AI Consulting for Insurance
From Claims and Underwriting Bottlenecks to AI Systems That Ship.
Your teams are buried in documents, emails, photos, estimates, and policy language. We build production AI that reads the documents, drafts the decision, and routes the exception — wired into the systems your team already runs.
Delivery-first — we ship working systems. 14+ years building enterprise systems. Human-in-the-loop & audit-aware.
Strategy
Production
Source Systems
Core · warehouse · docs · email
Siloed
Extraction & RAG
Documents → structured, searchable data
Models & guardrails
Scoring · evaluation · monitoring
In production
Integrated · audited · owned by your team
Live
Trusted to Build for Enterprise Insurance Teams
Why It Matters
The Demo Looked Great. Then It Met Your Actual Workflow.
AI pilots don’t fail because the models are weak. They fail because they never touch the systems, permissions, and edge cases that real insurance work runs on.
Where Most Teams Are Stuck
Pilots that never integrate with your policy, claims, or document systems
Happy-path demos that break on endorsements, multi-version policies, and jurisdiction rules
Models no one can put in front of a regulator — no thresholds, no audit trail
Scopes too big to ship — “transform claims” dies in committee
Where RTS Labs Takes You
One high-value workflow, scoped to a production phase 1
Integrated with the core systems your team already runs
Permissions, monitoring, and audit trails built in from day one
Success metrics defined before we write a line of code
Sound Familiar?
The Problems We're Actually Called In to Solve.
No hype, no transformation theater. These are the operating pains we hear from CIOs, CDOs, COOs, and heads of risk every week — and the ones our builds are designed to remove.
“Your claims team is still chasing context.”
Documents, notes, emails, photos, estimates, and invoices live in five systems. Adjusters spend more time assembling the file than deciding the claim.
“Underwriters collect data instead of pricing risk.”
The submission data is somewhere. Pulling it together, normalizing loss runs, and re-keying it eats the hours that should go to judgment.
“Customers and agents want faster answers.”
Coverage and status questions stall in inboxes and portals. Service teams answer the same things by hand, slowly, all day.
“Fraud and leakage show up too late.”
The clues are spread across documents, transactions, notes, and behavior. By the time a human spots the pattern, the payment is out the door.
“Every new tool is another place to log in.”
Policy, claims, billing, CRM, and document systems don’t talk. Work happens in the gaps — spreadsheets, PDFs, and tribal knowledge.
“Compliance can't be bolted on afterward.”
If you can’t show the decision, the evidence, the approval, and the audit trail, the workflow isn’t done — no matter how good the model is.
Where We Apply It
Use Cases We Ship Into Production.
Specific financial workflows — not generic “AI solutions.” Filter by the part of the business you’re trying to move.
Claims
FNOL Intake & Triage
Pain
Adjusters spend more time assembling files than deciding claims.
We Build
FNOL intake capture, triage, and severity scoring from email, photos, estimates, and policy data.
Impact
Faster cycle time, fewer hand-offs, and earlier leakage detection.
For: Head of Claims
Claims
Adjuster Copilot & Coverage Review
Pain
Coverage checks and file summaries are manual and slow.
We Build
A copilot that checks coverage against the policy and drafts the adjuster summary with key facts and next steps.
Impact
Faster, more consistent decisions with a defensible trail.
For: Claims Operations
Claims
Payment-Exception & Leakage Checks
Pain
Overpayments and leakage surface after the payment is out the door.
We Build
Automated exception checks that flag anomalies before payment and route them for review.
Impact
Less leakage, fewer reworks, and a cleaner audit trail.
For: SIU · Claims
Underwriting
Submission Intake
Pain
Too much intake, too little decision time.
We Build
Submission intake that reads ACORD forms and emails into clean, comparable, ratable data.
Impact
More submissions handled, better data quality, and faster quotes.
For: Chief Underwriting Officer
Underwriting
Loss-Run Analysis & Risk Scoring
Pain
Normalizing loss runs and scoring risk eats underwriting hours.
We Build
Loss-run analysis and predictive risk scoring with explainability built in.
Impact
Consistent appetite and reasons your team and regulators can trust.
