Build New Software With AI at the Core. Mobiloitte Singapore helps enterprises, growth-stage businesses, regional headquarters and public-sector-facing teams design and engineer new software where AI is part of the architecture from day one.
We build AI-native SaaS platforms, enterprise applications, agentic systems, RAG-based knowledge platforms and intelligent digital products that connect with your data, CRM, ERP and operational systems.
From business discovery and architecture through product engineering, enterprise integration, AI evaluation, deployment and ongoing operations, our Singapore-centred delivery model focuses on practical implementation, accountable governance and systems that can scale across APAC.
AI-native software engineering is the design and development of software where artificial intelligence is a foundational part of the product architecture rather than a feature added after the core system has been built.
An AI-native application can combine AI agents, retrieval-augmented generation, enterprise knowledge, predictive models, APIs, business systems, workflow orchestration and human oversight within one production environment. Mobiloitte Singapore applies this approach to new SaaS products, enterprise platforms, internal applications, customer experiences and intelligent workflow systems.
| Dimension | AI-Enabled Software | AI-Native Software |
|---|---|---|
| Timing | AI added as a feature | AI considered during architecture |
| Workflow | Standalone chatbot | AI embedded in business workflows |
| Data Context | Limited context | Enterprise knowledge and RAG |
| Tool Use | Model returns an answer | Agents can use approved tools |
| Integrations | AI isolated from systems | CRM, ERP and API integration |
| Governance | Governance introduced later | Controls defined during design |
| Monitoring | Basic monitoring | Evaluation and AI operations |
| Human Control | Human escalation added later | Human checkpoints designed upfront |
Important Supporting Note: Not every application needs to be AI-native. If AI performs only one supporting function, conventional software with a well-designed AI integration may be simpler and more cost-effective. The architecture should follow the business need rather than the technology trend.
When intelligent search, recommendations, automation, copilots or agents will be core product capabilities.
When AI needs to work directly with CRM, ERP, databases and operational workflows.
When employees spend significant time searching, classifying, summarising, reconciling or preparing information.
When employees, customers or partners need contextual access to approved organisational information.
When one software platform needs to support multiple markets, business units, languages or regional operating models.
Subscription products where AI copilots, recommendations, intelligent search, automation or agents are integral to user workflows.
Applications supporting operations, sales, service, finance, employees and other enterprise processes.
Controlled AI agents that can retrieve information, use approved tools, coordinate defined tasks and escalate when human judgement is required.
Knowledge systems connecting AI experiences to approved documents, databases and organisational information.
Digital products combining conventional application functionality with conversational, predictive or generative interfaces.
Applications combining business rules, data, automation and AI-assisted actions.
Products designed to support multi-market workflows, localisation, access models and regional integrations.
Target users, business problem, existing workflow, required outcomes, available data, existing systems, AI responsibilities, human responsibilities, risk boundaries, integration requirements, evaluation criteria, and first production scope.
Design user experience, application services, AI models, agent orchestration, enterprise RAG, data, APIs, CRM/ERP integration, identity, cloud, observability, security, and human approval as one unified environment.
Agents operate within defined permissions, tools, workflows and approval boundaries based on the risk profile of the use case. Multi-agent coordination, tool calling, memory, and approval gates.
Source-grounded AI with measurable retrieval quality. Ingestion, parsing, metadata, chunking, embeddings, vector search, hybrid search, reranking, and permission-aware retrieval.
Frontend, mobile, backend microservices, APIs, databases, authentication, admin tools, cloud infrastructure, integration, testing, and deployment pipelines.
Web, mobile, portals, conversational interfaces and employee tools.
Business logic, transactions, permissions and traditional application workflows.
Agents, routing, tools, task coordination and human approvals.
Operational databases, enterprise documents, vector retrieval, analytics and real-time context.
Commercial, open-source, specialist or privately deployed models selected according to the workload.
CRM, ERP, HR, finance, customer service, identity and internal APIs.
Cloud, containers, CI/CD, observability, MLOps and LLMOps.
Identity, access, evaluation, logging, policy controls, security and human accountability.
Engineering practices aligned with Singapore's Model AI Governance Framework, Generative AI framework, and AI Verify principles:
Define which activities AI may perform and which it may not.
Set meaningful approval points for material actions.
Evaluate outputs against representative Singapore/regional workflows.
Restrict tools, APIs, data and actions according to role.
Document relevant AI behaviour, limitations and escalation pathways.
Capture appropriate model, agent, retrieval and workflow events.
Evaluate external models, APIs, tools and processors.
Testing approaches aligned with Singapore AI-assurance guidance.
PDPA Positioning: Designed to support applicable PDPA, sector, contractual and organisational data-protection requirements.
Integrate AI-native software across enterprise platforms through governed APIs and middleware:
Define the business outcome, users and operational workflow.
Review data, systems, models, governance and feasibility.
Define architecture, integrations, risk boundaries and success criteria.
Test the highest-risk assumptions in a focused scope.
Engineer AI and conventional application components together.
Connect approved business systems and information.
Test AI quality, workflow behaviour, security and operational readiness.
Deploy in controlled phases.
Monitor usage, AI quality, cost and business outcomes.
Processing time, manual steps, exceptions
Adoption, task completion, feature usage
Agent completion, retrieval relevance, escalation
Deployment frequency, release lead time
Incidents, latency, availability
AI/infrastructure cost per completed workflow
Evaluation coverage, access exceptions
Local ownership and stakeholder communication for Singapore initiatives.
Build complete products rather than isolated AI demonstrations.
Address AI controls, evaluation, security and accountability from the beginning.
Connect new systems to existing business platforms.
Design software with regional expansion and multi-market operating requirements in mind.
Support monitoring, releases and continued evolution after launch.
AI-native software engineering means designing software with artificial intelligence as a foundational part of the architecture from the beginning. It can combine AI agents, RAG, models, enterprise data, APIs, workflows and human oversight within one production system.
Mobiloitte Singapore can develop AI-native SaaS platforms, enterprise applications, agentic systems, RAG knowledge platforms, web and mobile products and intelligent operational software.
Yes. Where appropriate, specialised agents can coordinate approved tasks, tools and systems with permissions, monitoring, failure handling and human approval.
No. RAG is useful when an application needs access to organisational documents or knowledge. Other applications may rely on predictive models, computer vision, speech, agents or other AI capabilities.
Yes. Applications can integrate with authorised CRM, ERP, databases, support platforms, identity systems and other enterprise technologies through APIs, middleware, events and appropriate integration patterns.
We define use-case boundaries, permissions, human-review points, evaluation criteria, logging and operational controls and can align engineering practices with relevant Singapore AI-governance guidance where appropriate.
We assess personal-data flows, processing purpose, access, retention, security and relevant third-party services and design controls intended to support the client's applicable obligations.
Yes. Model strategy can consider commercial APIs, open-source models and private deployments based on quality, cost, latency, security, data and infrastructure requirements.
Yes. Architecture can accommodate multi-market configuration, languages, regional integrations, access models and operating requirements where these are included in scope.
Evaluation can measure retrieval quality, model outputs, agent completion, tool behaviour, latency, failure handling and human escalation against representative test cases.
Timeline depends on product scope, data, systems, integrations, AI complexity, governance and deployment requirements. A focused proof of value can be delivered sooner than a complete enterprise platform.
Yes. Support can include application maintenance, cloud operations, AI evaluation, RAG optimisation, agent monitoring, releases, cost monitoring and continued product improvement.
Share your product goals, users and technical environment. Mobiloitte Singapore will help you define a practical architecture, integration plan and phased engineering roadmap.
