Specialized AI transformation partner for P&C, Global Specialty, Life & Annuity, and Reinsurance organizations

Perpendo AI brings deep insurance-domain expertise, AI architecture excellence, and regulatory intelligence to accelerate enterprise readiness for the Agentic AI era. Our advisory framework blends strategic vision, operational discipline, and compliance-by-design execution — ensuring AI adoption is measurable, explainable, and audit-ready.

7

Assessment pillars

0–5

Maturity levels

ROI + Risk

Outcome model

Hub & Spoke

Operating model

7

Assessment pillars

0–5

Maturity levels

ROI + Risk

Outcome model

Hub & Spoke

Operating model
What we deliver

Maturity baseline (0–5)

Score functions and lines of business, identify gaps, and produce a prioritized backlog with evidence requirements.

Use-case portfolio + ROI

Define KPI trees per use case and map agent actions to measurable outcomes (cost avoidance, revenue lift, risk reduction).

Agentic reference architecture

Composable orchestration layer, safe tool-use policies, and integration pattern with core systems and telemetry.

Audit-ready governance

Control library, DPIA/AIA templates, model risk documentation, evidence capture, and explainability artifacts.
Reference dashboard (example)
Avg maturity

2.8

Across prioritized functions
Adoption rate

93%

Active users / target
Addressable ROI

37%

Scoped processes
Compliance score

75%

Controls / evidence
This panel mirrors the “Agentic AI Adoption Tracker” concept shown in the assessment.
Philosophy

Human amplification — not replacement

In a regulated industry, agents must augment professional judgment with HITL checkpoints, explainability, and durable audit trails.
Diagram

Seven assessment pillars (click to explore)

A pragmatic, reusable framework synthesizing strategy, data, technology, use cases, operations,
people/change, and risk/compliance into a transparent 0–5 maturity model.

Strategy & Outcomes

10%

AI vision aligned to combined ratio / NBV / loss ratio / LAE; use-case portfolio with KPI trees; stage-gated funding.

Data Readiness

20%

LOB ontologies and catalogs; golden sources, lineage, SLAs; PII/PHI classification, masking, and access controls.

Technology & Architecture

20%

Composable agent orchestration; LLMOps/ModelOps (evals, versioning); integration with core systems, identity, telemetry.

Use Cases & Patterns

20%

Canonical patterns (intake/triage/summarization/QA); insurance-native agents (UW audit, FNOL triage, SIU, treaty analytics).

Operations & Reliability

10%

SLOs for quality/latency/cost; incident runbooks & kill-switches; cost governance (unit economics, token budgets, caching).

People, Risk Culture & Change

10%

Role clarity (product owners, safety stewards, SMEs); training and competency ladders; ethics training and escalation paths.

Risk, Compliance & Audit-Ready Artifacts

10%

Policy stack mapped to NAIC/NIST; DPIA/AIA + model risk docs; automated evidence capture; explainability artifacts for referrals/denials.

Evidence Required

    Diagram

    Six-level maturity model (0–5)

    Use this to benchmark where the organization is today and define the next set of measurable milestones,
    governance requirements, and outcomes.

    Level 0 — Not Started

    No initiatives

    No formal agentic AI initiatives; limited understanding; no budget/resources.

    Level 1 — Exploring

    Research phase

    Discovery started; cross-functional team assembled; pilot planning; initial governance.

    Level 2 — Piloting

    1–3 pilots

    Controlled pilots with defined success metrics; HITL on decisions; monitoring loops.

    Level 3 — Implementing

    Multi-BU prod

    Multiple business units in production; AI Ops established; semi autonomy with guardrails + HITL.

    Level 4 — Scaling

    Enterprise

    Enterprise-wide transformation; sophisticated AI platform; continuous monitoring; AI-first culture emerging.

    Level 5 — Optimizing

    Continuous

    AI embedded in core processes; continuous improvement; self-learning within strict constraints.

    What Perpendo AI does at this level
      Typical deliverables

        Offerings

        AI advisory and consulting services

        Engagements are modular: start with an assessment, then move into roadmap, implementation acceleration,
        and scalable governance.
        Assessment & portfolio
        • Seven-pillar assessment with evidence checklist
        • Function + LOB scoring and heatmap-ready outputs
        • Use-case prioritization by value, risk, and feasibility
        • KPI trees and ROI model (cost avoidance, revenue lift, risk reduction)
        • Roadmap with stage-gates and governance milestones
        Architecture & platform enablement
        • Composable agent orchestration and safe tool-use policies
        • LLMOps/ModelOps: prompt/version/eval harness (factuality, safety, bias)
        • Core system integration patterns (PAS/claims/billing), identity, telemetry
        • Cost governance: unit economics, caching, token budgets
        Implementation acceleration
        • Pilot design and production hardening
        • Acceptance tests + guardrail mapping + HITL checkpoints
        • Observability and SLOs (quality, latency, cost)
        • Promotion path: sandbox → pre-prod → prod with kill-switches
        People & change enablement
        • Role clarity: AI product owner, domain leads, platform lead, risk/compliance lead
        • Training & competency ladders for insurance professionals
        • Adoption incentives linked to outcome KPIs
        • Ethics training and escalation paths
        Typical engagement outputs

        Assessment report

        Scores, gaps, evidence checklist, prioritized backlog

        Roadmap

        Phase plan with stage-gates and measurable outcomes

        Control library

        Mapped controls + artifacts (DPIA/AIA/MRM)

