Application Re-engineering Services

Transform your architecture for continuous delivery.

Re-engineering legacy software means retaining key business logic while transforming rigid, outdated systems into scalable, cloud-ready architectures. Their structure is often unsuitable for dynamic environments where rapid deployment, modular development, and real-time responsiveness are essential.

We address this problem with precise architectural transformations. Our proprietary AI Solution Accelerator™ guides this transformation end-to-end, making our application re-engineering services faster, more accurate, and safer for complex environments:

• Architecture decomposition and mapping. Using the AI Solution Accelerator™, we analyze the runtime topology, dependency structures, logical flows, and usage density to define service boundaries and isolate high-impact components.
• Design of a cloud-native system. We design a target architecture based on the principles of modularity, statelessness, and elasticity, incorporating containers, service networks, event queues, or serverless functions based on the workload characteristics.
• Incremental transition execution. We plan and execute a controlled migration of application components using interface contracts, API gateways, and side-by-side implementations to maintain functional continuity.
• Platform customization. We configure infrastructure as code, auto-scaling rules, and observability pipelines for integration with your chosen cloud provider or hybrid environment.
• Runtime optimization. We ensure that the re-engineered application supports parallel deployments, faster recovery times, and real-time metric collection for proactive issue detection and resolution.

The result is a durable application core optimized for continuous deployment — a key outcome of our software re-engineering services.

Create clear boundaries for scalable system development.

As systems grow, business logic often becomes entangled in integration layers. Domain-Driven Architecture Mapping restores clarity by aligning the system structure with the actual business functions. Our engineering team leads the process end-to-end, supported by insights from our AI Solution Accelerator™, which enhances visibility, detects inconsistencies, and guides decomposition with precision:

• Strategic Domain Modeling. The AI Solution Accelerator™ maps business operations, user workflows, and data lifecycles, identifying domain boundaries based on interaction density, rule sets, and overlapping responsibilities.
• Identification of bounded contexts. We isolate logical domains with their language, rules, and responsibilities, reducing overlaps, contradictions, and coordination efforts.
• Adaptation of responsibilities. The areas are assigned to the responsible teams to ensure that each delimited context has a clearly defined responsibility and autonomy.
• Context-related contracts. We define integration contracts (APIs, messaging, and common kernels) between domains to enforce consistency and minimize overlapping dependencies.
• Restructuring the source code. We reorganize code bases to reflect domain boundaries by aligning repositories, package structures, naming, and access control with the architectural model.

The result is a system that reflects your business structure.

Restore architectural clarity.

When systems expand, the separation between domain, infrastructure, and interface levels is often removed, and business logic is embedded in controllers. External services are called directly from core workflows. Over time, the technical boundaries blur, which reduces modularity and makes testing more difficult.

We perform a structured stratification process — supported by the insights of our AI Solution Accelerator™, which precisely validates the layer boundaries:

• Delineation of responsibilities. We identify violations of the separation of concerns by detecting logical leaks between the domain, application, infrastructure, and presentation layers.
• Structural reorganization. We refactor the code base to achieve clean layering — isolating domain logic from I/O, protocols, persistence, and external dependencies.
• Enforce dependency direction. We apply control inversion, dependency injection, and port adapter patterns to ensure that the control flow respects architectural boundaries.
• Creation of test harnesses. We enable isolated, deterministic testing of domain behavior without invoking infrastructure, user interface, or integration code.
• Internal API abstraction. We define internal interfaces between layers to improve navigability, reuse, and resilience to change.
• Data re-engineering for consistency. We adapt data structures, transformation pipelines, and persistence logic to the newly defined domain and infrastructure boundaries.

The result is a fully structured code base where responsibilities are aligned, and long-term changes can be made with precision and confidence.

Redesign internal workflows for scalability, isolation, and fault tolerance.

Synchronous chains create latency, propagate errors, and limit system throughput. As applications scale, these patterns lead to operational risks that the static infrastructure cannot absorb. We re-architect the interaction logic in the following dimensions:

• Interaction flow analysis. We review existing synchronization processes, identify latency bottlenecks, and classify processes that are candidates for decoupling and parallelization.
• Event-driven architecture modeling. We define domain events, instruction flows, and messaging schemes using patterns such as publish/subscribe, sagas, and outboxes. The Accelerator provides event flow diagrams and validates the integrity of the message flow.
• Message transport integration. We integrate message brokers (e.g., Kafka, RabbitMQ, NATS) and configure delivery guarantees, dead-letter handling, and idempotency.
• Refinement of transaction boundaries. We redefine consistency units and apply any consistency patterns where strict isolation is not required.
• Traceability and recovery design. We implement complete transparency over asynchronous chains using correlation IDs, distributed tracing, and automatic replay policies.

This transformation increases operational autonomy between services, giving them control over data flow.

Regain control over third-party code

External dependencies are essential, but can bring unseen risks. We audit, reorganize, and stabilize your application’s dependency landscape to support predictable development:

• Comprehensive dependency inventory. We create a comprehensive map of direct and transitive packages, including their versions, lifecycles, and known vulnerabilities, across the entire stack.
• Flattening and deduplication. We remove redundant or conflicting dependencies, fix version mismatches, and consolidate overlapping libraries.
• Compatibility check. We analyze usage context to identify safe upgrade paths, flag obsolete APIs, and isolate sensitive change points.
• Upgrade sequencing and testing. We create a version upgrade strategy that aligns with release milestones, with test harnesses and CI checks that validate the integrity of behavior.
• Policy enforcement. We implement dependency management rules, automated audits, and change approval processes to keep the ecosystem clean after refactoring.

