Zero code generation
Deterministic configuration makes change safer, testing simpler, and auditability stronger in production.
- Controlled changes and traceability by design
- Predictable behavior in mission critical environments
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Enterprise Grade No-Code
Native, Governed AI
Legacy Apps Refactoring
Build and modernize mission-critical systems, without losing control.
Deterministic configuration for delivery, native agentic AI for control and productivity, and legacy refactoring to modernize safely.
Build enterprise applications faster through deterministic configuration, with governance and auditability by design.
- ✓ Model workflows and business rules
- ✓ Design portals and screens
- ✓ Operate with monitoring and diagnostics
Configure governed AI agents inside the platform to control, validate, score, and assist users in real processes.
- ✓ Orchestrated agents with execution boundaries
- ✓ Native similarity and scoring for controls
- ✓ Agents for document and data validation
Make legacy systems readable and structured to modernize safely, without blind rewrites.
- ✓ Extract rules and knowledge
- ✓ Map dependencies and flows
- ✓ Accelerate governed modernization
Why Works Platform
Deliver and evolve mission critical systems faster, without losing governance.
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Manage complexity at scale
A modular, loosely coupled architecture to assemble capabilities and evolve solutions without rewrites.
- Reusable building blocks across apps and teams
- Enterprise-grade flexibility through configuration
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AI you can govern
Configure agents inside the platform to control, validate, score, and assist users with clear execution boundaries.
- Agent orchestration with full traceability
- Native similarity and scoring for controls
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Enterprise-ready operations
Built-in foundations for running at scale: monitoring, diagnostics, audit, and multi-language support.
- Production observability and troubleshooting
- Governance aligned with enterprise policies
Proven in complex environments
Used to deliver enterprise systems across ports and logistics, banking, public sector, and social security.
Ports and logistics
Banking
Public sector
Social security
How Works delivers in enterprise
A practical, governed path from modeling to production operations, then continuous evolution.
1
▦
Model
Configure data, screens, workflows, and business rules with deterministic behavior.
- Reusable building blocks
- Clear governance and validation
2
⧉
Integrate
Connect to existing systems using enterprise standards and your internal policies.
- Aligns with your architecture
- No forced redesign
3
◷
Deploy and operate
Run at scale with monitoring, diagnostics, logs, and audit readiness built in.
- Production observability
- Traceability and control
4
⌁
Evolve safely
Deliver changes continuously through configuration, with governed AI where it helps.
- Deterministic change governance
- Configured agents with execution boundaries
Use cases for complex, mission-critical systems
Pick your domain. Each use case combines deterministic delivery, governed AI agents for control and validation, and structured modernization when legacy constraints exist.
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Ports and logistics
MISSION CRITICAL OPERATIONS
Port Community and Freight Systems
Orchestrate complex multi-actor workflows, with traceability and controlled evolution across years.
- Process orchestration and exceptions
- Cross-actor portals and controlled access
- Governed agent controls for validation
- Legacy modernization without disruption
Platform
Agentic AI
Legacy Refactoring
🏦
Banking and financial services
GOVERNANCE AND COMPLIANCE
KYC, Data Quality, and Controls
Standardize processes and embed governed AI agents for scoring, anomaly detection, and validations.
- Rules-driven customer lifecycle workflows
- Agents for document and data validation
- Similarity and scoring for controls
- Full traceability with human validation
Platform
Agentic AI
Legacy Refactoring
🏛️
Public sector and regulatory
POLICY-DRIVEN SYSTEMS
Regulatory Platforms and Case Tracking
Build long-lifecycle systems with governed change, auditability, and secure collaboration.
- Structured workflows and approvals
- Portals, forms, and document templates
- Operational monitoring and traceability
- Modernize safely with structured refactoring
Platform
Agentic AI
Legacy Refactoring
🧩
Social security
HIGH VOLUME PROCESSES
Benefits, Claims, and Eligibility
Industrialize processes with rules and traceability, while keeping change governance under control.
- End-to-end workflow automation
- Rules-driven eligibility and decisions
- Agents for validation and consistency checks
- Controlled modernization over time
Platform
Agentic AI
Legacy Refactoring
📄
Claims management
DOCUMENT-HEAVY OPERATIONS
Claims Processing and Controls
Manage complex claims workflows with evidence, traceability, and governed AI-assisted controls.
- Structured workflows and SLA tracking
- Document templates and DMS patterns
- Agents for document control and validation
- Audit-ready operations and monitoring
Platform
Agentic AI
Legacy Refactoring
⚖️
Legal and CASE-style
LONG LIFECYCLE CASEWORK
Case Management and Litigation
Model complex cases, processes, and documents with controlled evolution and full traceability.
- Case workflows and decision traceability
- Document generation and structured evidence
- Agents for control, validation, and scoring
- Modernize safely if legacy constraints exist
Platform
Agentic AI
Legacy Refactoring
Legacy Refactoring
Make legacy systems understandable, governable, and modernizable
Works Platform helps you regain control over long lifecycle applications by turning legacy complexity into structured, validated knowledge. Modernize with method, reduce change risk, and keep auditability and governance under control.
Structured extraction
Safer change governance
Traceable outputs
Turn legacy into structured, validated knowledge
Extract rules, processes, and dependencies into reusable assets you can govern and evolve.
- Inventory and map business logic with clear ownership and scope
- Reveal dependencies and impacts to reduce regression risk
Modernize incrementally with governance built-in
Choose safe modernization paths, keep control, and validate each step with traceability.
