Aelix Assurance · InsurTech claims automation
Settle the simple claims straight through. Catch the suspicious ones before payout.
Aelix Assurance is an AI-driven P&C claims platform that ingests First Notice of Loss, reads the documents, triages the damage, scores fraud, and auto-adjudicates the straightforward claims. While routing the risky ones to your adjusters and SIU. One authenticated pipeline, running inside your perimeter.
Aelix Agents · Agentic AI
An agentic layer over the claims pipeline.
Assurance now includes an agentic layer. Autonomous, tool-using agents that act through Assurance's governed APIs, run the routine claims work, and escalate the judgment calls to a human. Every agent is human-in-the-loop by default, runs inside your perimeter, and writes each step to the same audit trail as the rest of the platform.
Claims Adjudication Agent.
Runs FNOL to extraction to triage to fraud score and auto-settles the clean claims, routing the rest with a recommendation.
FNOL Intake Agent.
Conducts the first-notice conversation, validates documents, and opens a structured claim.
Fraud Investigation Agent.
Builds the SIU case file (linked claims, anomalies, prior history) for an investigator to act on.
Agents act only through permissioned, least-privilege APIs. They never settle a claim, deny coverage, or push an investigation outcome without a credentialed adjuster's sign-off, and the SIU keeps the final call on every fraud route. The reasoning model is a swappable component: run a local model on-prem for full data residency, or a hosted model where policy allows.
The problem
Two costs that pull in opposite directions.
Property & casualty claims operations are stuck between two costs that pull in opposite directions: the cost of moving slowly, and the cost of moving carelessly.
Manual and slow.
Adjusters re-key invoice line items by hand, chase documents across inboxes, and eyeball damage photos. A simple auto claim can sit in a queue for days before anyone touches it. While the policyholder's patience, and their loyalty, drains away.
Leakage-prone.
When every claim is worked the same way, overpayment hides in the volume. Inconsistent severity calls and missed duplicate line items quietly inflate loss costs across the book.
Reactive on fraud.
Fraud is too often caught after the check is cut. By the time a pattern is obvious, the money is gone, and the SIU is doing recovery instead of prevention.
Assurance closes all three gaps in a single pipeline. Built as real, working software, not a slide.
Available today
Shipped, running code. One pipeline, end to end.
One pipeline: FNOL to decision
Assurance runs a complete P&C claims-automation pipeline as connected, asynchronous workers. A claim enters through First Notice of Loss, flows through document extraction, damage triage, and fraud scoring, and lands on a decision (auto-approved, routed to an adjuster, or escalated to SIU) without a human touching the routine steps. Each stage (claims, FNOL, vision, fraud, policy, rating, retention, audit) is a distinct domain, so each is independently observable and independently improvable.
Real document extraction with local OCR
Document extraction runs on real local OCR, including multi-page PDFs. Extracted line items carry a per-field confidence so adjusters see what was read and how reliably. A clearly labelled deterministic fallback keeps the pipeline running rather than failing the claim, and a managed cloud-OCR option can sit behind the same extraction interface.
Damage triage that reads the actual image
Damage triage is genuine image analysis, not a placeholder. Assurance reads the real uploaded image bytes and computes pixel and region features (edge density, dark fraction, brightness variation, saturation, and a 3x3 region-damage grid) then a trained severity model maps those features to a severity class (minor / moderate / major / total). Bounding boxes come from the real region grid, and each region gets a repair-type narrative and an estimated cost. We are precise about what this is: an honest heuristic triage that prioritizes and pre-fills, not an object-detection model. That distinction is documented in the model card.
A real fraud model inside the adjudication path
Fraud scoring is not a dashboard you check after the fact. It is a trained model sitting in the decision path. A gradient-boosted classifier produces a 0 to 100 fraud probability per claim, and that score routes the claim: at or above the SIU threshold it is auto-routed to the Special Investigations Unit with a recorded detection. At or below the auto-approve threshold it clears straight through. Everything in between goes to an adjuster. A transparent rule-based scorer is the deterministic fallback if the model artifact is ever unavailable, so the pipeline never stalls.
A real actuarial rating engine
Rating is a working multiplicative actuarial computation over documented rating tables. Base rate by product, then relativities for class, driver age, prior claims, territory, coverage, telematics, and mileage. Not RNG and not hardcoded. Each factor comes from a lookup table or a documented curve, and the quote carries a confidence that reflects how complete the risk inputs are.
Proactive retention, not just claims
Keeping a policyholder is cheaper than winning a new one. Assurance runs automated retention workflows. At-risk scoring that triggers renewal outreach, offers, and follow-up tasks. So the relationship is worked before renewal, not after a lapse.
