What is the difference between AI-native and AI-enabled software?

AI-enabled software usually adds AI capabilities to an existing application. AI-native software is designed from the start around intelligent workflows, data, models and AI-assisted user experiences.

What AI-native software does Mobiloitte build?

Mobiloitte can develop AI-native SaaS platforms, enterprise applications, knowledge systems, agentic workflow platforms, web and mobile products, copilots and intelligent operational applications.

Can Mobiloitte build multi-agent AI systems?

Yes. Multi-agent systems can coordinate specialized agents across approved workflows, tools and enterprise systems. Architecture should include permissions, monitoring, error handling and human escalation.

Does AI-native software require RAG?

No. RAG is appropriate when an application needs access to external or enterprise knowledge. Other AI-native products may depend primarily on predictive models, agents, computer vision, speech or other capabilities.

Can AI-native applications integrate with existing ERP and CRM systems?

Yes. Applications can connect to approved ERP, CRM, HRMS, databases, helpdesk, communications and other enterprise systems through APIs, events, middleware and governed connectors.

Can we use OpenAI, Claude, Gemini or open-source models?

Yes. Model selection can be based on quality, cost, latency, security, context requirements and deployment constraints rather than locking the complete product to one provider.

Can AI-native applications run in a private cloud or on-premise?

Depending on the selected models and infrastructure, architectures can support public cloud, private cloud, VPC, on-premise or hybrid deployment.

How do you manage AI quality and reliability?

Evaluation can include representative test cases, retrieval quality, output quality, tool behaviour, latency, failure handling and production monitoring. High-impact workflows can also use human approval and fallback mechanisms.

How much does AI-native software development cost?

Cost depends on product complexity, model requirements, data readiness, agents, RAG, integrations, interfaces, deployment, security, evaluation and post-launch operations. Discovery is used to establish a reliable estimate.

How long does an AI-native software project take?

A focused prototype can be completed sooner than a production platform. Final timelines depend on scope, data, integrations, evaluation, security and operational requirements.

Does Mobiloitte support AI-native products after launch?

Yes. Support can include application maintenance, infrastructure monitoring, model evaluation, RAG tuning, agent monitoring, security updates and continued product development.

Agentic SDLC

AI-Native Software
Engineering & Agentic
SDLC Services

Q: What is AI-native software engineering?

AI-native software engineering is the practice of designing software with AI embedded into the product architecture from the beginning. It can combine AI models, agents, RAG, business data, workflow orchestration, enterprise integrations and human oversight within one production system.

Reengineer how software is planned, built, tested, modernized and operated with AI working alongside experienced engineering teams—helping enterprises build AI-native products, introduce governed AI across the SDLC, and deliver better software faster without compromising architecture, quality, security or human accountability.

Assess Your AI-Native Engineering Readiness

WHAT IS AI-NATIVE SOFTWARE ENGINEERING?

Engineering Software
for an AI-Driven World

AI-native software engineering is a software delivery approach in which AI participates across both the product architecture and the engineering lifecycle.

At the product level, AI may power agents, enterprise search, recommendations, intelligent workflows and decision support.

At the engineering level, AI can assist teams with requirements, architecture, coding, testing, documentation, modernization, security analysis, deployment and operations.

The important difference is control.

AI-native engineering combines AI automation with specifications, engineering standards, testing, security controls, observability and human verification so generated work can move safely toward production.

AI as a Force Multiplier

AI accelerates research, design, code, test and operations.

Governed & Responsible

Policies, guardrails, approvals and auditability at every step.

Context is Everything

Quality outcomes come from rich engineering context and data.

Human Accountability

Engineers remain responsible for decisions and outcomes.

Better Software, Faster

Increase velocity without compromising quality.

Built for the Future

Create systems that are ready for continuous AI innovation.

AI-Native Engineering Has Two Sides

Build AI-Native Products

Create applications where intelligence is part of the user experience or operational workflow.

AI agents
Enterprise copilots
RAG applications
Intelligent SaaS products
Recommendation systems
Predictive applications
Conversational interfaces
Workflow automation

Build Software the AI-Native Way

Use governed AI-assisted and agentic workflows throughout software delivery.

