See how Graph Atlas turns policy wording into governed action.
Follow one bounded claims example from source wording and available evidence to explicit policy reasoning, a required next action, and authorised human review where needed.
Illustrative example only. Semantic Labs supports authorised human decision-making; it does not replace evidence verification, legal interpretation or delegated authority.
WHY GRAPH ATLAS
AI can read insurance documents. Consequential outcomes need more than a plausible answer.
Language models can retrieve information, summarise submissions and identify relevant evidence at scale. But when a workflow reaches a coverage, eligibility or referral outcome, the insurer needs to show which policy conditions applied, what evidence was considered, what remained uncertain and what action was authorised.
Language AI helps with the work
- Reads policy wording, submissions and evidence
- Retrieves relevant information
- Summarises and prepares communications
- Identifies possible conditions, exclusions and missing evidence
- Produces useful outputs, not independently authorised insurance outcomes
Graph Atlas governs the reasoning
- Links reasoning to policy source wording
- Represents approved conditions, exclusions and evidence requirements
- Makes dependencies and exception paths explicit
- Records the relevant logic version
- Identifies an authorised next action or accountable human-review point
A neuro-symbolic approach
Language models help identify and structure relevant policy information. Approved, versioned policy logic makes the consequential reasoning explicit, inspectable and testable.
“Anybody can do the magic trick of extracting fields from a document. However, proving the accuracy of such extractions, ensuring they are not an outcome of a model hallucination, and recording the version and outcome become essential in a regulated industry.”
“The combination of capability and certainty is absolutely fire.”
Alex Taylor, Global Head of Emerging Technology, QBE Ventures
The example below shows what this looks like in practice, from source wording and available evidence to explicit policy reasoning, a required next action and authorised human review where needed.
Stage 1: Make policy meaning explicit
A benefit is not just a label. It is a network of definitions, conditions, exclusions, evidence requirements, alternative pathways and exceptions. Semantic Labs structures those relationships and links them back to the source wording.
The structured representation makes each condition inspectable. Where wording, evidence or interpretation is incomplete or ambiguous, the issue is recorded and routed for expert review.
Source Policy Text
'Heart Attack' means an acute myocardial infarction evidenced by the elevation of cardiac troponin in combination with acute ischemic ECG changes.
Structured policy condition
Stage 2: Validate the reasoning
We do not treat a keyword match as a coverage decision. Experts review how policy conditions interact, then approved logic makes clear which requirements are cumulative, which are alternatives, what evidence is sufficient, and when a case must be escalated.
If a required condition is not supported by available evidence—or the matter falls outside approved logic—the system identifies the gap and routes it for authorised review rather than silently resolving uncertainty.
Stage 3: Apply it with control
The output shows the applicable policy version, conditions evaluated, evidence available, unresolved issues and required next action. Straightforward cases can move faster; ambiguous or consequential cases go to an authorised expert with the reasoning already assembled.
Policy A
Requires Troponin elevation
Requires ECG changes
Requires Hospital Admission
Policy B
Requires Troponin elevation
Requires ECG changes
No admission required
Compare what policies actually mean—not just what they cost
Two policies can use the same benefit name while requiring different evidence, conditions or exclusions. Semantic Labs makes the difference explicit and source-linked so product teams, brokers, claims teams, customers and AI assistants can understand the real coverage distinction.
Required action: authorised expert review
AUTHORISED HUMAN REVIEW REQUIRED
• Troponin: 0.85 ng/mL ( > 0.04)
• ECG: ST-segment elevation found
One product intelligence layer. Multiple insurance workflows.
Use the same structured policy logic across the places where getting the answer right matters.
Product comparison
Compare benefit definitions, exclusions, conditions and evidence requirements across policies.
AI-enabled distribution
Give brokers, customers and AI assistants source-linked answers about coverage.
Claims
Map medical and documentary evidence to the conditions that govern a benefit.
Underwriting
Make eligibility, referral, evidence and exception rules explicit and reusable.
Semantic Labs provides the product and policy reasoning layer. Rating, medical underwriting, binding, payment and delegated authority remain connected parts of the wider insurance operating model.
AI handles the complexity. Experts retain the authority.
Semantic Labs does not hide uncertainty behind a confident answer. It shows the policy logic, evidence state, unresolved issues and required next action. Straightforward cases can move faster; ambiguous or consequential cases reach an authorised expert with the reasoning already assembled.
Escape AI pilot purgatory.
Start with one decision that matters. Validate the logic with your experts, run it in shadow mode, measure consistency and review effort, then scale only when the business value and control evidence are clear.
Book a Policy-to-Action Opportunity ReviewStart with one insurance decision that matters.
In a 60-minute Policy-to-Action Opportunity Review, identify where approved policy reasoning can help an AI-enabled workflow move faster without improvising what coverage means.
Book a Policy-to-Action Opportunity Review