For: Underwriting
Underwriting
Underwriting Copilot
Pain
A messy submission takes hours to turn into a decision.
We Build
An underwriting copilot that turns a messy submission into a clean, comparable quote package.
Impact
Faster quotes without loosening standards.
For: Underwriting Ops
Documents
Policy & Document Intelligence
Pain
Decisions are trapped inside PDFs, images, and scans.
We Build
Policy extraction, medical-record and estimate summarization, and invoice review.
Impact
Hours of manual review removed, with fewer missed details.
For: Head of Ops
Documents
Litigation & Regulatory Search
Pain
Answers live across filings, contracts, and bulletins no one can search.
We Build
Grounded document search across litigation and regulatory content, with citations.
Impact
Answers in seconds, every response sourced and auditable.
For: Compliance · Legal
Workflow & Compliance
Agentic Workflow Automation
Pain
High-volume routing and follow-up is done by hand.
We Build
Controlled agents that route claims, chase missing info, flag fraud signals, and prep renewals — with humans on exceptions.
Impact
Fewer manual touches, consistent follow-through, faster response.
For: COO · Operations
Workflow & Compliance
Data & Integration Foundations
Pain
Pilots fail because nothing connects.
We Build
Integrations to core claims, policy, billing, CRM, and warehouse systems, plus pipelines, data quality, and MLOps / GenAI Ops.
Impact
AI that works on your real data, inside your real systems.
For: CIO · CTO
Claims
FNOL Intake & Triage
Pain
Adjusters spend more time assembling files than deciding claims.
We Build
FNOL intake capture, triage, and severity scoring from email, photos, estimates, and policy data.
Impact
Faster cycle time, fewer hand-offs, and earlier leakage detection.
For: Head of Claims
Claims
Adjuster Copilot & Coverage Review
Pain
Coverage checks and file summaries are manual and slow.
We Build
A copilot that checks coverage against the policy and drafts the adjuster summary with key facts and next steps.
Impact
Faster, more consistent decisions with a defensible trail.
For: Claims Operations
Claims
Payment-Exception & Leakage Checks
Pain
Overpayments and leakage surface after the payment is out the door.
We Build
Automated exception checks that flag anomalies before payment and route them for review.
Impact
Less leakage, fewer reworks, and a cleaner audit trail.
For: SIU · Claims
Underwriting
Submission Intake
Pain
Too much intake, too little decision time.
We Build
Submission intake that reads ACORD forms and emails into clean, comparable, ratable data.
Impact
More submissions handled, better data quality, and faster quotes.
For: Chief Underwriting Officer
Underwriting
Loss-Run Analysis & Risk Scoring
Pain
Normalizing loss runs and scoring risk eats underwriting hours.
We Build
Loss-run analysis and predictive risk scoring with explainability built in.
Impact
Consistent appetite and reasons your team and regulators can trust.
For: Underwriting
Underwriting
Underwriting Copilot
Pain
A messy submission takes hours to turn into a decision.
We Build
An underwriting copilot that turns a messy submission into a clean, comparable quote package.
Impact
Faster quotes without loosening standards.
For: Underwriting Ops
Documents
Policy & Document Intelligence
Pain
Decisions are trapped inside PDFs, images, and scans.
We Build
Policy extraction, medical-record and estimate summarization, and invoice review.
Impact
Hours of manual review removed, with fewer missed details.
For: Head of Ops
Documents
Litigation & Regulatory Search
Pain
Answers live across filings, contracts, and bulletins no one can search.
We Build
Grounded document search across litigation and regulatory content, with citations.
Impact
Answers in seconds, every response sourced and auditable.
For: Compliance · Legal
Workflow & Compliance
Agentic Workflow Automation
Pain
High-volume routing and follow-up is done by hand.
We Build
Controlled agents that route claims, chase missing info, flag fraud signals, and prep renewals — with humans on exceptions.
Impact
Fewer manual touches, consistent follow-through, faster response.
For: COO · Operations
Workflow & Compliance
Data & Integration Foundations
Pain
Pilots fail because nothing connects.
We Build
Integrations to core claims, policy, billing, CRM, and warehouse systems, plus pipelines, data quality, and MLOps / GenAI Ops.