The AI Solution Accelerator™ supports this process by identifying high-risk dependencies.

Fortify your system.

In distributed architectures, resilience is an operational requirement. Systems must remain stable even in the event of partial failures, recover independently, and maintain defined service levels, even under unpredictable conditions. We improve the resilience profile of your platform by developing robust non-functional capabilities at every level:

• Failure Mode Mapping. We assess potential vulnerabilities in services, data streams, infrastructure nodes, and integration boundaries. AI Solution Accelerator™ models propagation paths, highlights individual points of failure, and detects risk signals in system behavior at an early stage.
• Stability mechanisms. We implement circuit breakers, isolators, rate limiters, and timeouts to isolate faults and contain cascading failures.
• Automated failover and self-healing. Using orchestration platforms (e.g,. Kubernetes, ECS, Nomad), we enable redundancy strategies, health checks, and restart logic.
• Integration of chaos tests. We simulate error conditions in staging or production-like environments to validate system behavior in the event of partial failures or restricted services.
• RTO/RPO enforcement. Through structured failback procedures, data replication, and snapshot strategies, we align recovery time and recovery point objectives with your operational service-level agreements (SLAs).

AI Solution Accelerator™ supports this process by continuously monitoring resilience indicators. The result is a system designed for continuity, capable of maintaining operations under pressure.

Centralize core logic.

In complex systems, overarching concerns, such as authentication, logging, telemetry, error handling, and configuration, are often distributed across different services. Centralizing these layers is essential to ensure architectural coherence.

We extract and consolidate the common logic into unified, observable and testable components:

• Identifying and mapping issues. We analyze the codebase and infrastructure to identify behavioral overlaps and fragmented implementations. AI Solution Accelerator™ improves this by detecting usage patterns and uncovering redundancies across domains.
• Separation and modularization. Shared logic is transformed into middleware, sidecar services, or internal libraries, which isolates responsibility and simplify reuse.
• Standardization of policies. We enforce standardized authentication schemes (OAuth 2, OpenID, mTLS), telemetry formats, logging structures, and error handling protocols.
• Adaptation to security and compliance. Compliance-relevant processes, such as audit logs, access policies, and data filters, are centralized and have version control and full test coverage.
• Integration of runtime observability. All centralized concerns are instrumented for end-to-end monitoring through platforms such as OpenTelemetry, Prometheus, and ELK.

This process strengthens the architecture.

Ensure consistent behavior.

In complex systems, critical business logic is rarely centralized; it is scattered across different layers, service boundaries, and legacy components, making it difficult to understand. Even small architectural changes can lead to unpredictable behavior.

We use a multi-level validation model to preserve intent, logic, and operational integrity at every step of the transformation.

• Reconstruction of the domain logic. We extract and document the functional core of your system: workflows, constraints, branching conditions, and business decisions. AI Solution Accelerator™ analyzes runtime traces, call graphs, and event propagation to create an executable representation of your domain behavior.
• Detection of architectural drifts. We monitor logic propagation paths and the resolution of dependencies between versions. The platform detects deltas in the execution sequence, decision branches, and state mutations between legacy and refactored components, even in distributed environments.
• Test strategy anchored in specifications. Using the extracted domain logic, we generate high-confidence test suites that encode behavioral expectations, input ranges, fault tolerances, and cross-component results. This ensures functional equivalence without relying on the assumptions of the legacy code.
• Behavioral differentiation and observability. Golden paths and edge cases are verified using traffic replay, state snapshotting, and time-bound diffing. We detect asynchronous failures, non-deterministic behavior, and silent logic shifts in different implementations with minimal impact on production.
• Runtime guardrails and drift monitoring. Deployed environments include behavioral guardrails — monitors that check the consistency of business rules in real time. AI Solution Accelerator™ logs gaps in test coverage, functional anomalies, and logical regressions across CI/CD cycles and runtime activities.

This approach ensures that re-engineering software improves your architecture without compromising what makes the system business-critical — its logic, correctness, and real-world behavior.

Achieve sustainable progress.

Without a clear structure and direction, technical changes can lead to fragmentation or loss of architectural integrity. We combine automated system insights from AI Solution Accelerator™ with hands-on technical guidance to accompany the transformation at every stage:

• Baseline and System Maturity Scan. We assess architectural cohesion, delivery throughput, and code health to create a clear starting point. Our accelerator highlights risk zones, deviation patterns, and dependency clusters that require immediate attention.
• Modular transformation planning. Changes are broken down into executable, low-risk milestones based on integration boundaries, business impact, and delivery capacity.
• Control by design. We define rules for prioritizing refactoring, managing structural changes, and controlling release gates,  ensuring traceability, auditability, and accountability across all teams.
• Cross-functional alignment. Through shared dashboards, rituals, and performance signals, we ensure that engineering, product, and compliance teams move in sync with transparency at every step.
• Live monitoring and feedback. Structural KPIs are continuously tracked, including coupling, cohesion, test signal strength, and change frequency, allowing decisions to be made based on system behavior in real-time.

This roadmap ensures that modernization is aligned with your long-term architecture and delivery strategy.