        Operating model

        Roles, decision rights, and governance cadence
        Compliance-native

        Controls, artifacts, and audit readiness

        Per use case: maintain a control library mapped to policy requirements and capture evidence as structured
        artifacts (prompts, versions, data sources, evaluations, approvals, HITL checkpoints).
        Pre-deployment artifacts
        • DPIA: data mapping, legal basis, necessity, risk analysis, sign-offs
        • AIA: stakeholder impact, fairness evaluation, transparency measures, HITL checkpoints
        • Model Risk Documentation: model inventory, development docs, independent validation, monitoring plan
        • Testing: edge cases, safety red-teaming, performance + integration testing, UAT sign-off
        Runtime monitoring & controls
        • Drift detection: input/prediction/performance drift with alert thresholds
        • Grounding validation: consistency checks and confidence thresholds
        • PII/PHI leak prevention: sanitization, output filtering, audit logging
        • Guardrails: scope boundaries, tool restrictions, rate limiting, cost limits
        • Incident response: escalation, RCA + corrective actions
        Operating model (hub & spoke)

        AI Product Owner

        Outcome + adoption + compliance accountability

        Domain Leads

        Rules, exceptions, acceptance tests

        AI Platform Lead

        Orchestration layer, evals, telemetry

        Risk & Compliance

        Control library, audit management

        Change & Training

        Competency ladders, enablement
        What we deliver

        Maturity baseline (0–5)

        Score functions and lines of business, identify gaps, and produce a prioritized backlog with evidence requirements.

        Use-case portfolio + ROI

        Define KPI trees per use case and map agent actions to measurable outcomes (cost avoidance, revenue lift, risk reduction).

        Agentic reference architecture

        Composable orchestration layer, safe tool-use policies, and integration pattern with core systems and telemetry.

        Audit-ready governance

        Control library, DPIA/AIA templates, model risk documentation, evidence capture, and explainability artifacts.
        Reference dashboard (example)
        Avg maturity

        2.8

        Across prioritized functions
        Adoption rate

        93%

        Active users / target
        Addressable ROI

        37%

        Scoped processes
        Compliance score

        75%

        Controls / evidence
        This panel mirrors the “Agentic AI Adoption Tracker” concept shown in the assessment.
        Philosophy

        Human amplification — not replacement

        In a regulated industry, agents must augment professional judgment with HITL checkpoints, explainability, and durable audit trails.

        Bordereaux QA & treaty analytics

        Governed automation with evidence.
        View details →

        Referral rationale with evidence citations

        Governed automation with evidence.
        View details →

        Complex document pipelines (SOV, binders)

        Governed automation with evidence.
        View details →

        Suitability scoring & adverse language

        Governed automation with evidence.
        View details →

        Policy servicing agents (loans, 1035) with HITL

        Governed automation with evidence.
        View details →

        Closed‑block conversions with lineage

        Governed automation with evidence.
        View details →

        Cat/event roll‑ups & driver explanations

        Governed automation with evidence.
        View details →

        Treaty program registry with ECDIS

        Governed automation with evidence.
        View details →

        Exposure quality & exception analytics

        Governed automation with evidence.
        View details →

        Agentic AI Readiness Assessment

        A 360° assessment to quantify enterprise readiness for AI-driven transformation — spanning technology, data, people, and compliance.
        Deliverables:
        • Technology & Data Readiness Scoring
        • Business User Capability Assessment
        • Regulatory Alignment Gap Analysis
        • Prioritized Roadmap with Business Case
        • Readiness Scorecard with Risk Ratings

        Outcome: A clear, measurable view of AI readiness and a risk-weighted roadmap for enterprise-wide enablement.

        Readiness & Adoption Tracker

        Readiness & Adoption Tracker

        Track program posture and value delivery across your portfolio.

        Controls
        Data
        People
        Ops
        Risk
        score
        • Exit criteria: pilot → HITL → supervised automation
        • Evidence completeness meter for every use case
        • Kill-switch/rollback coverage with rehearsal history

        AI-Assisted Decision Readiness for Insurance Professionals

        A structured curriculum to elevate AI-Assisted Decision Readiness across the insurance enterprise — from boardroom to front line.

        Four Tailored Tracks

        Track Audience Focus Areas
        Executive Leadership CXOs, Board, BU Heads AI Strategy, Risk, ROI, Competitive Edge
        Business Leaders UW, Claims, Actuarial, Ops Use Cases, Implementation, Compliance Scenarios
        Technical & Analytics IT, Data Science, CoEs LLM Integration, RAGOps, Sandbox Labs
        Front-Line Users Underwriters, Claims Handlers Workflow Integration, Daily Task Agents, Prompting Best Practices
        Outcome: Organization-wide confidence and competency to engage, evaluate, and govern AI responsibly.

        End-to-End Agent Adoption Program

        A holistic framework to operationalize Agentic AI — bridging technology, business users, and compliance.
        Track A

        Technology & Data Readiness

        • Agent / LLM Platform Selection & Guardrails
        • Core System Integration (Guidewire, Duck Creek, SAP, etc.)
        • Data Governance & Privacy-by-Design Controls
        • Non-Production Sandboxes & Evaluation Harness
        Track B

        Business User Adoption

        • Human-in-the-Loop (HITL) Frameworks & Authority Matrices
        • Role-Based Journey Mapping
        • Training Playbooks & Super-User Certification
        • Benefit Tracking (Cycle Time, NIGO, Leakage, STP)
        • Operational Runbooks & Rollback Rehearsals
        Track C

        Regulatory Alignment

        • NAIC / NYDFS / Colorado / State-wise Control Mapping
        • Explainability Packs & Adverse Action Templates
        • Vendor Risk Assessment & ECDIS Approvals
        • Audit Artifact Factory & Automated Reporting

        Outcome: From pilot to scaled adoption, each track ensures technology, business, and governance advance in lockstep — producing compliance-anchored, measurable AI value.