Impact
AI that works on your real data, inside your real systems.
For: CIO · CTO
How We Partner
From Strategy to Production in Four Practical Steps.
We price by scope, not by the hour. Every engagement starts with a fixed-fee discovery sprint, so you know the number — and the go/no-go — before real money is spent.
1
Find the Workflow With the Most Pain
We pinpoint where manual review, delay, rework, leakage, and compliance risk actually show up across claims, underwriting, and servicing.
Prioritized shortlist with ROI
2
Validate the Data, Documents & Systems
We review claims, policy, billing, CRM, document, third-party, and reporting flows to confirm what’s ready and what needs work first.
Go/no-go before real money
3
Build a Phase 1 Production Solution
We design, build, integrate, test, and deploy a focused AI workflow your team can actually use — with controls and oversight built in.
Shipped, not staged
4
Measure, Improve & Scale
We track adoption, accuracy, cycle time, leakage, exceptions, and response time — then extend to the next workflow.
Phase 1 to enterprise
Why RTS Labs
The Team That Builds It — Not Just the One That Recommends It.
Plenty of firms will write you a strategy. Fewer will stand up the production system, own the data and security, and stay past the demo. Here’s how we compare to the usual options: big consulting firms, offshore dev shops, and AI prototype vendors.
Big Consulting Firms
Offshore Dev Shops
AI Prototype Vendors
Ships Production Systems, Not Slide Decks
Senior Engineers on Your Account
Owns Data, Security & Governance
Phase 1 Live in Weeks, Not Quarters
Stays Past the Demo — Eval & Monitoring
Speaks Insurance Workflows Fluently
See It in Action
Watch How We Ship AI Into Production.
A short look at how RTS Labs takes a financial workflow from stuck pilot to a system your team runs every day — the data work, the guardrails, and the handoff. No buzzwords, just the build.
Why Insurance Leaders Trust Us to Build
Built So Compliance, IT, and Risk Can Say Yes.
No vanity metrics. These are the things that decide whether an AI system survives contact with production, compliance, and your risk team.
Human-in-the-Loop by Design
People approve consequential decisions. AI does the prep, drafting, and routing — never the final call on a claim or coverage.
Role-Based Permissions
Every agent and assistant is scoped to approved systems and actions, enforced by role — not by hope.
Audit Trails & Traceability
Every action, source, and decision is logged. When a regulator asks “why,” you have the evidence and the approval chain.
Evaluation & Monitoring
We instrument output quality, flag drift, and alert when performance drops below defined thresholds.
Confidence Thresholds
Low-confidence outputs don’t act — they ask. Thresholds and escalation rules keep edge cases in front of a person.
PII & Data Privacy Controls
Security-conscious architecture, careful PII handling, and data boundaries your security team can review and approve.
Production AI Systems
Integrated into your core claims, policy, and billing systems — running in your environment, not a sandbox.
Practical Phase 1 Delivery
A scoped, production-ready first build that earns the right to scale — instead of a multi-quarter promise.
Delivery-First, 14+ Years
We’re measured by what ships and what it moves — backed by 14+ years building enterprise systems.
Proof
What It Looks Like When It Ships.
One representative engagement, plus the template we use to capture results. Metrics appear as slots — we populate them with your real, audited figures.
Insurance · Policy Review
Cutting Policy Review Time for an Insurance Carrier
A carrier managed a huge portfolio of long, dense, frequently-updated health, life, and property policies. Manual review was slow and error-prone. RTS Labs built AI that summarizes multi-version contracts in seconds and answers complex coverage queries directly from the documents.
“Reviewers stopped reading every page and started asking the document questions.”
— RTS Labs Engineering Lead, Data & AI Practice
Case Study Template · Underwriting
Submission Intake, Rebuilt as a Live System
Problem
Brokers’ submissions were rekeyed by hand — slow and inconsistent.
What RTS Built
AI intake that reads submissions and ACORD forms into the rating engine.
Metrics to Collect
Intake time · submissions triaged · data quality
Case Study Template · Claims
Claims Leakage Reduction
Problem
Rules engines flooded SIU with false positives and missed organized fraud.
What RTS Built
Real-time claims scoring + review queue, integrated with the claims system.
