Agents for the Queue That Never Gets Shorter
Every claim read. Every risk understood. Every action auditable.
We build agents for carriers, MGAs and TPAs. Reading the file is the easy part. Proving how the answer was reached, two years later, is the job.
Date and cause of loss
FNOL form, page 2
Damage detail and estimate
Inspection report, page 7
Prior claims history
Your claim system, 2021 to 2025
Applicable policy language
Policy form, section I
The agent assembles the file. It does not decide the claim.
Every field traced back to the page it came from. That is what survives an exam.
Carriers, brokers and agencies — not one insurance logo borrowed from an adjacent industry. Every one of these is a production system, not a pilot.
Getting It Built Is Easy. Getting It Explained Is the Job.
In insurance a good answer is not enough. Someone will ask how you got there: an auditor, a reinsurer, a regulator on a market conduct exam. We build for that question.
Every answer cites its source
A finding points to the document and the page it came from, and to the policy language it was read against. Nobody has to take the agent’s word for it.
A person still decides
Agents read, extract, summarize and draft. They do not deny a claim, decline a risk, or set a rate. A named human makes the call and the approval is part of the record.
Reconstructable years later
Every action, input, prompt and model version captured append-only. When an exam asks what happened on a file in March, you answer with a record, not a recollection.
Proven on your worst files
The accuracy bar gets set on your documents before anyone commits, not on our benchmark set. We agree the bar with your team, test against the files that actually break things, and tell you the number.
Its own identity, its own limits
Every agent gets a service identity and least-privilege access, system by system. It reads your policy admin and claims systems. It does not write to them without a gate.
Still running next quarter
Monitoring, drift checks and the morning a source system changes its file format are ours. An agent nobody owns after go-live quietly stops being used.
These are the same controls we publish openly, applied to a regulated book. See the governance framework in full →
Sixteen years building production systems, well before anyone called it agentic.
A firm with a bench, not a two-person shop that disappears after launch.
No offshore delivery. The engineers who build it are the ones on your calls.
We build in your environment. Your data does not leave it.
Bring us one workflow. We prove it pays, build it properly, control it so security signs off, and keep it running after everyone else has moved on.
Nobody buys ten agents. You buy one queue, it clears, and the second one is an easier conversation.
Agents for the Work That Never Stops Arriving
Document-heavy, queue-heavy work where the volume is relentless and the rules are already written down. Filter by where in the cycle it sits.
Intake is everything that arrives without warning and has to be read. Underwrite is judgement work, prepared rather than made. Serve is what the policyholder and the broker actually feel.
Claims Intake & Triage Agent
Every FNOL read, classified, and routed in minutes. Exceptions flagged with reasons.
Claims OperationsClaims Document Agent
Inspection reports, loss runs, and medical records extracted into structured claim data.
Claims OperationsFraud Signals Agent
Suspicious patterns surfaced across claims, loss history, and third-party data. Humans decide.
SIU / FraudSubrogation & Recovery Agent
Recovery opportunities identified and documented before they expire.
SubrogationUnderwriting Submission Agent
Submission packages, loss runs, and risk reports summarized into an underwriter-ready view.
UnderwritingUnderwriting Escalation Agent
Clean files sorted into a fast lane. Edge cases arrive pre-summarized with the policy cited.
UnderwritingRenewal Preparation Agent
Renewals assembled with claims history, benchmark comparisons, and rate context.
Underwriting / RenewalsPolicy Servicing Agent
Endorsements, certificates, and correspondence drafted and issued with approval gates.
Policy ServicesCompliance & Filings Agent
Regulatory changes monitored, filings drafted, every decision traceable.
Compliance & RegulatoryBroker Enablement Agent
Appetite, product, and status answers at the point of sale, from your own guidelines.
DistributionNothing in that stage yet.
Already Reading Claim Documents
One agent on this roster is in production with a named client. Here is what it does, so you can judge the claim rather than take it.
- S2 Software, inside RiskWise. Third-party inspection reports are read and turned into structured events and actions in the platform, instead of a person retyping them into a form. S2 will talk about it, and their testimonial runs on our homepage.
- The pattern behind the other nine. Document extraction, retrieval with citations, exception routing and reporting assembly are all running in production for clients in other regulated industries. Different documents, same machinery.
We will walk you through it on a call, including what did not work the first time. More named work, workflow traces and the full stack live on our development page.
Three Sensible Places to Start
Not the biggest theoretical payoff. The ones where the queue is bounded, the documents already exist, and you can prove it inside a quarter.
Claims Document Agent
The one we have already shipped. The documents arrive in the same shapes every day, and the before-and-after is obvious to anyone who has watched the retyping.
Underwriting Submission Agent
Underwriter time is revenue time. The agent assembles the submission view so the underwriter spends the hour on the risk instead of on the paperwork.
Claims Intake & Triage Agent
Start by measuring the intake queue rather than automating it. You learn where the time actually goes, and nothing touches a claim until you decide it should.
Bring us the queue that never gets shorter and we will tell you straight whether an agent is the right answer for it.
Fair Enough. The Longer Answers.
How is this different from the automation we already have?
