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Applied AI 

Capability · Applied AI

AI is easy to experiment with. Applying it is an engineering problem.

Move AI from pilots into production with the validation, governance, security and cost control that enterprise scale actually requires.

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Applied AI 
AI-assisted engineering AI SDLC AI Agents Enterprise AI apps AI governance

Enterprise Reality

AI can accelerate engineering. But what happens after the code is generated?

Most enterprises are past the question of whether AI works. Licenses are bought, pilots are running, and almost none of it has reached a customer. The gap isn’t model capability. It’s everything between a working demo and a system the business can depend on.

AI adoption is not a model-selection exercise. It’s an engineering operating model.

What can you transform?

From isolated AI experiments to an engineered enterprise AI system.

Enterprise AI adoption touches much more than model selection. The engineering challenge spans the software lifecycle, application architecture, enterprise knowledge, security, governance, model access, cost and production operations.

01

AI-Assisted Software Engineering

Embed AI into the engineering workflow to accelerate requirements analysis, architecture, coding, testing and documentation.

Technical Scope

  • AI-Assisted Requirements
  • Architecture Assistance
  • Code Generation
  • Code Transformation
  • Test Generation
  • Documentation Generation
  • Developer Assistants
  • Engineering Workflow Integration
  • Human-in-the-Loop Review

How It Works

AI capabilities are integrated into existing engineering environments and repositories, allowing engineers to use enterprise context while keeping code review, testing, security and approval processes in the lifecycle.

A 500M+ user media platform writes and refactors code 60% faster with AI inside the engineering flow.
02

AI SDLC

Extend AI beyond coding into the complete software development lifecycle.

Technical Scope

  • Requirements Engineering
  • Architecture
  • Code Generation
  • Code Review
  • Test Generation
  • Security Validation
  • CI/CD
  • Release Engineering
  • Documentation
  • Engineering Intelligence

How It Works

AI capabilities are connected across lifecycle stages so engineering teams can automate or augment repetitive activities while existing quality, security and delivery controls remain enforced.

A 500M+ user media platform reviews code 80% faster, with AI working across repositories, pull requests, logs and infrastructure.
Read the story
03

AI Code Assistance

Give developers contextual AI assistance for understanding, generating, refactoring and testing code.

Technical Scope

  • Code Completion
  • Code Generation
  • Refactoring
  • Code Explanation
  • Test Generation
  • Documentation
  • Repository Context
  • IDE Integration
  • Secure Model Access
  • Codebase Analysis

How It Works

AI operates with appropriate repository and engineering context while access, security and review controls remain aligned with enterprise development practices.

A 10M+ user OTT platform cut developer debugging time 60%.
Read the story
04

AI Agents

Move beyond AI suggestions toward agents that can execute defined engineering tasks.

Technical Scope

  • Agent Workflows
  • Tool Calling
  • Repository Interaction
  • CI/CD Integration
  • Log Analysis
  • Issue Diagnosis
  • Automated Remediation
  • Workflow Execution
  • Approval Gates
  • Execution Policies

How It Works

Agents are given bounded access to engineering tools and workflows. They can interpret context, perform defined actions and return results while permissions and approval boundaries control what they are allowed to execute.

Typical Transformation

  1. Manual investigation
  2. AI-assisted investigation
  3. Controlled agentic execution
50%+Fewer non-actionable alerts
~25%Less investigation time
~20%Lower pipeline maintenance
05

AI-Led Modernization

Use AI to accelerate the analysis and transformation of complex legacy applications.

Technical Scope

  • Legacy Code Analysis
  • Code Understanding
  • Dependency Analysis
  • Code Transformation
  • Automated Documentation
  • Test Generation
  • Continuous Validation
  • Security Validation
  • Human-in-the-Loop Review

How It Works

AI assists engineers in understanding legacy code and generating modernization changes. The resulting changes remain subject to testing, security validation and human review.

A global consulting firm modernized 85+ legacy Java classes with AI inside its own secure environment, holding 80%+ test coverage at zero security violations.
Read the story
85+Java classes modernized
80%+Test coverage maintained
ZeroSecurity violations
06

AI-Powered Testing

Increase the ability of engineering teams to create, analyze and maintain tests as AI accelerates software delivery.

Technical Scope

  • Test Generation
  • Test Case Analysis
  • Regression Testing
  • Test Failure Analysis
  • Coverage Analysis
  • Test Maintenance
  • AI-Assisted Validation
  • Quality Engineering

How It Works

AI assists with identifying test scenarios, generating test cases and analyzing failures while automated quality gates and existing engineering validation remain part of the delivery process.

A 500M+ user media platform generates test scenarios 50% faster, including edge cases and release-readiness checks.
07

Enterprise AI Applications

Engineer AI directly into enterprise applications, products and business workflows.

Technical Scope

  • LLM Applications
  • AI Assistants
  • AI Agents
  • RAG Applications
  • Enterprise Knowledge Systems
  • Workflow Automation
  • Model Integration
  • AI APIs
  • Application Security

How It Works

AI capabilities are integrated with enterprise applications, APIs, business workflows and data sources, with security and access controls applied around the application architecture.

A hospital network cut clinical documentation time 85%, returning three to four hours a day to each doctor.
Read the story
08

RAG & Knowledge Systems

Build the retrieval and knowledge layer required for AI applications to work with enterprise information.

