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Insurance IconInsurance

Insurance Software Development

Policy administration, claims systems and process automation for insurers, MGAs and brokers, built to work with the core platforms you already run rather than around them.

Insurance Architecture Ecosystem
Policy adminClaimsRPAGuidewireDuck Creek
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/// INTRODUCTION

Sodio builds insurance software development covering policy administration, claims management, health insurance platforms and policyholder apps, alongside insurance process automation and RPA across legacy systems.

We also build insurance AI for claims processing, fraud detection, document handling and churn prediction. Insurance runs on systems that were bought a decade ago and cannot be replaced, so most of the useful work is integration and automation around them rather than replacement of them.

Insurance Automation Infrastructure

/// WHAT MAKES INSURANCE DIFFERENT

The Cost Is in the Handoffs

Insurance Operations and Policy Workflow
01/

The Document Business

Insurance is a document business running on systems bought a long time ago. Most of the operational cost is not in any single step — it is in the gaps between them.

02/

Handoff Cost

A claim arrives as a phone call, an email, a photograph and a PDF. Someone reads it, keys it into one system, looks up the policy in another, requests a document, waits, chases, then rekeys the outcome somewhere else. Each handoff adds days and an opportunity for error, and none of it is visible as a line item. It shows up instead as cost per claim and as cycle time.

03/

Automation Over Replacement

That is why automation is where the return is, and why replacement usually is not. Core policy and claims platforms represent years of configuration and regulatory approval. Replacing one is a multi-year programme with a poor success record. Automating the work around it delivers measurable results in months, and the two are not the same proposition.

04/

RPA as an Honest Answer

Where no API exists — and in insurance that is common — RPA against the user interface is sometimes the honest answer rather than a rewrite. It is not elegant. It works, and it does not require the vendor's cooperation.

05/

Explainability Constraint

The constraint on all of it is explainability. An underwriting decline or a claims decision has to be reconstructable, with the factors that drove it. That shapes how AI can be used here: to surface, extract and recommend, with a human accountable for the outcome.

/// INSURANCE SOLUTIONS

Insurance Software We Build

Insurance Software Development

Insurance Software Development

Insurance Mobile App Development

Insurance Mobile App Development

Custom Insurance Software

Custom Insurance Software

Policy Administration Systems

Policy Administration Systems

Claims Management Systems

Claims Management Systems

Insurance Process Automation

Insurance Process Automation

RPA for Insurance

RPA for Insurance

Cloud Insurance Software

Cloud Insurance Software

Insurance Technology Consulting

Insurance Technology Consulting

Health Insurance Platforms

Health Insurance Platforms

Gradient

///AI FOR INSURANCE

AI for Insurance

Insurance AI earns its place where it removes a handoff or catches something before payout. Every model here attaches to a number an insurer already reports: cost per claim, cycle time, fraud loss, combined ratio, renewal rate.

FNOL intake, document extraction, triage and settlement recommendation. The largest operational cost line in any insurer.

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Claims fraud scoring at submission rather than after payout, tuned against your own confirmed fraud history.

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Risk assessment with feature-level attribution and a decision audit trail, for both insurance and lending underwriting.

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Policies, claims forms, medical reports and loss adjuster notes turned into structured data with review workflow.

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Policy questions, claim status and first notice of loss, grounded in real policy data with scoped permissions.

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Renewal prediction with the reason and the intervention, weeks before the renewal notice goes out.

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Actuarial support rather than replacement — models that surface signal your pricing team validates and owns.

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Parametric cover with smart contract settlement, shared claims registries and reinsurance treaty automation.

