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.
From your SOPs to AOPs — your AI Agents, written by your Forward-Deployed AI Engineers.
Layerup's Forward-Deployed AI Engineers embed inside your environment, sit with your adjusters, examiners, underwriters, and ops leads, and convert how the work actually runs today — your Standard Operating Procedures — into Agent Operating Procedures (AOPs): runnable, evaluable, governed instructions your AI Agents execute against your real systems, real documents, and real exception paths. The agents that go to production are not generic — they are yours, written for your carrier, your LOBs, your handbooks, your guardrails.
Capture
Layerup's Forward-Deployed AI Engineers embed inside your environment and shadow your adjusters, examiners, underwriters, and ops leads. They capture the SOP as it actually runs today — coverage rules, appetite logic, fee schedules, escalation paths — not the wiki version.
Convert
Each Standard Operating Procedure is rewritten as an Agent Operating Procedure (AOP): structured inputs, decision criteria, tool calls, system write-backs, evidence requirements, and the exact escalation paths your AI Agent will execute against your real systems.
Calibrate
AOPs are tested against your golden file set and calibrated to your accuracy, throughput, and governance bar before the agent ever sees a live queue. Your FDE owns the evaluation harness end to end.
Codify
AOPs live in version control, are reviewable by Risk, Compliance, and IT, and are owned by your enterprise — not locked inside a vendor black box. Your handbooks become a first-class engineering artifact.
Continuously improve
As your SOPs evolve — bulletins, fee schedules, appetite changes, regulatory updates, NAIC and state DOI guidance — your FDE versions, re-evaluates, and re-deploys the AOPs so the AI Agent stays in lockstep with your operating model.
A named Forward-Deployed AI Engineer per workflow.
Not a shared support queue. Your FDE owns the AOPs end to end — on your tooling, on your cadence, accountable to your COO and CIO offices.
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.