AI-Led Application Modernization Platform for Complex Enterprise Estates
SHIFTAIon is an AI-led application modernization platform that helps enterprises migrate, modernize, and rationalize large, complex software estates. It maps every component and dependency, ties each migration decision to the target architecture you have defined, and coordinates specialist AI agents across tightly controlled delivery waves.
Most AI coding tools stop once the code is written. SHIFTAIon migration factory goes further, testing every proposed change against the reference contracts, the behavior the business expects, and agreed acceptance rules. The platform runs in an environment you approve, plugs into the engineering toolchain you already use, and keeps you in direct control of who gets access, which AI models run, and how evidence and releases are handled.
Why SHIFTAIon
AI Tools Generate Code. SHIFTAIon Proves It Works.
Legacy system modernization programs stall when teams cannot see the full estate, agree on a target, or prove that transformed applications still behave as the business expects.
Today
- Services, APIs, jobs, and shared dependencies hidden across repositories and teams
- Fragmented target decisions create conflicting patterns and costly exceptions
- Context lost between assessment, build and review
- Code compiles, but behavior, contracts, and security stay unproven
- Progress reported as agent activity and code volume
With SHIFTAIon
- One evidence baseline of components, contracts, dependencies, and gaps
- Target-architecture decisions recorded with rationale and owner approval
- Context and approvals travel with every component and wave
- Contract, behavior, and security parity verified before acceptance
- Progress reported only as deliverables that are verified and accepted
Platform Capabilities
AI Capabilities Across the Application Modernization Lifecycle
Estate Intelligence
Understand the full application estate, with evidence, before any scope is committed.
Component and dependency mapping
Map services, APIs, jobs, data flows, and shared dependencies into business-aligned components before committing scope.
Reference behavior baseline
Record existing contracts, error handling, and observable behavior so every change is measured against a clear benchmark.
Brownfield-ready grounding
It grounds AI work in real source evidence, tests, and standards; gaps are flagged for owners, never assumed.
Evidence-based disposition
Recommend whether to migrate, modernize, retain, or retire, while accountable owners make the final call.
Architecture-Directed Planning
Blueprints and constraints become ordered, owned, and repeatable delivery plans.
Target architecture alignment
Turn blueprints, design constraints, and disposition choices into repeatable decisions with explicit, approved exceptions.
Dependency-aware delivery waves
Group work into ordered waves, each defined by its prerequisites, change boundaries, acceptance gates, and a named owner.
Reusable migration recipes
Apply qualified, versioned recipes to similar workloads instead of starting from scratch each time.
Learning across waves
Turn approved fixes, recurring failure modes, and the decisions reviewers make into engineering assets that every new wave can reuse.
Governed AI Agents
Purpose-built agents work within firm limits, with people accountable at every checkpoint.
Specialist AI agents
Purpose-built agents each take on one qualified task, whether that is analysis, migrating legacy code, or verification.
Bounded authority
Each task runs within firm boundaries: set inputs, approved tools, permitted changes, a cap on attempts, and mandatory human checkpoints.
Workload-aware orchestration
Ready branches run concurrently, while checkpoints, budgets, and policies keep execution on their approved path.
Agent observability
Trace tool calls, retries, failures, and model consumption for audit, diagnosis, and cost control.
Verification & Assurance
Every change is proven against the baseline and approved by a person, not by the agent.
Contract and behavior parity
Check every transformed service against its reference contracts, payloads, and error cases, as well as against what its consumers expect.
Independent quality checks
Evidence comes from deterministic builds, regression testing, and security scanning, not just from the agent’s account of its own work.
Human review gates
Reviewers examine a specific version and either approve it or ask for a targeted fix. Agents are never allowed to sign off on their own changes.
Evaluation-driven updates
Recipe and model changes are tested against reference workloads before human-approved promotion.
Modernization Use Cases
One evidence model applied across the full range of modernization work.
Cloud and platform migration
Move APIs, services and supporting workloads to selected cloud or runtime targets with verified sequencing.
Legacy framework modernization
Update languages, frameworks, service boundaries and deployment patterns against an approved behavior baseline.
API and service renewal
Renew interconnected APIs and services, Mule to Apigee paths included, without breaking the contracts that consumers rely on.
Connected product and mobile
Modernize mobile modules and the cloud back ends behind them, keeping device interactions and user journeys intact.
Batch and file workloads
Bring scheduled jobs and file transfers into the same evidence model, subject to qualified adapters.
Application portfolio rationalization
Bring overlapping services together under a single blueprint, with every decision to retain, replace, refactor, or retire clearly documented.
Three Layers, One Migration Factory
Intelligence layer
AI grounded in real project context analyzes source code, dependencies, and contracts, then produces an evidence baseline and recommendations that fit your target state.
