Modernise What Works. Evolve What Holds You Back. Mobiloitte Singapore helps organisations modernise existing applications, SaaS platforms and enterprise systems without automatically committing to a full rewrite.
We assess the software, data, integrations and business logic already in place and determine what should be retained, stabilised, optimised, replatformed, refactored, rearchitected, rebuilt, replaced or retired.
Modernisation can then introduce stronger APIs, cloud-ready architecture, modern digital experiences, governed data foundations and new AI capabilities such as enterprise RAG, copilots, AI agents, semantic search and workflow automation.
AI-powered application modernisation is the process of evolving existing software so it can meet current business needs and support new intelligent capabilities without unnecessarily replacing the entire system.
It can include architecture assessment, AI-assisted code analysis, refactoring, API enablement, cloud migration, data modernisation, UX improvement, DevSecOps, enterprise integration and the controlled introduction of RAG, AI agents and workflow automation.
Years of incremental change can make development slower and more difficult to test.
Tightly coupled systems make small changes affect larger parts of the platform.
CRM, ERP, finance, customer and operational systems may depend on manual data movement or fragile interfaces.
Older customer and employee platforms may no longer meet modern usability expectations.
Important organisational knowledge may sit across applications, databases, spreadsheets and documents.
Existing systems may not have suitable APIs, retrieval layers, access models or data foundations for modern AI.
Keep the system when it remains fit for purpose.
Improve reliability, security, performance or operations.
Move infrastructure with minimal software modification.
Adopt newer platform capabilities without rebuilding the whole system.
Improve code structure and maintainability.
Change core architecture where the existing structure prevents future capabilities.
Recreate selected systems when the existing technology no longer provides a practical foundation.
Use an established commercial platform when custom software is no longer strategically useful.
Remove applications or functionality that no longer create value.
"AI assists engineering. Qualified engineers remain responsible for architecture, code review, testing, security validation and production decisions."
Connect existing applications with approved organisational knowledge.
Add contextual assistance inside current employee or customer workflows.
Allow controlled agents to work across defined APIs and enterprise systems.
Improve search beyond exact keyword matching.
Extract, classify, summarise and route business documents.
Add forecasting, recommendations or anomaly signals.
Reduce repetitive work across connected systems.
API enablement → modular monolith → selective microservices (*chosen around actual business scale and domain boundaries*) → event-driven architecture → API gateways → containers.
Cloud-readiness assessment, database upgrades, data-quality controls, data pipelines, search indexes, vector retrieval, and CI/CD observability.
Regional configurations, country-specific workflows, multi-language support, regional identity management, and multi-market access models.
Understand users, business processes and system value.
Review code, architecture, data, infrastructure, integrations and risks.
Determine what to retain, optimise, replatform, refactor, rearchitect, rebuild, replace or retire.
Define target architecture, migration strategy and governance.
Start with a meaningful business workflow or application boundary.
Improve reliability and system connectivity.
Add RAG, agents or automation where there is an appropriate business case.
Test functionality, performance, security, data and AI behaviour.
Use phased release and controlled cutover approaches.
Monitor application, AI and operational outcomes continuously.
Lead time, deployment frequency, defects
Incident volume, MTTR, availability
Performance, completion rate, usability
Failed syncs, reconciliation work
Manual steps, processing time
Quality, freshness, reporting latency
Retrieval relevance, agent completion, escalation
Infrastructure and AI operating cost
Assess valuable business logic and integrations before recommending replacement.
Improve architecture while introducing AI only where it adds value.
Treat CRM, ERP and business systems as part of the target architecture.
Address data, AI, security and accountability during delivery.
Maintain clear local stakeholder communication and delivery responsibility.
Design applications to evolve across regional operations where required.
AI-powered application modernisation evolves existing software through improved architecture, cloud, data, APIs, UX and selected AI capabilities such as RAG, agents, semantic search and workflow automation.
No. An assessment should determine whether components should be retained, optimised, rehosted, replatformed, refactored, rearchitected, rebuilt, replaced or retired.
Yes. Suitable applications can be enhanced with enterprise RAG, AI copilots, agents, document intelligence, semantic search, predictive models and workflow automation.
Yes. Existing integrations can be assessed and retained, improved, API-enabled or replaced depending on stability, security and future requirements.
AI-assisted engineering tools can support code understanding, dependency discovery, documentation, migration planning and selected refactoring tasks. Engineers remain accountable for architecture, testing and production changes.
No. Microservices can provide value in some systems but also create additional operational complexity. Architecture should follow the system's actual business and technical requirements.
Yes. Depending on requirements, applications can remain on-premise, use private cloud, move selected workloads to public cloud or adopt a hybrid model.
We review relevant personal-data flows, processing purposes, access, retention, security and third-party services and design technical controls intended to support the client's applicable responsibilities.
Yes, provided suitable APIs, data access, permissions, monitoring, workflow controls and human-review mechanisms are designed into the system.
Approaches may include phased releases, API facades, compatibility layers, feature flags, parallel operation, staged migration and controlled cutover.
Timeline depends on application size, architecture, code condition, data, integrations, security, AI scope and migration requirements.
Yes. Support can include application maintenance, cloud operations, integration monitoring, DevOps, AI evaluation, security updates, performance optimisation and continued modernisation.
Share your current application, technology constraints, business priorities and AI goals. Mobiloitte Singapore will help define a realistic phased modernisation roadmap.
