Production-ready Agentic AI, deployed inside your insurance operation.
Layerup's Agentic AI Deployment Services take carriers, MGAs, MGUs, and TPAs from a workflow-level opportunity map to production-grade AI agents — measured on combined ratio, LAE, indemnity leakage, and cycle time. Embedded delivery. Audit-ready governance. No rip-and-replace.
POCs do not move P&L. Production deployments do.
Most enterprise AI programs stall in pilot. Production agentic AI inside an insurance carrier requires workflow excavation, integration topology, model-risk governance, and a champion-challenger rollout plan. Layerup operates as your embedded deployment partner from discovery through production — the same way the top consulting firms staff a transformation, but built around production AI engineering rather than slideware.
Lower indemnity leakage
Coverage discipline, contents valuation, estimate review, and recovery capture — engineered into agents that catch what humans miss at scale.
Compress cycle time
FNOL-to-settlement, submission-to-quote, and renewal cycles compressed by removing manual handoffs and queues — without diluting underwriting discipline.
Reduce LAE & redeploy headcount
Move work from doing to approving. Substitute BPO spend with elastic, embedded AI labor — and redeploy your best people to high-judgment work.
A six-phase deployment methodology, built for insurance.
A repeatable, defensible delivery model that runs from discovery to production rollout — with executive exit gates, joint sign-off with Risk and IT, and a Run plan you can operate for years.
- 0101 Discover Workflows & BottlenecksWorkflow taxonomy + bottleneck heatmap
- 0202 AI Workflow MappingReference architecture & integration topology
- 0303 File & Document AnalysisDocument corpus + edge-case taxonomy
- 0404 P&L Opportunity ScoringLoss-cost, LAE, and combined-ratio impact model
- 0505 AI Agent Design + PilotShadow → HITL → autonomous-with-approval
- 0606 Implementation & RolloutChampion-challenger rollout + Run plan
- 07Run & OptimizeSRE, observability, drift & continuous improvement
What happens, who is in the room, and what you walk away with.
Each phase has named deliverables, executive exit gates, and a fixed cadence. We do not move forward until the prior phase has cleared its gate.
Discover manual workflows and bottlenecks.
We begin with a structured excavation of the operating model — workflows, queues, handoffs, and exceptions — to baseline where humans are the bottleneck and where AI agents will move the P&L fastest.
- 01Time-and-motion study across in-scope workflows
- 02Workflow taxonomy by LOB, function, and severity
- 03Queue, SLA, and handoff inventory
- 04Indemnity leakage, LAE, and cycle-time baseline
- 05STP candidate identification and exception clustering
- 06Source-system inventory and data lineage map
Map people, systems, vendors, handoffs, and data flows.
We translate the operating model into an agent-first reference architecture — where AI agents will read, reason, decide, write back, and route exceptions across your existing claims, underwriting, and policy systems.
- 01Agent surface area mapped to each workflow step
- 02Source / destination system inventory
- 03Integration topology and authentication model
- 04Vendor and BPO substitution map
- 05Human-in-the-loop and approval-gate design
- 06Throughput-bound vs. accuracy-bound classification
Analyze real claims and submissions for leakage and friction.
We sample real files — claims, submissions, statements, estimates, medicals, declarations — to build the document corpus, edge-case taxonomy, and ground-truth set the agents will be evaluated against in production.
- 01Stratified document corpus across LOBs and severities
- 02Edge-case taxonomy and rare-event coverage
- 03Ground-truth labeling protocol and QA cadence
- 04Leakage, delay, manual-rework, and exception analysis
- 05Coverage / appetite / fraud / subrogation signal review
- 06Evaluation harness and accuracy thresholds per workflow
Rank workflows by P&L impact, AI fit, and speed to value.
Each candidate workflow is scored on financial impact, AI feasibility, integration effort, and time to value — producing a defensible, executive-ready prioritization for the deployment roadmap.
- 01P&L impact model per workflow (loss-cost, LAE, leakage)
- 02AI fit score: data quality, determinism, exception rate
- 03Integration complexity and source-system readiness
- 04Time-to-value estimate with confidence intervals
- 05Risk-tier classification and governance burden
- 06Sequencing recommendation: pilot, fast-follow, deferred
Design and deploy the highest-value AI agent in production.
We design, build, and deploy the pilot agent in your environment — moving it through shadow mode, human-in-the-loop, and finally autonomous-with-approval — against pre-agreed exit gates that prove KPI movement before any expansion.
- 01Agent design doc: inputs, reasoning, actions, write-backs
- 02Reference data, prompts, tools, and policy guardrails
- 03Shadow → HITL → autonomous-with-approval progression
- 04Pre-agreed exit gates on accuracy, throughput, and KPI lift
- 05Reasoning traces and evidence-linked decisions from day one
- 06SR 11-7-aligned model risk file and validation evidence
Move from pilot to production rollout across the enterprise.
Once the pilot clears its exit gates, we execute a champion-challenger rollout across teams, geographies, and lines of business — with a Run & Optimize plan, SRE coverage, and a governance posture audit, compliance, and IT can defend.
