Intake buries adjusters.
Every claim arrives as a pile of PDFs, photos, and emails. Adjusters spend more time assembling the file than deciding it.
Insurance & claims
Adjudicate routine claims in minutes, not days.
First-notice-of-loss, document intake, policy checks, and fraud signals are repetitive until they aren't. Qrambo handles the clean claims end-to-end and hands adjusters the ambiguous ones with the evidence pack already built.
Trusted by teams shipping AI to production
Why it stalls
The work that eats insurance ops teams alive.
Routine claims deserve consistent calls.
Straightforward claims still wait behind complex ones, and two adjusters can reach two answers on the same policy language.
Ambiguity is where leakage hides.
Push everything to automation and you pay claims you shouldn't. The fix is a reviewer on the ambiguous ones, not on all of them.
How it works
How Qrambo runs insurance operations.
AI runs the flow, your people own the last call, and the system learns from every correction. Live in production in about three weeks, not six months.
Assemble the FNOL file automatically.
Qrambo reads each first-notice-of-loss as it arrives (claim forms, photos, repair and medical documents, email threads), extracts the loss facts, and matches them against the policy on record. Adjusters open a decision-ready file instead of a folder of PDFs, so intake stops being where the hours go.
Adjudicate the clean claims, route the ambiguous ones.
The AI settles first-pass claims that fall clearly inside policy terms and hands adjusters the ones with coverage gaps, conflicting evidence, or fraud signals, with the evidence pack already built and one click to approve or deny. That split keeps a human on exactly the claims where leakage hides.
Tighten with every adjuster decision.
Each override teaches the model your policy language and reserve logic, so its calls track your desk over time. Accuracy improves roughly +1.4 points a week off a flat 78% baseline, sharpening the edge cases most prone to leakage.
Where Qrambo starts
The workflows we ship first for insurance.
Each one runs the routine cases end-to-end and routes the judgment calls to your team, with a clean audit trail behind every decision.
Insurance ops
Underwriting.
Turn messy submissions into decision-ready files: extract appetite fields, score against rules, and route before an underwriter opens anything.
Insurance ops
Claims management.
Clear FNOL intake so adjusters start at the decision: extract loss details, check coverage, settle first-pass claims inside policy.
59%
First-pass claims adjudicated without adjuster review
post-week-4 average
4.1s
Median time to route an ambiguous claim to an adjuster
p99 · 8.2s
3×
Faster first-notice-of-loss intake with automated document assembly
vs. manual baseline
30days
From kickoff to first workflow live in production
median
Go deeper
Playbooks for insurance ops.
The benchmarks and playbooks behind the work, with the numbers, the flows, and widgets you can play with.
Industry
First-pass claims adjudication in minutes, not days
FNOL, document intake, and policy checks are repetitive until they aren't. Here's how to adjudicate the clean claims end-to-end and hand adjusters the ambiguous ones, evidence-ready.
Industry
First-pass claims review: patterns, anti-patterns, and pitfalls
What separates a claims review agent that actually ships from one that lives forever in a sandbox. Patterns, anti-patterns, and the pitfalls we have hit.
FAQ
Insurance AI agents, answered.
How does Qrambo handle first-notice-of-loss (FNOL) intake?
Qrambo ingests each FNOL as it arrives (claim forms, photos, medical or repair documents, and email threads) and assembles a structured file: extracted loss details, the matched policy, coverage checks, and any missing items flagged. Adjusters open a decision-ready file rather than a pile of PDFs, which is where most intake time is spent today.
Will it auto-pay claims it shouldn't? How do you control claims leakage?
Qrambo only adjudicates claims that fall clearly inside policy terms; anything with a coverage question, conflicting evidence, or a fraud signal routes to a human adjuster before payment is authorized. That split, automation on the clean claims and an adjuster on the ambiguous ones, is deliberate, because leakage hides in the ambiguous cases, not the straightforward ones.
How does Qrambo check coverage against the policy?
For every claim, Qrambo pulls the policy on record and verifies the loss against its terms (limits, exclusions, effective dates, endorsements) and surfaces any mismatch for review. When policy language is genuinely ambiguous it does not guess; it escalates to an adjuster with the conflicting clauses highlighted, in a median of about four seconds.
What does the audit trail look like for a regulator or reinsurer?
Every claim decision is logged end to end: the documents received, what the AI extracted, which policy provisions applied, the fraud checks run, and the adjuster who approved or denied it. When a regulator, auditor, or reinsurer asks why a claim was paid, you produce a reviewer-attributable record instead of a model output.
How does Qrambo spot potential fraud?
Qrambo flags fraud signals as it builds the file: inconsistent dates, duplicate or altered documents, prior-claim patterns, values out of line with the loss. It routes those claims to an adjuster rather than adjudicating them. It does not decide fraud on its own; it makes sure a human sees the claims that warrant a closer look, with the evidence already assembled.
How fast can we get a claims workflow live?
Most pilots run first-pass claims review live in about 30 days on one line of business. You begin with an adjuster approving every AI adjudication, then widen the auto-settle band as accuracy holds week over week on your own claims.
Give adjusters the ambiguous claims, not the intake pile.
Point Qrambo at one line of business and we will have first-pass claims adjudicating in about 30 days: clean claims settled end to end, the ambiguous ones on an adjuster's desk with the evidence pack already built.