Product discovery
Requirements analysis
Architecture support
Code generation
Refactoring
Automated testing
Documentation
Security analysis
Legacy modernization
DevOps automation
Incident investigation

Mobiloitte supports both. That means we can help you build an AI-native product while also modernizing the engineering system used to deliver and operate it.

Move Beyond AI Coding Assistants

Giving developers an AI coding tool does not automatically create an AI-native engineering organization.

The largest gains come when AI can work with reliable engineering context, defined workflows and measurable quality gates.

AI-Assisted Development

AI supports individual tasks such as:

Code completion
Documentation
Test generation
Debugging
Code explanation

Useful—but usually local to one engineer or task.

Agentic Software Engineering

AI agents participate across connected engineering workflows.

Requirement → Technical Plan → Implementation → Test →
Review → Security Check → Release

Agents operate within defined repositories, policies, tools and approval boundaries.

AI-Native Engineering

Engineering practices, architecture, platforms, governance and team responsibilities are redesigned around productive human + AI collaboration.

This is where AI becomes part of the software-delivery system instead of another developer tool.

The Architecture Behind Production AI-Native Software

Experience Layer

Web, mobile, SaaS, conversational, voice and employee interfaces.

Application Layer

APIs, business rules, authentication, permissions and transactional workflows.

Agent & Orchestration Layer

Reasoning, routing, tool selection, multi-agent coordination and workflow execution.

Knowledge & Data Layer

Enterprise documents, operational databases, vector stores, analytics and real-time context.

Model Layer

Commercial, open-source, specialized or custom models selected according to task requirements.

Integration Layer

CRM, ERP, HRMS, payments, communications, databases and external APIs.

Platform & Operations Layer

Cloud infrastructure, CI/CD, containers, monitoring, model gateways, MLOps and LLMOps.

Governance & Security Layer

Identity, permissions, logging, evaluation, policy controls, human oversight and incident handling.

Choose Models Around the Product—not the Other Way Around

Mobiloitte designs applications so the business logic is not unnecessarily tied to one model provider, ensuring maximum flexibility, cost-efficiency, and future-proofing.

Product & Business Logic Layer

Core application, user experience, and enterprise workflows

AI Gateway & Model Routing

Standardized API interfaces, fallback mechanisms, and context management

Output quality Cost & Latency Data sensitivity

Commercial Models

GPT, Claude, Gemini

Open-Source & Local

Llama, Mistral, Private Deployments

Specialized Models

Embeddings, Vision, Custom ML

Connect AI With the Systems Where Work Actually Happens

AI-native applications become valuable when they can securely interact with business systems rather than operating as isolated chat interfaces.

CRMERPHRMSCustomer-support platformsDocument-management systemsDatabasesData warehousesPayment platformsCommunication systemsIdentity providersWorkflow enginesExternal APIs

Define What AI Can Do—and What Requires Human Judgment

AI-native does not mean removing humans from every workflow. We design explicit boundaries:

AI Can

Retrieve information, Summarize, Classify, Recommend, Draft, Detect patterns, Prepare actions, Execute approved low-risk tasks.

Human Review May Be Required For

Financial decisions, Healthcare decisions, Legal interpretation, Regulatory decisions, High-value transactions, Irreversible actions, Exceptional cases, Policy overrides.

System Controls

Confidence thresholds, Approval gates, Escalation, Role-based permissions, Audit logs, Kill switches, Fallback workflows.

AI Governance, Security & LLMOps

Operate AI Like Production Software. AI-native systems require continuous operational management and governance after launch.

AI Governance & Security

Identity & Access

Define which users, agents and systems can access specific data and actions.

Data Protection

Apply appropriate encryption, permissions, retention and data-handling controls.

Model Evaluation

Evaluate quality against representative workflows and business-specific acceptance criteria.

Prompt & Configuration Versioning

Track changes to important prompts, policies, model configurations and tools.

Agent Permissions

Restrict agents to approved tools, systems and operations.

Human Oversight

Route defined high-impact actions to authorized reviewers.

Logging & Traceability

Maintain relevant model, retrieval, tool and workflow logs.

AI Monitoring

Track quality, errors, latency, usage, cost and unusual behaviour.

Security Testing

Assess application, API, model, agent, retrieval and infrastructure risks according to scope.

Designed to support applicable organizational, contractual, privacy and regulatory requirements.

