Modernize healthcare claims, benefits and member-service platforms with AI—without exposing sensitive logic or losing operational control

Healthcare modernization is rarely a simple technology refresh. Claims adjudication, benefits administration and member-service platforms often sit on decades of COBOL and Synon code, green-screen workflows, batch processes and deeply embedded rules that still determine what gets paid, what gets approved and how members are served. These systems may be old, but they remain business-critical. They contain the operational logic that keeps the organization running.

For many payers and healthcare benefits organizations, the hardest part is not deciding that modernization is needed. It is finding a way to move forward without exposing sensitive source logic, losing hidden adjudication rules or creating operational instability in systems the business cannot afford to get wrong. Documentation is often incomplete. Critical logic may be buried across thousands of screens, copybooks, subroutines and batch jobs. The people who truly understand how the estate behaves may be few in number and increasingly hard to scale.

Sapient Slingshot is built for that reality. It helps healthcare organizations modernize legacy claims, benefits and member-service systems with AI in a more controlled, specification-led model—one designed to recover business logic before rewriting systems, preserve continuity and keep human experts in control.

Why healthcare modernization is uniquely difficult

In healthcare, legacy systems do more than process transactions. They encode policy rules, exceptions, validation logic, field mappings, service workflows and years of institutional knowledge. That logic may govern claims processing, member inquiries, benefits configuration, enrollment administration and downstream reporting. If modernization loses fidelity, the impact is not abstract. It can affect customer service, operational continuity and trust in the platform itself.

This is what makes healthcare different from a simple lift-and-shift or rewrite. Teams are not just replacing old technology. They are trying to preserve decades of business behavior while moving toward more maintainable, cloud-ready architectures.

Traditional approaches struggle because they often depend on manual rediscovery. Teams interview SMEs, reverse-engineer behavior from code and attempt to rebuild future-state systems while still figuring out how the current state really works. That slows modernization, increases risk and puts even more pressure on the small group of experts who already carry too much of the burden.

Read before you rewrite

Slingshot takes a safer path. Rather than moving directly from old code to new code, it starts by analyzing the existing application estate and recovering the business logic trapped inside it. The platform extracts rules, dependencies, behaviors, process flows and data structures from legacy systems, then converts that knowledge into reviewable, testable specifications.

Instead of relying on assumptions, outdated documentation or fragile tribal knowledge, healthcare teams can validate what the current system actually does before modern code is generated. Adjudication logic becomes visible. Hidden dependencies become easier to trace. Business behavior becomes easier to review with product owners, engineers and domain experts. This helps organizations preserve continuity while reducing dependence on scarce legacy SMEs.

For healthcare leaders, this is one of the most important shifts AI can enable: not just faster code generation, but safer modernization grounded in recovered business intent.

Protect sensitive source logic with controlled handling

Security in healthcare modernization is not just about perimeter controls. It is about how sensitive application logic is handled during transformation.

Slingshot is designed to ingest and process client source code in a controlled, time-bound way. Raw source code pulled from client repositories is held only in temporary processing buffers and deleted immediately after vectorization. Only vector embeddings—numerical representations of code logic—are retained, and those embeddings are isolated per client. Client code is not used to train Publicis Sapient models or third-party large language models.

That approach helps healthcare organizations recover logic from proprietary claims and benefits systems without creating unnecessary persistence of raw intellectual property.

Deployment options reinforce that control. Slingshot can be deployed as secure SaaS in a private cloud, on-premises or through a hybrid managed-services model. In dedicated SaaS deployments, environments are single-tenant and isolated, with dedicated compute, storage and databases. Data remains in the chosen region, processing happens in that same region and customers benefit from clear residency and retention controls. Encryption at rest and in transit, role-based access controls and centralized logging further support secure, auditable operations.

For healthcare organizations modernizing sensitive platforms, this provides a practical answer to a common concern: how to use AI without surrendering control of business-critical logic or data handling.

Human validation is built in

AI can accelerate discovery, specification, code generation and testing. But healthcare modernization still requires human judgment.

Slingshot is designed with human-in-the-loop validation so architects, engineers, product leaders and business stakeholders remain accountable at critical decision points. AI-generated outputs are reviewed and validated before they move forward. Quality checks are applied before outputs are incorporated into delivery workflows.

This is essential in claims and member-service modernization, where silent errors can have outsized consequences. Teams need to assess edge cases, confirm rule fidelity and make sure the modernized system behaves exactly as intended. Slingshot helps reduce manual effort, but it does not turn modernization into a black box.

Better testing, stronger continuity

Healthcare modernization programs often slow down in QA because expected behavior is difficult to reconstruct. When legacy logic is undocumented, testing teams are forced to infer what the application is supposed to do.

Slingshot improves this by carrying context forward from code discovery into specification, design, code generation and testing. Automated test generation helps increase coverage and reduce manual QA effort, while the retained chain of context supports more reliable validation of parity between legacy and modernized behavior.

That continuity is a major advantage in regulated, operationally sensitive environments. Requirements, specifications, code and tests stay more closely connected, creating a clearer path from original behavior to release readiness.

Proven in healthcare modernization

This approach is already grounded in real healthcare outcomes. Publicis Sapient used Slingshot to help a leading healthcare benefits provider modernize more than 10,000 COBOL and Synon mainframe screens to improve claims processing and customer service. By uncovering hidden business rules and dependencies, generating specifications and accelerating automated test creation, Slingshot enabled a faster, safer migration. The program achieved 3x faster migration and 30% cost reduction while supporting movement toward a cloud-native architecture.

That story is important because it reflects the exact challenge many healthcare organizations face today: large, business-critical estates with buried logic, aging interfaces and too much dependency on hard-to-scale expertise. The lesson is not simply that AI can speed up migration. It is that modernization becomes more controllable when the platform recovers logic first, documents it clearly and keeps validation tied to human oversight.

A modernization model built for healthcare reality

Generic AI coding tools may help developers move faster, but healthcare core-platform modernization requires more than code assistance. It requires enterprise context, specification-led transformation, controlled source-code handling, deployment flexibility, automated testing and clear governance.

Slingshot brings those elements together across the full software development lifecycle. Its enterprise context graph creates a living map of business logic, workflows, repositories, specifications, data and dependencies, helping teams retain continuity across discovery, design, build, test and deployment. Its modernization workflows are built to preserve business logic rather than guess at it. Its security model is designed to minimize persistence of raw source code while maintaining isolation and control. And its human-centered operating model keeps experts involved where accountability matters most.

For healthcare organizations modernizing claims, benefits and member-service systems, that changes the modernization equation. The goal is no longer just to replace legacy code faster. It is to recover what matters, preserve operational behavior and modernize with confidence.

When the systems at stake determine adjudication, service quality and continuity, that is the AI modernization model that matters.