Insurance

Less manual handling. More accuracy.

Opus maps how your claims operation really runs, then rewires the work worth handing to AI, with a person on every decision that matters.

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Use cases

How insurance teams use Opus

Some examples of the high-volume processes teams hand to Opus, each one live in weeks.

FNOL intake & triage

TodayClaims logged and routed by hand.

OpusCaptures the claim, checks what's missing, routes to the right handler.

Fraud & abuse screening

TodaySuspicious claims caught late, if at all.

OpusScreens every claim at FNOL, flags anomalies, routes to investigation with the evidence.

Claim validation & adjudication

TodayFiles checked page by page against policy.

OpusValidates the file, interprets coverage, and drafts the decision with rationale for a human to sign off.

AI underwriting assistant

TodaySubmissions read and summarised by hand.

OpusSummarises the submission, retrieves policy wording, drafts a recommendation and rationale.

Application pre-fill

TodayApplications completed field by field.

OpusPre-fills and validates in real time, flags gaps, produces a ready-to-submit application.

Payment & reconciliation ops

TodayPayments and exceptions managed by hand.

OpusExecutes payments, reconciles, and routes only the breaks, with every step logged.

The problem

You can see the ratio you're measured on. You can't see the work that moves it.

Your operation runs across a dozen systems, on documents, under rules you have to evidence. You can see what it produces: cycle times, cost per claim, leakage. You can't see which steps absorb the effort, where handlers decide differently on similar files, or which change would move the number. So AI programmes start on a hunch. You pay first, and find out later.

How it works

How it works differently

01 · Discover
A costed map of the work, built with the people who do it

We map your claims and underwriting workflows with the handlers and underwriters who run them. Not from system logs, which miss the work happening in inboxes, spreadsheets and phone calls. Not from a process document that stopped being accurate years ago. What you get is a view of what the work costs you today, with opportunities ranked by what fixing each one is worth.
02 · Build
See what AI can do for you, then you decide

Opus shows you which parts of the workflow AI can take on, and which should stay with your team, drawing on a Large Work Model™ trained on millions of real business processes. You can automate the process as it stands, or improve the flow while you do it.

Then you set the rules. What clears without a handler, what gets referred, and at what value. You approve or reject every step, so no system decides what gets paid out without your say-so.
03 · Run
It runs the process you approved, every time

Opus uses AI to design the workflow, not to improvise while it runs. Whoever picks up the file, the same approved steps get followed, and the exceptions you flagged still go to a person. Actions are versioned and logged, and delegated authority stays exactly where you set it. So you can show exactly how any claim was decided, years later.
04 · Optimize
It keeps getting better after go-live

Every run is tracked: cycle times, throughput, referral rates, where exceptions cluster. When the same step keeps failing, Opus flags it and suggests a change, rather than leaving your team to work around it for another quarter. And as your volumes, rules and exceptions shift, it shows you what has drifted, so the workflow keeps matching the work instead of slowly falling behind it.