Enterprise security, built in
Assurance ships with the controls regulated carriers ask for in diligence. CSRF protection on every state-changing call, PII log scrubbing that redacts emails, phone numbers, card-like numbers, and tokens before they hit a log line, field-level encryption at rest on the most sensitive PII columns, strict file-upload validation, and no default admin credential. RBAC and audit logging run across the platform.
How it works
One continuous flow from notice of loss to decision.
01
Claim intake (FNOL).
A loss is captured through guided First Notice of Loss, with documents and photos attached and validated on upload.
02
Extract.
Real local OCR reads the documents and pulls structured line items with per-field confidence.
03
Triage.
The vision worker analyzes the actual image pixels and a trained severity head classifies damage by region, with bounding boxes and estimated repair cost.
04
Score.
The trained fraud model produces a 0 to 100 fraud probability from the claim's features.
05
Auto-approve or route.
The score decides: clear straightforward claims automatically, route mid-risk claims to an adjuster, and escalate high-risk claims to SIU with a recorded detection.
06
Adjuster review.
Humans work the claims that need judgment (with the extraction, triage, and fraud signal already assembled in front of them) and every step lands in the audit log.
The honest metrics
Measured numbers, not marketing literals.
We tier our claims by what is provable in code, and we report measured numbers. Not marketing literals.
Metrics are computed, not hardcoded.
OCR, vision, and fraud metrics are computed from a held-out evaluation set, not typed into a slide. Accuracy is validated on your own claims book during deployment before it is quoted.
A real fraud model in the decision path.
Gradient-boosted scoring that actually routes claims to auto-approve, adjuster, or SIU.
Real OCR and real image analysis.
Local OCR over documents. Pixel and region features plus a trained severity model over photos.
Security hardening as a differentiator.
CSRF, encryption at rest, PII scrubbing, validated uploads, and no default credentials are closed and in the code. Not on a backlog.
On-prem-first, cloud-portable.
Runs inside your perimeter today, with a clean path to cloud.
Security and compliance
Engineered for carriers that have to keep PII inside their own perimeter.
SOC 2-aligned controls.
Role-based access control and audit logging across the platform, CSRF protection on every state-changing call, and file-upload validation on the document path.
Encryption at rest.
Application-level encryption on the most sensitive PII columns, so ciphertext is what lands on disk.
PII stays out of logs.
A log-scrubbing processor redacts emails, phone numbers, card-like numbers, and tokens before they are written.
No default credentials.
Admin access is provisioned per deployment. There is no shipped default to find.
Designed to support GDPR, and on-prem-first.
Single-host deployable with no exposed datastore ports, and a clean path to AWS/GCP/Azure when you want it.
SOC 2-aligned controls. SOC 2 Type II and ISO 27001 audits planned.
See it on your claims
See Assurance on your own claims.
Bring your claim types, your documents, and your fraud thresholds. We will show you a claim move from FNOL through OCR, triage, and a real fraud score to an auto-approval or an SIU route. End to end, on real working software, inside your perimeter.
Frequently asked
Questions teams ask in the second meeting.
Is the fraud model real, or is it a rules engine in disguise?
Real. It is a trained gradient-boosted classifier that outputs a 0 to 100 fraud probability per claim, and that score routes the claim. Auto-approve, adjuster review, or SIU escalation. A transparent rule-based scorer exists only as a deterministic fallback if the model artifact is ever unavailable. The model is retrained and validated on your own claims data during deployment.
Is the damage detection actually computer vision?
It is genuine image analysis: we read the real uploaded image bytes, compute pixel and region features, and a trained severity head classifies the damage by region with bounding boxes and cost estimates. We are deliberately precise. This is honest heuristic triage, not object detection.
Where do your accuracy numbers come from?
From a held-out evaluation set, computed by the platform rather than hardcoded. We do not publish accuracy figures until they are validated on your own claims book during deployment.
How is it secured?
CSRF protection, field encryption at rest, PII log scrubbing, file-upload validation, and no default admin credential, on top of RBAC and audit logging. SOC 2-aligned controls. SOC 2 Type II and ISO 27001 audits planned.
How does it connect to our core policy and claims systems?
Core-system integration is scoped per deployment as an integration step, not a rewrite. Assurance never fakes a vendor call: an integration that is not connected refuses rather than fabricating a result.
Need this tailored to your environment?
Every Aelix product can be configured, extended, or built bespoke for your industry, data sources, and compliance constraints. Talk to our engineers about what would change.
Configurable workflows
Adapt rules, thresholds, and approval flows to match your operational policies.
Custom data integrations
Connect to your specific ERP, MES, SCADA, CRM, or proprietary systems.
Bespoke modules
Build product extensions tailored to your industry, region, or compliance needs.