MLOps & LLMOps

Model versioning

Prompt versioning

Evaluation pipelines

CI/CD

Automated testing

Model and agent monitoring

RAG quality monitoring

Latency monitoring

Usage tracking

Token and inference cost tracking

Incident alerts

Rollback

Model replacement

A/B evaluation

Our AI-Native Engineering Process

From Product Idea to Production AI System

Business & Product Discovery

Define the business problem, target users, product goals and success criteria.

AI & Data Feasibility

Assess data, models, integrations, expected quality, risk and operational cost.

Architecture & Governance

Design application, AI, data, cloud, security and human-oversight architecture.

Prototype & Evaluation

Test the highest-risk assumptions before committing to full production development.

Product Engineering

Build frontend, backend, agents, RAG pipelines, APIs and platform capabilities.

Enterprise Integration

Connect AI workflows with approved business and data systems.

Production Readiness

Validate security, reliability, model quality, observability, fallback and operational support.

Launch, Monitor & Improve

Measure adoption, AI quality, business KPIs, infrastructure usage and product performance.

Technology Stack

AI Models

GPT-family models
Claude
Gemini
Llama
Mistral
Approved custom/private models

Web & Mobile

React
Next.js
Angular
Vue.js
Swift
Kotlin
Flutter
React Native

Backend & APIs

Python
FastAPI
Node.js
Django
Go
REST
GraphQL

Data & Retrieval

PostgreSQL
MongoDB
Redis
Vector databases
FAISS
Milvus
Pinecone

Cloud Infrastructure

AWS
Microsoft Azure
Google Cloud

AI Operations

Evaluation
Observability
Model gateways
Prompt management
LLMOps
MLOps

AI-Native Engineering for Complex Industry Workflows

BFSI & Fintech

Fraud intelligence, lending support, document workflows, service copilots and governed financial AI.

Healthcare

Knowledge systems, operational automation, patient workflows and clinical-support applications with appropriate human oversight.

Retail & E-Commerce

Product discovery, personalization, commerce agents, inventory intelligence and customer-service AI.

Government & Smart Cities

Citizen-service platforms, knowledge assistants, urban intelligence and controlled government workflows.

Manufacturing & Supply Chain

Operational agents, predictive systems, document intelligence and connected industrial workflows.

Real Estate & Construction

Property intelligence, project-document systems, AI assistants and construction workflow automation.

Choose the Right Starting Point

AI-Native Discovery & Architecture

For organizations still defining the product, business case and architecture.

AI-Native MVP

For organizations ready to validate a focused product with real users.

Dedicated AI Product Team

For ongoing product development requiring product, AI, frontend, backend, QA and DevOps capability.

Enterprise AI Platform Program

For large applications requiring multiple integrations, security controls, environments and operating teams.

Managed AI Operations

For post-launch monitoring, model evaluation, LLMOps, platform maintenance and continuous product improvement.

What Determines AI-Native Software Development Cost?

Cost depends on Product scope, Number of user roles, Agent complexity, Model selection, RAG requirements, Data readiness, Number of enterprise integrations, Web/mobile requirements, Cloud architecture, Security requirements, Evaluation depth, Deployment model, Operational support.

A focused AI prototype requires less effort than a production AI-native platform with multiple agents, RAG, enterprise integrations, role-based permissions, monitoring and private deployment.

Following discovery, Mobiloitte can provide a defined architecture, product scope, evaluation plan, phased timeline and commercial estimate.

Selected AI-Native Engineering Work

Why Choose Mobiloitte for AI-Native Software Engineering?

AI + Full-Stack Software Engineering

Build the complete software product rather than an isolated AI demonstration.

Architecture Before Model Selection

Define workflows, data, responsibilities and integration before choosing the final model stack.

Agentic AI & RAG Capability

Combine agents, retrieval and enterprise systems inside controlled workflows.

Cloud & Platform Engineering

Design deployment, CI/CD, observability and operational infrastructure alongside application development.

Security Integrated Into Delivery

Address application, API, data and AI-specific risks throughout the engineering lifecycle.

Model & Platform Flexibility

Select models and infrastructure according to quality, cost, privacy and deployment requirements.

Production Operations

Support evaluation, monitoring, cost visibility and continued improvement after launch.

AI-Native SoftwareEngineering & AgenticSDLC Services

Engineering Softwarefor an AI-Driven World