- Human validation loops for critical rules and decisions
- Governed change with audit trails and review-ready artifacts
Designed for mission-critical environments where change must be fast, controlled, and auditable.
Legacy Refactoring makes modernization a governed program, not a risky rewrite.
How it works
From legacy complexity to governed modernization
Legacy Refactoring turns what is implicit and risky into structured, reviewable assets. The goal is not “AI output”, it is reliable knowledge you can validate, govern, and evolve over time.
A practical flow teams can operationalize
Each step produces traceable artifacts that can be reviewed by domain experts and reused as the foundation for safe refactoring.
01
Discover and inventory
Identify components, business domains, data objects, and critical paths. Establish scope, ownership, and what must not break.
Component map
Critical flows
Risk hotspots
02
Extract structured knowledge
Turn rules, validations, exceptions, and dependencies into explicit assets that can be reused and governed.
Rules and controls
Data semantics
Dependency graph
03
Validate with human-in-the-loop
Domain experts review, adjust, and approve the extracted knowledge. Validation is recorded and becomes auditable.
Reviewed assets
Approval trail
Test targets
04
Refactor safely, step by step
Modernize in controlled increments using validated assets, predictable change, and continuous verification.
Controlled releases
Reduced regression
Governed evolution
Principle
Modernization succeeds when knowledge is explicit, validated, and governed. Legacy Refactoring makes this knowledge operational, so teams can evolve mission-critical systems safely and continuously.
Outputs
What gets extracted and governed
Legacy Refactoring produces explicit assets that teams can review, approve, and reuse. These assets become the safest foundation for modernization and long-term evolution.
Rules, controls, and validations
Turn implicit logic into explicit, reviewable controls that can be governed and tested.
- Validation rules and constraints, with clear intent
- Exception patterns and edge cases worth protecting
- Test targets and regression scenarios derived from real behavior
Processes, flows, and responsibilities
Make operational behavior explicit: who does what, when, and under which conditions.
- Workflow steps, states, and approval points
- Roles and responsibilities mapped to real operations
- Decision points and exceptions that impact performance and risk
Data semantics and dependencies
Clarify data meaning, ownership, and how systems interact to reduce impact uncertainty.
- Data objects, constraints, and lifecycle signals
- Dependency and impact mapping across components
- Integration touchpoints that must remain stable during evolution
Why it matters
The safest modernization is the one you can explain. By extracting explicit rules, processes, and data dependencies, teams can plan changes, validate outcomes, and keep auditability under control as systems evolve.
Method
From legacy to a governed target, step by step
Legacy Refactoring is not a rewrite. It is a controlled modernization method that makes the existing behavior explicit, then transitions teams to a target model that can evolve safely.
1
Extract and structure
Turn implicit logic into explicit assets teams can review.
- Identify rules, constraints, and exception patterns
- Map flows, responsibilities, and decision points
- Clarify data semantics and key dependencies
2
Validate with business ownership
Align on what must remain true, and what can change.
- Confirm critical behavior and compliance controls
- Prioritize what to modernize first, with risk visibility
- Establish acceptance criteria and regression targets
3
Transition to a governed target
Move to a target model designed for safe evolution.
- Rebuild with deterministic configuration and reusable assets
- Preserve traceability across rules, flows, and approvals
- Deliver iteratively with controlled change and reduced disruption
Outcome
You reduce uncertainty before you change anything. The result is a modernization program that can be planned, validated, and governed, while keeping day-to-day operations stable.
Deliverables
What you get from Legacy Refactoring
The goal is to turn uncertainty into governed assets: a clear understanding of the existing behavior, a controlled target model, and a transition path that teams can execute safely.
Structured understanding of the existing system
A readable view of what the system does and why, with traceability across rules, flows, and data.
- Business rules and decision logic made explicit
- Flow maps, approvals, exceptions, and responsibilities
- Data semantics and dependencies clarified
A validated target model with controls
A target system designed for governed change, with acceptance criteria and regression protection.
- Target workflows, rules, and roles aligned with governance
- Control points and validation patterns defined upfront
- Acceptance criteria and regression targets agreed with owners
A controlled transition plan you can execute
A realistic modernization path that reduces disruption and keeps delivery measurable.
- Sequenced scope with priorities and risk visibility
- Coexistence approach to keep operations stable
- Governance for changes, tests, and approvals along the way
Key message
Legacy Refactoring transforms a risky modernization effort into a governed program: explicit behavior, validated targets, and an execution path that can be controlled over time.
Program readiness
Why Legacy Refactoring works in real enterprise programs
Modernization succeeds when the unknowns are reduced early, governance is explicit, and delivery is sequenced with control points. This is exactly what Legacy Refactoring is designed to produce.
Three conditions that keep modernization under control
Legacy Refactoring creates the discipline needed to modernize with confidence, even when the system is large, old, and business-critical.
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Clarity before change
Make behavior, rules, and dependencies explicit early so the program stops relying on assumptions.
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Governance built into delivery
Define control points, reviews, approvals, and audit trails as part of the refactoring method, not as an afterthought.
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Sequenced scope with measurable outcomes
Prioritize by risk and value, deliver in controlled increments, and protect operations through coexistence where needed.
Outcome
Instead of a risky transformation driven by assumptions, you get a governed modernization program that produces clarity, validated targets, and controlled delivery over time.
Engage
Start with clarity, then modernize with control
Legacy Refactoring is most valuable when it is approached as a governed program, not a one-off assessment. The goal is to make the existing system understandable, define refactoring targets, and deliver modernization in safe increments.
What you get
A structured baseline, a prioritized refactoring backlog, validated targets, and a delivery sequence that protects operations.