Risk Intelligence
Automated underwriting with AI risk scoring, integrates third-party data, loss history, and behavioral signals with full decision audit trails.
Actuarial Analytics & Reserving
ML-augmented reserving and loss development, scenario modeling, sensitivity analysis, and regulatory-ready actuarial outputs on Databricks Unity Catalog.
Policy Servicing & Customer Intelligence
AI-powered policy lifecycle management, churn prediction, next-best-action, and governed customer data platform with consent management.
AML/KYC Compliance & Transaction Monitoring
Real-time transaction monitoring with AI-generated SAR narratives, tunable thresholds, explainable alerts, and full FinCEN regulatory alignment.
Trade Surveillance & Market Abuse Detection
Broker-dealers must detect market abuse across high-volume trade, order and communications data, and explain every alert to a regulator. Legacy rules engines drown investigators in false positives with little narrative context.
Cross-asset surveillance platform detecting spoofing, wash trading, and insider trading patterns, audit-ready case management with regulator export.
- Surveillance platform on Azure Synapse and Databricks over trade, order and communications data
- Monitored patterns for spoofing, layering and insider-trading signals
- Explainable alerts carrying the narrative context an investigator actually needs
- A reviewable trail behind every flag raised, exportable for a regulator
- 25,000
- historical alerts modeled
- 5
- streaming ingestion hubs
- 42
- Azure resources deployed
Regulatory Document Intelligence
On-premises LLM pipeline for regulatory filing analysis, policy comparison, and compliance gap detection, zero data leaves the perimeter.
Wealth Management & Portfolio Intelligence
AI-powered portfolio analytics with suitability monitoring, risk attribution, and client reporting, governed model outputs with full explainability.
What every build includes
Not demos. Deployable architecture.
01
Compliance-first architecture
Governance and audit trails built into the foundation.
02
Multi-agent AI
Autonomous agents with human-in-the-loop safeguards and full observability at every step.
03
Production-ready infrastructure
Deployable with monitoring, alerting, and security controls already wired in.
04
Fully documented
Architecture docs, deployment runbooks, and compliance framework mappings included.
05
Real dataset patterns
Synthetic or publicly available vertical data, realistic enough to validate the architecture.
06
Engagement-ready
Each project maps directly to a Kriv AI service offering and can become a client engagement.
See one adapted for your environment.
Every build here is a starting point. A discovery call turns it into a scoped engagement built around your compliance framework, cloud platform, and team.