Production-grade AI systems for carriers, MGAs, and agencies - built to sit inside the policy admin, claims, and document systems you already run.
The specifics differ. The same operational friction keeps appearing wherever this work crosses people, records, and decisions.
A claim comes in by phone, email, or a web form, and it waits for a person to read it, classify it, and route it to the right adjuster - often after the same information gets typed into a system a second time.
First point of frictionPolice reports, medical records, repair estimates, and photos arrive on different days from different senders, and someone has to notice they're missing before the adjuster can move.
Recurring operating costA PDF here, a spreadsheet there, an email body with the key numbers buried in prose - underwriting spends real hours just getting a submission into a comparable shape before pricing it.
Recurring operating costThe book doesn't shrink, but the staff reviewing it doesn't grow, so endorsement requests and renewal reviews back up right when the volume peaks.
Recurring operating costNew hires and even experienced staff end up asking the same veteran the same questions about coverage nuances, past exceptions, and carrier-specific rules that were never written down in a place anyone can search.
Recurring operating costA red flag in the claims notes, an inconsistency in the documents, a pattern across related claims - each one is visible on its own, but nobody is positioned to see them together before payout.
Recurring operating costBefore we propose a system, we map the recurring surfaces of the operation and the work that moves through them.
Read a claim, submission, or service request as soon as it arrives from any channel.
File documents, compare requirements, and identify what is still missing from the record.
Send work to the correct queue with the facts and source files already attached.
Keep underwriting, coverage, and payout decisions with the people accountable for them.
The operation keeps its judgment. AI takes on the repeatable, system-to-system work that makes good people spend their time on administration.
AI handles
Your team handles
The system can draft, organize, and surface the work. An authorized person decides and acts.
Operational systems that connect the tools you already use. Not chatbots sitting next to the work.
The problem: first notice of loss comes in through several channels, and each one needs a person to read it, decide what kind of claim it is, and get it to the right desk. That first step sets the pace for everything downstream, and it's the step most likely to sit idle overnight or over a weekend. The fix: an AI system that reads an incoming claim the moment it arrives, pulls the relevant facts out of the call transcript, form, or email, classifies the claim type and urgency, and routes it to the right adjuster or queue - with the extracted facts already logged, not re-typed by the next person to touch the file.
The problem: a claim file fills up over days or weeks as documents trickle in from claimants, providers, and third parties, and someone has to keep checking whether the file is complete enough to move forward. That checking is manual, easy to miss, and it's the reason files stall on an adjuster's desk. The fix: a pipeline that ingests documents as they arrive, classifies and files them against the right claim automatically, and flags what's still missing against the file's requirements - so the adjuster works the claim instead of chasing paperwork.
The problem: underwriting submissions arrive in whatever format the broker sent them in, and turning that into something comparable and priceable takes manual reading and re-keying before any underwriting judgment happens. The fix: a system that reads a submission in whatever form it arrives - PDF, spreadsheet, email - extracts the risk data into a consistent structure, and flags what's missing or needs a supplemental request, so underwriters start from a comparable file instead of a blank read-through.
The problem: renewal volume hits the same weeks every cycle, and endorsement requests need to be read, checked against the current policy, and processed correctly - work that scales with the book but not with headcount. The fix: a system that reads renewal and endorsement requests, checks them against the current policy and underwriting rules, prepares the change or renewal for review, and surfaces exceptions that genuinely need a person - so the seasonal spike stops requiring seasonal overtime.
The problem: coverage nuances, past exceptions, and carrier-specific rules live in a handful of senior people's heads, and everyone else interrupts them to get an answer that should be searchable. The fix: a knowledge assistant built on your actual policy wording, underwriting guidelines, and claims precedent, so staff get a sourced answer directly instead of waiting on the one person who remembers the exception from three years ago.
The problem: fraud indicators show up in different systems and different forms - claims notes, document inconsistencies, patterns across related claims - and no single view puts them together before a payout goes out. The fix: a system that aggregates signals across claims, documents, and related-claim patterns into a single flagged view for your special investigations team, so the pattern is visible before the check clears, not after.
One representative example of an operating system shipped around a real workflow.
Insurance · Case study
We design around the systems of record. Integration scope comes from the real workflow, access rules, and decision boundaries - not a platform replacement plan.
The principles we use when we design, ship, and improve a system alongside the people who run it.
The work still belongs to your operation. We make the repetitive path reliable and visible.
Each system needs a measurable baseline, source traceability, and a clear way to improve when it is wrong.
The best system sits inside the tools and habits your team already depends on.
The system can prepare, route, and remember. Your team keeps the decisions that carry consequence.
How we apply the same operating discipline in other contexts.
AI systems for customer support, retention, order exceptions, and revenue recovery as volume grows.
Explore →AI for Financial servicesAI systems for KYC, onboarding, fund administration, reporting, and controlled compliance operations.
Explore →AI for HealthcareAI systems for revenue cycle, claims, documentation, and patient operations with controls that fit care delivery.
Explore →The things teams ask before the work begins.
Start where the work is frequent, visible, and costly when it goes wrong. The first mapping session identifies the people, systems, data, and controls around that workflow.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
Start with the work
Pick a time for a working session or send a short brief. Either way, we will come prepared to understand where the work gets stuck.
Talk through the work
Bring the workflow that feels slow or fragile. We will determine whether it is a sensible candidate for an AI system.
Bartosz LuderaFounder, HarnessloopSend a workflow brief
Tell us where work waits, repeats, or falls through the cracks.