Execution layer
Specialist AI agents carry out qualified migration tasks using reusable recipes and approved tools, working within set budgets and checkpoints.
Assurance layer
Acceptance rests with human reviewers, backed by deterministic tests, behavior parity checks, and security scans. No agent approves its own work.
SHIFTAIon Platform Architecture
One Governed Modernization Stack, from Legacy Codebase to Release Acceptance
Built for Governed Enterprise Modernization
Governance That Covers How AI Works, Not Just the Code It Writes
Enterprise security boundary
Enterprise security boundary
You choose the hosting and manage identity yourself. Tool access follows least privilege, secrets stay managed, and each migration runs in its own isolated workspace.
Built-in AI governance
Built-in AI governance
Only approved model endpoints are used, agent instructions are version-controlled, and consumption budgets and tool permissions are defined up front.
Human decision authority
Human decision authority
Architects approve target direction. Owners from product, security, and release teams oversee exceptions and acceptance, and approve production releases through the CI/CD gates you already run.
Audit-ready evidence
Audit-ready evidence
Retain run traces, tool activity, attempts, failures, and review decisions for audit, diagnosis, and cost control.
How It Works
A governed application modernization strategy in six stages. Every stage hands its estate context, decisions, and artifacts on to the next, along with clear accountability for each.
- 01Discover
Identify components, contracts, dependencies, and data flows, and surface anything still unknown.
- 02Decide
Set priorities and disposition in line with the target architecture, and document the reasoning behind each choice.
- 03Plan
Build dependency-aware waves with owners, recipes, and acceptance gates.
- 04Run
Specialist agents execute qualified tasks with checkpoints and bounded concurrency.
- 05Prove
Validate the code, its contracts and behavior, and its security posture, then accept the work or send it back for correction.
- 06Report
Wrap up every wave with a record of accepted artifacts, outstanding exceptions, and rework history.
Measurable Outcomes for Legacy Software Modernization
Defensible Scope
Dependencies, exclusions, and gaps are visible before you commit.
Smarter Investment
Explicit trade-offs focus engineering on the right candidates.
Repeatable Engineering
Proven patterns cut rework across similar workloads.
Outcome Confidence
Behavioral checks and sign-offs turn changes into accepted results.
Transparent Performance
Wave scorecards show accepted work, review load, rework, cycle time, and agent run cost.
Who It’s For
Enterprise Application Modernization for Every Part of Your Estate
Built for CIOs, CTOs, VPs of Engineering, and enterprise architects running repeatable modernization programs.
Manufacturing & Connected Products
Modernize device-facing apps and the cloud services behind them.
- Mobile and product modules
- Batch and file workloads
- Preserved device interactions
Technology & Software Platforms
Renew API and service portfolios without breaking consumers.
- Framework upgrades
- Contract-safe API migration
- Portfolio rationalization
Retail & Distributed Operations
Move distributed workloads to cloud targets in governed waves.
- Cloud and platform migration
- Dependency-aware sequencing
- Verified cutover
Frequently Asked Questions
How is SHIFTAIon different from AI coding tools?
Coding tools produce changes. SHIFTAIon is an application modernization platform that connects discovery, architecture decisions, coordinated migration, independent verification, and evidence-based acceptance across a whole program. It is built for existing estates, not greenfield development.
Can it handle poorly documented legacy systems?
Yes. It combines source analysis with available contracts, tests, reference behavior, and expert input. When business rules remain unknown, owners resolve them before accepting the work.
Which workloads does it cover?
It covers qualified API and service migrations, legacy framework modernization, connected application modules, and batch or file workloads, including OOJS-to-NestJS and Mule-to-Apigee paths. Exact coverage depends on source evidence, recipes, target architecture, and adapters.
Does SHIFTAIon decide whether to rehost, refactor, replace, or retire?
It recommends options based on evidence. Target direction and major exceptions stay with your accountable decision-makers.
How is success measured?
Against agreed contracts, reference behavior, automated tests, security requirements, and named reviewers. KPIs include accepted units, first-pass acceptance, contract coverage, review effort, rework, cycle time, and agent run cost, all measured against a Wave 0 baseline. This is evidence within defined criteria, not a promise of zero risk. Planning targets are never reported as realized savings.
Does it fit our tools and security policies?
Yes. It connects to your repositories, CI/CD, and approved model endpoints, under your policies for identity, secrets, isolation, and retention. Agents run under task scopes, attempt limits, and budgets, with independent checks and human reviewers controlling release.
Is it software or a managed service?
SHIFTAIon is deployable enterprise software. You host and operate it with your own teams. ACL Digital offers optional deployment, adapter development, pattern configuration, and enablement services. Commercial packaging is confirmed during engagement.