Insurance • Healthcare • Banking
AI-Driven Decisioning for Regulated Enterprises
Deliver measurable automation for high-stakes decisions—without sacrificing auditability, compliance, or control. We combine deterministic rules, governed AI, and human-in-the-loop controls to design decision systems where AI is a governed participant—not a black box.
Decision systems that stand up to scrutiny
We architect AI decisioning platforms for insurance, healthcare, and banking—where every decision must be explainable, auditable, and defensible under regulatory review.
Separate deterministic from non-deterministic logic. Deterministic: policy interpretation, calculations, state machines. Non-deterministic: extraction, classification, recommendations (with gates). We design rules engines orchestrated with AI, built on canonical data models that ensure consistency.
Deploy AI with explainability, audit trails, and bias guardrails built in. Human-in-the-loop decision gates ensure oversight where it matters most.
Pre-built decisioning systems for claims STP, eligibility adjudication, and policy risk assessment—with simulation and testing harnesses included.
Partner with architects who understand both engineering and compliance. We build decision platforms that stand up to audits, regulators, and litigation.
Decisioning systems for regulated industries
We build AI decisioning platforms for insurance carriers, healthcare payers, and banks—designed to withstand regulatory scrutiny, audits, and litigation. Every decision is traceable, explainable, and defensible.
Built for: Heads of Claims, Underwriting Ops, Digital Transformation, Platform Engineering, and Risk/Compliance leaders at enterprises with multi-product, multi-region operations and high regulatory burden.
Claims Decisioning & STP
- Automate FNOL triage, coverage verification, and liability assessment
- Rules-based adjudication with AI-assisted exception handling
- Full audit trails for regulatory compliance and litigation defense
Eligibility & Policy Decisioning
- Real-time eligibility verification with deterministic rule enforcement
- Risk scoring and underwriting automation with explainable outputs
- Policy issuance workflows with human-in-the-loop controls
Governance & Compliance Frameworks
- AI governance architectures that satisfy regulatory requirements
- Bias detection, explainability, and audit trail implementation
- Testing harnesses for decision validation and regression prevention
Decision Platform Architecture
- Canonical data models and ontology design for decision consistency
- Rules engine + AI orchestration on Azure cloud platforms
- Simulation environments for testing high-stakes decision logic
Proven results in regulated industries
We deliver measurable outcomes for Fortune 500 insurance carriers, healthcare payers, and banking operations— where every decision must withstand regulatory scrutiny.
Challenge
Manual FNOL processing causing 48-72 hour intake delays and inconsistent routing decisions
Solution
Implemented AI-powered FNOL intake with deterministic routing rules and human-in-the-loop exception handling
Outcomes
- Reduced average intake time from 48 hours to 4 hours
- Improved routing accuracy to 94% with full audit trails
- Eliminated $2.3M in annual leakage from mis-routed claims
Challenge
Eligibility verification requiring 200+ FTEs with high error rates and compliance risk
Solution
Built governed decision platform combining policy rules engine with AI-assisted exception handling
Outcomes
- Automated 78% of eligibility decisions with explainable outputs
- Reduced verification errors by 63%
- Achieved full regulatory compliance with deterministic audit trails
Challenge
Legacy underwriting systems unable to scale across products and regions with consistent governance
Solution
Designed canonical data model with rules-based decisioning and AI orchestration on Azure
Outcomes
- Unified 14 product lines onto single decision platform
- Reduced underwriting cycle time by 40%
- Established repeatable governance framework across 8 regions
Trusted by Fortune 500 insurance carriers, regional healthcare payers, and global banking operations
Our clients operate in highly regulated environments where decision systems must be explainable, auditable, and litigation-ready.
Decision Systems Architects
We are not tool builders. We are decision systems architects who bridge engineering, compliance, and business— designing AI platforms that stand up to regulatory scrutiny and litigation.
Carlos Megret
Founder, Principal Decision Architect
- •15+ years designing decision systems for insurance, healthcare, and financial services
- •Led AI decisioning platforms for Fortune 500 insurance carriers
- •Specialized in deterministic rules + AI orchestration for high-stakes decisions
- •Expert in governance frameworks that satisfy regulatory requirements
- •Former technical lead for claims adjudication and STP modernization at scale
Rebecca Lescay
Principal Data & Decision Architect
- •3+ years designing canonical data models for decision consistency
- •Expert in ontology design and semantic frameworks for regulated industries
- •Specialized in data governance architectures that enable explainable AI
- •Led master data management programs for insurance and banking
- •Deep experience building decision data pipelines with full lineage tracking
Peter Smith
Senior AI Decision Engineer
- •3+ years building governed AI systems for high-stakes decision automation
- •Expert in Azure OpenAI with explainability and bias detection frameworks
- •Specialized in deterministic AI outputs with full provenance tracking
- •Led implementation of human-in-the-loop controls for regulated decisions
- •Strong background in NLP for document intelligence in claims and underwriting
About nimbus180
nimbus180 is a cloud and AI engineering consultancy focused on enterprise modernization, decision automation, and AI adoption with governance. We help teams build platforms that are production-grade, auditable, and scalable—without unnecessary complexity.
Our approach is architecture-led: define canonical models, decision boundaries, and operational guardrails first—then apply AI and automation where it creates measurable outcomes.
What you get
- Executive clarity: a plan you can take to your CTO board.
- Engineering assets: reference implementations, IaC, CI/CD patterns.
- Repeatability: playbooks and accelerators reused across products and regions.
Let’s talk
For consulting, advisory, or a quick architecture review, reach out and we’ll respond with next steps.