Receive
7 files attached
FNOL_28471.pdf · estimate.pdf · police_report.pdf
Claims operations
Your adjusters should be deciding claims, not preparing them.
ExceptionLayer handles the preparation surrounding a new claim—reading incoming files, matching policies, extracting and validating information, preparing the case record and routing genuine exceptions for review.
Your claims system stays. Your team retains judgment.
Increase claims capacity without increasing manual preparation at the same rate.
Map a claims workflow
See the exception layer25-minute working session. We start with the workflow and its economics.
Live claim intake09:42:18 CST
CLM-28471
7 documents received
- ·Claim receivedWAITING
- ·Documents classifiedWAITING
- ·Policy matchedWAITING
- ·Fields validatedWAITING
Watching for exceptions…
Route
Pending verification
Authority
Human
Source evidence attachedNo claim decision made
Live claim · CLM-28471
From submission to adjuster-ready.
Follow one claim as documents become structured work, a contradiction becomes an explicit exception, and the adjuster receives the evidence needed to decide.
ClaimCLM-28471Trace active
Understand
Claim recognized
Policy PL-99142-A · insured matched
Verify
Exception found
Loss date differs across two source documents
Prepare
Review packet assembled
31 fields · chronology · source evidence
Route
Adjuster review
Exception reason and evidence delivered together
Learn
Correction captured
Resolution added to evaluation history
The preparation burden
The expensive part of claims isn’t always the decision.
Before judgment begins, teams still spend hours opening documents, identifying claims, finding policies, re-keying information, checking completeness, reconciling conflicting evidence, preparing chronologies and moving work between systems.
ExceptionLayer handles that preparation layer so experienced people can spend more of their time on work that actually requires experience.
Operating economics
Make the economics visible.
The deployment is measured against the operating baseline—not against an AI benchmark.
Illustrative interface — synthetic data
Not customer performance
These aren’t benchmarks. The deployment case is built from your volume, touch time, exception rate and rework.
14,284
Submissions
11,907
Prepared for review
1,481
Missing-information exceptions
896
Judgment exceptions
83.4%
Prepared for review
04:18
Median preparation time
7.2 min
Human touch / submission
2.8%
Rework
0
Autonomous claim decisions
The exception line
Routine work and judgment-heavy work should not travel through the same path.
CLM-28471 leaves the routine path at verification because its source documents disagree. The exception carries its reason and evidence to human review.
Exception architecture
TRACE · CLM-28471
01Input7 documents
02UnderstandClaim + policy matched
03VerifyLoss date conflict
04ForkRoute by state
RoutineApproved action
ExceptionHuman judgment→Approved action
Audit + evaluationEvery route and correction retained
Authority architecture
Some decisions should stay decisions.
CLM-28471 · Evidence delivered to adjuster
System prepares
- — Document classification
- — Structured extraction
- — Policy / claim matching
- — Completeness checks
- — Evidence reconciliation
- — Chronology preparation
- — Task preparation
- — Information-request drafts
- — Exception detection
- — Recommended routing
Human authority
LOSS DATE CONFLICT · CLM-28471
FNOL_28471.pdf: Aug 14 · police_report.pdf: Aug 15
- — Coverage
- — Liability
- — Reserve changes
- — Fraud determinations
- — Claim denial
- — Settlement authority
- — Payment authorization
More operating capacity. Explicit decision boundaries.
Existing stack
A layer. Not another system of record.
You already have systems that hold the claim, policy data, documents and operational history. ExceptionLayer is designed to work across that environment rather than forcing the organization to rebuild around a new core platform.
Integrations are scoped around the systems already running the workflow.
Deployment architectureCLM-28471
Inputs
Email
Documents
Portal / intake
API / SFTP
ExceptionLayerOperational layer
01
Understand
02
Verify
03
Route
Controlled destinations
Exception route
Human reviewEvidence attachedApproved write
System of recordAfter approval where appropriateSystems of record remain authoritative.Least-necessary accessActions logged
Operating equation
Don’t start with AI. Start with the operating equation.
We establish the current-state baseline before designing the deployment.
Volume
How much work enters the queue?
Touch time
How much human time does each case consume?
Exception rate
What actually requires judgment?
Rework
Where does incomplete or incorrect work loop backward?
If the economics don’t justify changing the workflow, there shouldn’t be an AI project.
Assess a workflow →When it becomes worth looking
Operational pressure usually shows up before the AI project.
- 01Claim volume is growing faster than headcount.
- 02Adjusters are spending meaningful time on preparation work.
- 03New client procedures create recurring manual exceptions.
- 04Intake is fragmented across inboxes, portals and documents.
- 05Backlog or SLA pressure is producing rework.
- 06Leadership wants AI adoption without handing consequential decisions to the model.
These are operating problems first. AI is useful only if it changes the equation.
Deployment method
Start with one queue.
01
Map
Observe how work actually moves.
OutputWorkflow + exception map
02
Baseline
Measure volume, touch time, rework, exceptions and cycle time.
OutputOperating baseline
03
Shadow
Run against historical and controlled live work.
OutputEvaluation set + failure taxonomy
04
Verify
Compare against real work and acceptance criteria.
OutputAcceptance threshold
05
Deploy
Introduce controlled production actions.
OutputLive workflow + approval gates
06
Expand
Move to adjacent workflows when economics repeat.
OutputReusable deployment module
Organizations we’re built for
The buying moment is operational, not theoretical.
Third-Party Administrators
When new programs, client-specific procedures and rising volume turn intake into headcount.
Independent Adjusting Organizations
When claim surges consume adjuster capacity before actual adjusting begins.
MGAs & Specialty Operations
When specialized workflows demand automation without flattening expert judgment.
Claims Administrators / Managed Claims Operations
When recurring preparation work constrains service levels, margins or the ability to take on new programs.
Building insurance technology? Explore deployment partnerships →
Control posture
Designed for controlled deployment.
Enterprise AI is an access-control and operational-governance problem as much as a model problem. Deployments are designed around the permissions, data boundaries, approval paths and audit requirements of the workflow.
View deployment architecture →Least-necessary access
Only the systems and actions required by the workflow.
Human approval gates
Configured wherever authority should not be delegated.
Action logging
Operational actions and exceptions should be traceable.
Evaluation before expansion
New capabilities earn production scope through testing.
Bigger vision
Claims is where we’re starting. Exceptions exist everywhere.
The same structural problem appears throughout consequential operations: large volumes of routine work, fragmented systems and a smaller set of cases that genuinely require human judgment.
InsuranceActive
Financial operations
Healthcare operations
Logistics
The first working session
What happens when we look at a workflow together.
Show us the work
Bring one recurring queue and how it moves today.
Establish the economics
Volume, touch time, rework, cycle time and exceptions.
Separate routine from judgment
Identify what can be verified and what must remain human.
Decide whether it is worth deploying
If the economics are not substantial enough, we stop there.
You should leave the first conversation with a clearer operating problem—even if we never work together.
One queue. Measured.
Show us the queue everyone hates.
If your team repeatedly reads, checks, re-keys, reconciles or routes the same operational work before judgment can begin, we should probably look at it.
Map the workflow
We’ll start with the workflow and its economics—not a generic AI demo.