Every policy decision, insurable and explainable in the same breath.

Underwriting and claims run on judgment that has to hold up to a regulator, a reinsurer, and a policyholder, sometimes all three at once.

Request a demo See use cases

What we consistently hear

The same handful of pain points, in every conversation with credit, fraud, compliance, and relationship management teams.

Underwriting consistency varies by reviewer

The same submission can get different terms depending on which underwriter's desk it landed on, with no clean record of why.

Complex claims stall on coordination, not complexity

A multi-party claim doesn't sit open for months because it's hard. It sits open because one adjuster is chasing five parties alone.

Quarterly close eats the time meant for the board pack

Reconciling premium, reserving, and reporting across separate systems consumes the weeks that should go to actually reading the numbers.

First notice of loss depends on the channel it arrived through

Phone, web, or app, intake gets handled a little differently each time, and structured data isn't guaranteed until someone keys it in by hand.

How Zentis addresses it

Not a bolt-on feature for each pain point. One governed platform that removes the root cause of all of them.

Portfolio-wide consistency, checked before it's bound

A SPAR adversarial pass flags where one underwriter's call would have disagreed with another's on a similar risk, before either reaches a bound policy.

Claims that carry their evidence

From first notification to adjudication, every claim moves with the documentation and reasoning that produced the decision already attached.

Forecasting, reserving, and reporting on one core

Policy Intelligence and Claims Intelligence in Zentis Analytics run off the same live data all quarter, not four systems reconciled at the end of it.

Every channel feeds one structured intake

Phone, web, and app claims get captured and classified the same way, so routing doesn't depend on who happened to answer.

Use cases in Insurance

Specific workflows, worked through end to end.

Insurance · United Kingdom

Portfolio risk review for a specialty carrier

6 weeks → 48 hours

Portfolio risk review cycle

View use case →Insurance · United Kingdom

End to end motor policy automation

11 days → 4 hours

Quote to policy issuance

View use case →Insurance · Singapore

Quarterly close and reporting for a life insurer

9 weeks → 2 days

Quarterly board pack turnaround

View use case →Insurance · United States

Resolution for stalled complex claims

90%

Cut in complex claim resolution time

View use case →Insurance · United States

Voice automation for a multi state contact center

40%

Reduction in average handle time

View use case →Insurance · India

First notice of loss, automated across channels

48 hours → 15 minutes

FNOL processing time

View use case →Insurance · United Kingdom

Marine risk assessment for a Lloyd's syndicate

5 days → 6 hours

Submission to quote turnaround

View use case →Insurance · Global · Marine

Cargo and hull claims settlement

9 days → 2 hours

Claim intake to settlement recommendation

View use case →Insurance · Global

Health insurance underwriting automation

4 days → 30 minutes

Application to coverage decision

View use case →Insurance · Global

Health claims adjudication for a TPA

6 days → 3 hours

Claim intake to adjudication decision

View use case →Insurance · India

Life insurance underwriting automation

7 days → 4 hours

Application to acceptance decision

View use case →

More workflows are being modelled

If yours isn't listed yet, that's a conversation worth having with Zara.

Built for how this industry is actually regulated

Lloyd's market requirements, IRDAI, and FCA, with regulatory packs swapped per geography, not rebuilt. Deployment runs as SaaS, private cloud, on-premise, or fully air-gapped, with model choice, OpenAI, Anthropic, Google, open-weight, or your own, set per agent at runtime. GDPR compliant, SOC 2 and ISO 42001 certified, regardless of which option you choose.

Why teams choose this over the alternatives

Most insurers evaluating this work compare a horizontal, no-code platform against an internal build. Neither ships with insurance-native agents, an adversarial check built into the cycle, or jurisdiction-aware regulatory packs for the markets you actually write in. Zentis starts with all three in place.

Three answers, before you have to ask.

The economic buyer is assessing career risk, not capability. These are the three doubts, answered inline.

The full detail lives on the Trust page.

Determinism

What happens when the model is wrong

Deterministic rules run before any model does, and roughly 69% of narration-audit records settle on rules alone. Below the confidence threshold the workflow escalates to a human rather than guessing.

Deployment

Where the models run, where the data sits

Your choice per deployment: SaaS, private cloud in your own subscription, on-premise inside your data centre including the models, or fully air-gapped with no egress at all.

Regulatory

Can this be shown to a regulator

Every decision carries a defence file: source, rule applied, confidence, adversarial record, and the named approver. Regulatory overlays ship per geography, versioned alongside the workflow.

A bounded way to find out.

No quarter-long RFP. A working session on your documents, with the challenge step switched on. If the controls it extracts are wrong, you will see that immediately — which is the point.

DurationSix to eight weeks, on a process you name.

What we need from you

A named process, and the documents it touches
One working session with the team that owns it
The regulatory pack for the geography you run in

What you get at the end

A running agent team on your data
The defence file for a real decision, end to end
A written readout: what held, what did not, what production takes

The same cycle, whatever the domain.

Insurance work goes through SPAR like everything else. The domain knowledge changes; the governance does not.

SaaSPrivate cloudOn-premise

Model choice is configuration, per agent, at runtime — including your own on-premise model.

SSourcePulled from the system of record, with provenance and hash captured at ingest.provenance · hash

PProcessDomain specialists run in parallel under a schema contract, so bad payloads fail loudly.schema contract

AAdversarialA separate agent, on a different model, tries to break the result.cross-model review

RReleaseNothing leaves without a named human approving it. Citations, confidence, all of it.named approver

See insurance on your own documents.

A working session, on your data, with the challenge step switched on. If the controls it extracts are wrong, you'll see that immediately — which is the point.

Book a demo