AI modernization for regulated industries

In banking, healthcare and the public sector, modernization is never just a technology upgrade. Core systems often carry payment rules, claims logic, eligibility decisions, reporting obligations and years of institutional knowledge. These are the applications the business cannot afford to get wrong. That is why regulated organizations need more than faster code generation. They need an AI modernization approach that protects control as rigorously as it improves speed.

Sapient Slingshot is built for that reality. It helps enterprises modernize legacy systems and deliver new software through a connected, governed software development lifecycle. Rather than acting as a generic coding assistant, Slingshot supports specification-led modernization: reading existing systems, recovering business rules, surfacing hidden dependencies and turning legacy behavior into verified, reviewable specifications before rebuilds begin. The result is a safer path to modernization for organizations that must preserve business fidelity, maintain auditability and keep people in control.

Why regulated teams need more than a coding copilot

Off-the-shelf AI coding tools may help individual developers work faster, but regulated modernization is a system-level challenge. Leaders are not simply asking how to generate code. They are asking whether core business logic is preserved, whether deployment aligns to security and residency requirements, whether changes can be traced back to requirements and whether release decisions can stand up to audit.

Those questions become urgent when delivery is fragmented. Requirements may be spread across documents, backlogs and aging applications. Critical rules may be buried in decades-old code or known only by a shrinking group of subject matter experts. Architecture intent can drift from implementation. Testing teams may have to infer expected behavior. Release evidence is often assembled late, under pressure, instead of being created continuously as work moves forward.

Slingshot is designed to solve that continuity problem. Its enterprise context graph creates a living map of business logic, architecture, repositories, specifications, dependencies, workflows, data and telemetry so context can carry forward across discovery, planning, engineering, testing and deployment. That continuity helps regulated teams move faster without turning delivery into a black box.

Start by recovering what the legacy system actually does

In regulated environments, hidden logic is one of the greatest modernization risks. Claims platforms, payment engines, administrative systems and citizen-facing applications often contain critical behavior that is poorly documented, tightly coupled or embedded in legacy code. If modernization begins from assumptions, defects and rework multiply quickly.

Slingshot takes a different path. Instead of jumping directly from old code to new code, it extracts business rules, dependencies, data structures and process behavior from existing systems and converts them into verified, testable specifications. Those specifications become the source of truth for downstream design, code generation, testing and release readiness.

This specification-led model matters because correctness must be demonstrated, not assumed. Teams can validate functional behavior earlier, review sensitive rules before change begins and trace modern outputs back to original system intent. For regulated leaders, that means payment logic, claims adjudication rules, eligibility decisions and reporting flows are less likely to be lost in translation.

Secure deployment built for policy-sensitive environments

For many regulated organizations, the question is not whether AI can help. It is whether the deployment model is secure enough for sensitive systems and proprietary source code.

Slingshot’s dedicated SaaS deployment is designed for enterprise organizations that need strong security, strict data isolation and clear governance without the operational burden of running the platform themselves. In this model, Slingshot runs in a single-tenant, isolated environment hosted by Publicis Sapient in client-approved cloud regions. Infrastructure is not shared with other clients. Compute, storage and databases are dedicated to the individual environment. Customer data and backups remain within the selected region, and processing workloads execute in that same region.

That combination supports organizations working under data residency, sovereignty and operational risk requirements. Teams gain the benefits of a fully managed SaaS experience while maintaining strong isolation comparable to a client-hosted model.

Controlled source-code handling that minimizes unnecessary exposure

Modernization in regulated industries often requires access to highly sensitive source code and related artifacts. Slingshot is designed to process that code in a controlled, time-bound manner. Raw source code pulled from client repositories is held only in temporary processing buffers and deleted immediately after vectorization. Only vector embeddings are retained, and those embeddings are isolated per client. Client code is never used to train Publicis Sapient models or third-party large language models.

This approach reduces the persistence of raw intellectual property while still enabling AI-assisted modernization workflows. For organizations concerned about how proprietary code is handled, that creates a more controlled operating model than generic AI tools that were not built for compliance-sensitive modernization.

Governance controls that strengthen auditability

Security is only one part of the regulated modernization picture. Leaders also need confidence that the delivery model itself remains inspectable and disciplined.

Slingshot includes role-based access controls so only authorized users can access critical capabilities and assets. Encryption protects customer and application data at rest and in transit. Centralized logging captures access events, authorization activity and other security-relevant actions, with logs retained in the deployment region and rotated according to policy. Data residency and retention are also defined clearly: transient execution data is removed immediately after use, source code is deleted immediately after vectorization and other data types follow stated retention periods, with deletion available on request and during off-boarding.

Together, these controls help regulated organizations align modernization with policy expectations around access, evidence, lifecycle management and operational resilience.

Human-in-the-loop validation keeps accountability where it belongs

AI can accelerate analysis, specification, code generation and testing, but accountability must remain with people. Slingshot is built for human-in-the-loop validation. AI-generated outputs are reviewed, refined and validated before they are incorporated into delivery workflows. Quality checks are applied, and architects, engineers, product leaders and domain experts remain responsible for validating business logic, assessing edge cases and approving critical decisions.

That matters in every regulated sector. A healthcare platform cannot lose adjudication logic. A banking workflow cannot introduce ambiguity into payment mappings or downstream reporting. A public sector system cannot weaken transparency or accountability simply because delivery has been accelerated. Human oversight is what turns AI modernization into a controlled transformation model instead of a risky experiment.

Traceability from requirement to release

Regulated delivery depends on being able to answer simple but essential questions: What changed? Why did it change? Which requirement or business rule does it trace back to? What validation was completed before release?

Slingshot helps create that connected thread across the full SDLC. Requirements can inform backlog creation. Specifications can shape architecture. Architecture can guide code generation. Code changes can connect directly to automated test creation, deployment workflows and release evidence. Because context is retained across stages, testing does not begin from guesswork. It is informed by the same preserved business logic that guided modernization in the first place.

Built for the outcomes regulated leaders care about

Slingshot has been deployed with more than 100 enterprise customers to automate the full software development lifecycle while preserving critical business logic. Organizations have used it to achieve outcomes such as up to 99% code-to-spec accuracy, 40% productivity gains across engineering teams, up to 50% reduction in modernization costs and modernization delivered three times faster than traditional approaches. Other reported outcomes include up to 95% accuracy in business rule extraction, up to 85% first-time pass rate for generated code, up to 5x greater velocity for new feature releases after modernization and up to 80% less expert time required to support modernization projects.

In healthcare, Slingshot has helped modernize large COBOL and Synon estates supporting claims processing and customer service, uncovering hidden rules and dependencies while accelerating migration and reducing manual QA effort through automated test generation. In banking, it has supported modernization of complex mainframe and payments-related environments by analyzing hundreds of files and large volumes of code to produce specifications, mappings, flowcharts and target-state artifacts with greater speed and accuracy.

Modernize faster without losing control

For regulated enterprises, the goal is not speed at any cost. It is faster modernization with governance intact. That requires secure deployment, regional data residency, controlled source-code handling, RBAC, centralized logging, specification-led transformation and human review at the moments that matter most.

Sapient Slingshot provides that path. It helps banking, healthcare and public sector organizations recover business rules from legacy systems, preserve sensitive logic, trace changes from requirement to release and modernize mission-critical applications without sacrificing control, auditability or business continuity.