Metrics to Collect
Leakage rate · false-positive rate · fraud caught
Our Clients' Words
Testimonials
Dumpster Dudez
- Brian Johnson | President
National Center for Teacher Residencies
- Chris Lozier | COO
Blue Ocean Brain
- Gemma Brooks | COO
Momentum Holdings
- Carol Trotter | Momentum Holdings
eComEngine
- Erica Rowe | Director of Product Engineering
Holon Health
- Jason Herzog | CEO
Questions, Answered Straight
Before You Talk to Us.
What is AI consulting for insurance?
AI consulting for insurance helps carriers, MGAs, brokers, and claims teams use AI to improve claims, underwriting, fraud detection, document processing, and customer service, then build and ship the systems that run them. The goal isn’t to “use AI,” it’s to turn insurance data into faster decisions and cleaner workflows, integrated into the policy admin and claims platforms you already run.
How can AI improve insurance claims processing?
AI speeds claims by reading and summarizing claim documents, flagging missing information, scoring severity and risk, and routing each claim to the right adjuster. That cuts manual review and cycle time, and lets your team focus on the claims that actually need human judgment. Every step stays auditable, with a human approving the decisions that matter.
How can AI improve underwriting?
AI helps underwriters intake submissions, extract and compare risk factors, analyze loss runs, and flag missing information before it stalls a quote. Underwriters move faster while keeping human control over pricing and risk decisions. The result is shorter submission-to-quote time and more consistent, explainable risk assessment your regulators can follow.
Can AI help insurance companies detect fraud?
Yes. AI analyzes claims data, customer behavior, documents, images, and historical patterns to flag unusual activity earlier and cut the false positives that bury investigators. It doesn’t replace your fraud team, it helps them prioritize suspicious claims faster and miss fewer signals, with an audit trail behind every flag.
Can AI automate insurance document processing?
Yes, and it’s one of the clearest opportunities in insurance. AI extracts, classifies, summarizes, and validates information from PDFs, ACORD forms, policies, loss runs, medical records, and email, so much of the manual document review goes away. Every output traces back to the source page, which matters for claims and compliance review.
How can AI improve insurance customer service?
AI answers routine policy questions, provides claim-status updates, summarizes account and policy history, and gives agents faster access to the right information inside their existing screen. It works best when grounded in your trusted internal systems rather than static FAQs, so the answers are accurate, sourced, and actually useful to the policyholder.
Does insurance AI integrate with our legacy policy admin and claims systems?
Yes, and this is where most AI projects fail. We connect to policy administration systems, claims platforms, CRMs, data warehouses, document systems, and reporting tools using live connections, not exported files. We audit those data flows and confirm what’s feasible before building, because AI only creates value when it runs inside the systems your team already uses.
Is our data good enough for AI, and what data is needed?
You don’t need perfect data to start, but you need access to the right data. Useful sources include claims history, policy and underwriting files, customer records, call notes, documents, payment history, and third-party risk data. We assess what exists and where it lives, fix the foundation as part of the work, and confirm feasibility before promising an outcome.
How long does an insurance AI project take, and how do you measure ROI?
A focused phase-1 build typically ships in 8 to 16 weeks, depending on the workflow, data quality, integrations, and compliance requirements. We measure ROI in operating terms set with you up front: claims cycle time, processing cost, manual reviews removed, fraud caught, submission-to-quote time, and customer response time. If success can’t be measured, the project is too vague to start.
How is RTS Labs different from a big consulting firm or offshore dev shop?
Big consulting firms write the strategy and hand the build to a junior team you never met. Offshore shops build what you spec, not what your workflow needs. RTS Labs is delivery-first: the senior engineers who scope your project build it, integrate it into your production stack with controls, audit trails, and explainability for regulators, and stay through go-live and past it. We own the outcome, not just the tickets.
Let's Talk
Bring Us Your Toughest Insurance Workflow.
We’ll tell you in one conversation whether AI can move the needle — on cycle time, leakage, or the combined ratio — and what it takes to ship it. No pitch theater, no obligation.
A senior engineer on the call — not a salesperson
A straight read on feasibility, data, and effort
A practical first step, whether or not you work with us