Your existing automation follows a path someone drew in advance. It works until a document arrives in a shape nobody anticipated, and then it drops the file into a human queue. That queue is where your backlog actually lives.
An agent reads the file, works out what it needs, and takes the next action: opening the claim, extracting the fields, routing the exception, drafting the correspondence. That is also why this page spends its first section on controls. Something that follows a script needs testing. Something that acts needs an identity, a permission boundary, an audit trail and a human gate.
If a person still reviews everything, have you actually removed any work?
That is the right question, and it is how most document tools become shelfware. If every output has to be checked, you have converted typing into checking, and checking is worse: a person reviewing a confident machine catches less than a person doing the task.
So the design goal is not accuracy in the abstract. It is knowing which files can skip review entirely. Every extraction carries a confidence signal, and we agree with your team which case types are eligible to go straight through at which threshold. Bounded, high-volume, low-variance documents earn that eligibility first. Complex and high-severity files never do, and should not.
The other half is the agent knowing what it does not know. We would rather it abstain and route a file than guess confidently, so we measure how often it declines as carefully as how often it is right. An agent that guesses is the one that quietly stops being trusted.
What you should hold us to is straight-through rate on your real volume after a few months in production, not accuracy on a test set. That is the number that turns into hours, and it is the number we scope the diagnostic to estimate.
What happens to the files the agent cannot handle?
They go to a named exception queue with the reason attached, not back into the general pile. The reason matters: a person picking up an exception should see what the agent could not resolve and what it already assembled, so the handoff saves work instead of duplicating it.
That queue is also the roadmap. The exceptions cluster, and the clusters tell you what to build next or what to fix upstream in a form or a vendor feed. On most engagements the exception analysis is worth more in the first quarter than the automation is.
How does this survive a market conduct exam?
By assuming the exam from day one rather than retrofitting for it. Every agent action is written to an append-only record: what came in, what the agent read, which version of which model produced the output, what it recommended, who approved it and when. That record is queryable by claim, by policy and by date range.
What matters in an exam is not that the agent was accurate on average. It is that you can reconstruct one specific file, on one specific date, and show that the decision was made by a person against the policy language. So the record captures the citation, not just the conclusion.
We also expect your compliance function to see the design before we build, not after. If they cannot explain how it works to an examiner, the design is wrong and we would rather find that out early.
Can an agent deny a claim or decline a risk?
No, and we will not build one that does. Coverage determinations, declinations, rate decisions and anything that produces an adverse notice to a policyholder stay with a person on your team. The agent assembles the file, cites the policy language and recommends. A person decides.
This is not caution for its own sake. An auto-issued adverse decision is the single fastest route to a complaint, a bad-faith allegation and a regulator’s attention, and no efficiency gain is worth that exposure. The value is in the ninety percent of the work that is reading and assembly, which is also where the cost actually sits.
What about bias and unfair discrimination in underwriting?
Treated as scope from the start, not as a review at the end. Testing for disparate outcomes is in the plan before we build, your compliance and actuarial teams see the test design, and the results are part of the documentation package rather than something we produce if asked.
It is also why the underwriting agents prepare and escalate rather than advance files on their own. An agent that summarizes a submission is a very different regulatory object from one that decides who gets a policy, and we deliberately build the first.
Will these work with our policy admin and claims systems?
Yes, and the integration is usually the honest majority of the work. We have built against modern platforms and against the older systems underneath them, including the ones with no real API where the answer is a scheduled file drop.
Read-only first, always. The agent reads your systems and writes to its own record. Writing back into the system of record is a separate conversation with your controls team, and it comes later, with gates.
Should we build these, or buy a platform?
Both, and we will tell you which is which on the first call. Buy where the product is already ahead and the problem is generic: Vertesia for document processing infrastructure, Further AI for packaged insurance workflows. If your problem is reading documents faster in a shape the market has already solved, buying beats building.
Build where the work is shaped like your book: your appetite and your rating logic, the seams between your policy admin, claims and billing systems, the reporting your regulators and reinsurers want in your format. No vendor is going to build those for one carrier.
We will say buy when buying is right, including when the answer is that you do not need us for this one.
Do we have to take all ten?
No, and nobody ever has. You take one queue, it either clears or it does not. The second conversation is much easier because the controls, the environment and the compliance review are already done.
What does it cost to find out if one of these is worth doing?
A fixed-fee diagnostic: two weeks, a clear verdict on the workflow, the integration surface mapped, and a build estimate you can take to your budget holder. Delivered as the Lumynate ROI Audit, from $30K, with half the fee crediting against the build.
For the build itself, first builds are fixed scope with a fixed ship date and typically land in the low-to-mid six figures, driven almost entirely by how many systems the agent has to touch.
If the verdict is that an agent is the wrong answer, you get that in writing, and the finding is usually worth more than the fee.
Every agent above is built on Lumynate, our system for building and operating agents: the method, the reusable component library, the guardrails, and the team that keeps it running after go-live.
Bring Us the Queue That Hurts
Thirty minutes, one workflow, and an honest answer about whether an agent belongs anywhere near it.
Or start with the two-week diagnostic if you already have a pilot that stalled.