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Gradient

///RELATED SECTORS

Related Sectors

Fintech, banking, lending, payments and RegTech

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Clinical systems, EHR integration and medical software

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Smart contracts, tokenisation and Web3 infrastructure

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///SYSTEMS AND INTEGRATIONS

Systems We Integrate With

/// Policy & claims platforms
Guidewire
Guidewire
Duck Creek
Duck Creek
EIS
EIS
Sapiens
Sapiens
Applied Epic
Applied Epic
Legacy on-premise cores
Legacy on-premise cores
/// Broker & distribution
Acturis
Acturis
Open GI
Open GI
Comparison site feeds
Comparison site feeds
Carrier quote APIs
Carrier quote APIs
/// Document & intake
Email and post intake
Email and post intake
OCR pipelines
OCR pipelines
DocuSign
DocuSign
Structured claims forms
Structured claims forms
/// Payments & billing
Stripe
Stripe
Adyen
Adyen
Direct debit
Direct debit
Premium finance providers
Premium finance providers
/// Identity & fraud
Onfido
Onfido
ComplyAdvantage
ComplyAdvantage
Sanctions screening
Sanctions screening
Shared claims registries
Shared claims registries
/// Automation
UiPath
UiPath
Automation Anywhere
Automation Anywhere
Power Automate
Power Automate
Custom RPA
Custom RPA
/// AI & data
Python
Python
PyTorch
PyTorch
Document extraction models
Document extraction models
Vector stores
Vector stores
Snowflake
Snowflake

/// HOW WE WORK

How We Work on Insurance Projects

STEP 1

Process and systems audit

We follow a claim or a policy end to end, counting the handoffs and the rekeying. Then we establish what each system genuinely exposes — API, event stream, database, file exchange or nothing at all.

STEP 2

Free solution architecture

Automation candidates ranked by return, integration approach per system, AI feasibility and a costed delivery plan. Yours to keep whether or not you build with us.

STEP 3

Pilot on one process

One workflow, running against real data with real users. Handling time and error rate are measured before and after, so the result is a number rather than an impression.

STEP 4

Production rollout

Extended process by process, with monitoring, exception handling and escalation paths. We do not attempt an estate-wide automation programme in a single release.

///FEATURED CASES

Future-Proof Software for Your Business

At Sodio, we deliver mobile apps, web apps, blockchain (DApps), AI integrations, SaaS platforms, and custom software development. Our innovative solutions are scalable, secure, and user-friendly, designed to drive growth and efficiency, keeping your business ahead in the competitive landscape. Trust Sodio for your digital transformation needs.

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/// FAQ

Frequently Asked Questions

Usually, though the method varies. Modern platforms such as Guidewire, Duck Creek and EIS expose APIs and event streams. Older on-premise systems often mean an integration layer, database-level extraction or scheduled file exchange instead. Where a system has no usable interface at all, RPA against the user interface is sometimes the honest answer rather than a rewrite. We establish what genuinely exists during discovery.

It removes handoffs rather than people. The work that automates well is high-volume, rules-driven and document-heavy: data re-entry between systems, claims document classification, renewal processing, reconciliation and compliance reporting. Underwriting judgement and complex claims decisions stay with humans. Anyone promising to automate those is either overselling or has not read the regulatory requirements.

It can support them. In regulated markets the requirement is explainability — which factors drove the outcome, what the applicant can be told, and a decision trail a regulator can reconstruct. We build models with feature-level attribution and keep a human accountable for declines and referrals. An opaque model making binding underwriting decisions is a compliance problem waiting to surface.

With the same discipline any regulated data demands: access controls, audit logging, encryption in transit and at rest, and developers working against anonymised or synthetic datasets wherever the work allows it. Where medical data is involved, health data rules apply on top.

All three, though the problems differ. Insurers need core system work and integration at scale. MGAs need speed to market on new products and the rating flexibility to support them. Brokers need distribution, quoting across carriers and client management. The engineering discipline is shared; the constraints and timelines are not.

For a defined scope we target a working MVP in 30 days and a core production build in 90. Insurance extends that more often than most sectors: core system integration approval, regulatory review, actuarial sign-off and phased migration all sit outside our control. We flag which apply during the free solution architecture.

/// RELATED SERVICES

Related Services

Internal linking back into the main service tree.

/// GET STARTED

Start With One Process

Tell us which process costs the most to run and which systems it touches. We will prepare a free solution architecture ranking the automation candidates by return, with an integration approach for each system, so you can judge the case before committing.

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