- 01Champion-challenger rollout by team, region, and LOB
- 02Production runbook, SLOs, and on-call coverage
- 03Change management, training, and adoption plan
- 04Executive scorecard and KPI dashboards
- 05Drift, regression, and continuous-improvement loop
- 06Governance review with Risk, Compliance, and Internal Audit
Three artifacts your executive team can act on.
Every Layerup deployment produces the same three executive-grade artifacts — designed to be presented to the operating committee and the board, not stored on a SharePoint.
AI Opportunity Map
A workflow-level inventory of where agentic AI moves P&L the fastest across claims and underwriting — scored on impact, feasibility, and time to value.
- Workflow taxonomy across LOBs
- STP candidates and exception clusters
- Bottleneck heatmap by team and queue
- Build vs. buy vs. agentic decision matrix
Prioritized Rollout Plan
A sequenced deployment roadmap — from first pilot workflow to enterprise rollout — with exit gates, dependencies, and a champion-challenger expansion model.
- T-shirt sized initiatives with confidence intervals
- Pilot success criteria and exit gates
- Cross-LOB sequencing and dependencies
- Change-management and training plan
Quantified Business Case
An executive-ready financial model that ties each candidate workflow to combined ratio, LAE, indemnity leakage, headcount redeployment, NPV, IRR, and payback months.
- Loss-cost and LAE delta by workflow
- Cycle-time compression and SLA attainment
- Headcount redeployment and BPO substitution
- NPV, IRR, payback months, sensitivity model
An embedded delivery pod, staffed for production AI.
Each engagement is delivered by a named, stable Layerup pod that operates inside your environment for the life of the deployment — with carrier-side accountability and joint exit-gate sign-off at every phase.
Engagement Lead
Carrier-side accountable owner. Manages exit gates, executive cadence, and the joint delivery plan with your COO and CIO offices.
Insurance Solution Architect
Carrier-native architect. Translates claims and underwriting workflows into agent surfaces, integration topology, and governance posture.
Forward-Deployed AI Engineers
Embedded engineers inside your environment. Build, integrate, and harden agents against your real systems, real documents, and real edge cases.
Agent / Workflow Engineers
Specialists on agent design, evaluation, prompts, tool use, and exception routing. Own the accuracy, throughput, and STP rate per workflow.
Reliability Engineer (SRE)
Owns SLOs, on-call, observability, drift detection, and the production runbook. Treats agents as a first-class production service.
Governance & Model-Risk Lead
Owns reasoning-trace evidence, the SR 11-7-aligned model risk file, NAIC AI Model Bulletin posture, and the joint sign-off with your Risk and Compliance teams.
Audit-ready governance, not vendor opacity.
Layerup is built so that Risk, Compliance, Internal Audit, and the regulator can each defend the production posture. Reasoning traces, evidence-linked decisions, approval gates, model risk file — all generated by the platform, not assembled on demand.
Reasoning traces & audit evidence
Every agent decision recorded with inputs, evidence, model output, action taken, and downstream system write — exportable for audit and regulator review.
Evidence-linked decisions
Each conclusion is tied to the underlying document span, system field, or rule that justified it. No black-box outputs reach an adjuster or underwriter.
Approval gates by dollar & risk tier
Configurable approval requirements per workflow, per LOB, per dollar threshold, per risk tier — enforced at the agent layer, not bolted on.
Role-based access & segregation of duties
Permissions aligned to your operating model — adjusters, examiners, underwriters, leads, oversight, SIU, and audit. SCIM-managed and SoD-enforced.
Model risk management
Model risk file, validation evidence, monitoring plan, and challenger model framework — aligned to NAIC AI Model Bulletin, NIST AI RMF, and SR 11-7 model-risk principles.
Shadow → HITL → autonomous progression
Every workflow follows the same change posture: shadow mode, human-in-the-loop, then autonomous-with-approval — with measured exit gates at each stage.
A staged engagement model with executive exit gates.
We move from discovery to production-grade rollout in deliberate stages. Each stage has a named scope, a duration window, an exit gate, and a clear billing model — designed so that finance, procurement, and IT can underwrite the engagement with confidence.
Discovery Sprint
- Workflow taxonomy and bottleneck heatmap
- AI Opportunity Map and Prioritized Rollout Plan
- Quantified Business Case (NPV, IRR, payback)
- Reference architecture and integration topology
Pilot Deployment
- Production-grade agent build for one workflow
- Integration into your systems with full governance posture
- Shadow → HITL → autonomous-with-approval rollout
- KPI dashboard against pre-agreed baseline
Enterprise Rollout
- Champion-challenger rollout across teams, regions, and LOBs
- Cross-workflow agent fleet and shared governance plane
- BPO and vendor substitution where economics support it
- Executive scorecard with quarterly business review
Run & Optimize
- SRE coverage, SLOs, and 24×7 on-call
- Drift detection, regression evaluation, model refresh
- Continuous workflow expansion and exception elimination
- Annual governance and model-risk review
The metrics this engagement is measured on.
Layerup is measured on the executive scorecard — combined ratio, indemnity leakage, LAE, cycle time, throughput, hit ratio, and SLA attainment. Not tokens, prompts, or seats.
Move from POC to production-grade Agentic AI.
Start with a Discovery Sprint. Walk away with an AI Opportunity Map, a Prioritized Rollout, and a Quantified Business Case — and a pilot scoped to move a real KPI within a quarter.