Technical Scope

  • Document Ingestion
  • Content Processing
  • Chunking
  • Embeddings
  • Vector Search
  • Metadata
  • Knowledge Sources
  • Retrieval Pipelines
  • Access Controls
  • Grounded Generation
  • Evaluation

How It Works

Enterprise information is ingested, processed and indexed so relevant context can be retrieved at inference time. Access controls and source-level governance determine what information an AI application can use.

A consumer super-app made senior engineers’ diagnostic knowledge retrievable, so incident response stopped depending on who was on call.
09

AI Governance & Security

Build controls around AI usage so enterprises can manage model access, data exposure, security, compliance and AI activity.

Technical Scope

  • Model Access Policies
  • Identity & Access
  • Data Protection
  • Prompt Controls
  • AI Traffic Governance
  • Auditability
  • Usage Policies
  • Security Controls
  • Compliance

How It Works

AI access and activity are routed through defined controls that establish who can use which models, what information can be processed and how AI interactions are monitored.

10

AI Economics & Token Optimization

Make the economics of AI visible as model usage expands across applications, engineering teams and business workflows.

Technical Scope

  • Token Usage
  • Model Cost
  • Prompt Optimization
  • Model Routing
  • Usage Analytics
  • Cost Allocation
  • Budget Controls
  • AI FinOps
  • Workload Optimization

How It Works

AI traffic and consumption are measured at the request and workload level. Prompts can be optimized and workloads routed to appropriate models based on cost, performance and policy requirements.

30%Lower model spend
25%Fewer tokens per request
100%AI traffic governed
11

AI Gateway & Model Routing

Create a governed abstraction layer between enterprise applications and the growing ecosystem of AI models.

Technical Scope

  • Model Routing
  • API Gateway
  • Virtual Keys
  • Provider Management
  • Policy Enforcement
  • Cost Controls
  • Usage Tracking
  • Automatic Failover
  • Security Controls

How It Works

Applications interact through a consistent AI interface while the gateway handles provider selection, access policies, usage visibility, routing and failover.

A hospital network cut inference cost 45% through dynamic model routing.
12

Engineering Productivity

Apply AI to repetitive engineering work while measuring whether it actually improves engineering throughput.

Technical Scope

  • Developer Assistants
  • Code Assistance
  • AI Review
  • Test Generation
  • Documentation
  • Engineering Analytics
  • Workflow Automation
  • Productivity Signals
  • Engineering Intelligence

How It Works

AI is introduced into existing engineering workflows rather than deployed as an isolated productivity tool. Engineering signals can then be used to understand adoption, workflow friction and delivery impact.

A 500M+ user media platform cut infrastructure troubleshooting effort 40% and documentation effort 30%.
13

Intelligent Operations & AIOps

Use AI across operational telemetry and incident workflows to help engineering teams detect, diagnose and respond.

Technical Scope

  • AI Observability
  • Anomaly Detection
  • Log Analysis
  • Root Cause Analysis
  • Incident Intelligence
  • Automated Remediation
  • SLO Monitoring
  • Operational Automation

How It Works

AI analyzes telemetry from existing observability systems to identify patterns, correlate signals and assist with diagnosis and remediation while remaining connected to SRE and operational controls.

A consumer super-app cut MTTR 40% with AI orchestrating incident response.

Google partnership

A license is not adoption. Adoption is not impact.

Google brought us in to drive real adoption of their AI coding tools inside their largest enterprise accounts across APAC. Every engagement runs on a live ROI dashboard: license utilization, developer time saved, feature velocity and code quality in one window.

From experimentationto enterprise operation.

“AI adoption is not a model-selection exercise. It is an engineering operating model.”

01 / 06

  1. Assess

    Identify AI opportunities across applications, engineering workflows, data and operations.

  2. Enable

    Introduce the appropriate AI capabilities into existing engineering environments.

  3. Govern

    Establish security, access, policy, model and usage controls.

  4. Integrate

    Connect AI with applications, data, developer workflows and enterprise platforms.

  5. Operate

    Monitor AI workloads, model usage, reliability, security and business impact.

  6. Optimize

    Continuously improve model selection, prompts, workflows, engineering productivity and AI economics.

AI Across the Engineering Lifecycle

Seven AI-enabled capabilities across development, delivery, operations and business outcomes.

These capabilities can be deployed independently or combined across the engineering lifecycle.

01 / 02
Enterprise AI Adoption

Engineered AI-DLC Across the Engineering Lifecycle for a 500M+ Media Platform

A media and entertainment platform with 500M+ active users embedded AI across build, review, investigate, operate and validate, rather than as a tool sitting beside the work.

Engineered

  • Claude across repositories
  • pull requests
  • Infrastructure configs
  • CI/CD and operational workflows
  • 80%faster code reviews
  • 60%faster debugging and RCA
  • 50%faster test generation
  • .request understanding from days to hours
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Engineered AI-DLC to Scale a 1 Crore+ Micro-Drama OTT Platform

A 10M+ user vertical video platform in seven Indian languages moved AI from occasional code assistance into everyday production engineering.

Engineered

  • Claude API and Claude.ai inside the engineering flow
  • extending into an AI filmmaking initiative
  • 60%faster debugging
  • 50%faster infrastructure setup
  • 40%faster access provisioning
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Connected Ecosystem

AI Adoption doesn't operate alone.

AI sits inside the enterprise engineering ecosystem — Applications, Data, Platform, Cloud, DevSecOps and SRE.

  • Data Engineering & Analytics

    Engineer the data backbone that powers AI, analytics and business decisions, from fragmented sources to governed, AI-ready intelligence.

    Explore more

  • Cloud Security 

    Find what’s exposed, fix it continuously and stay audit-ready, with security engineered into the pipeline rather than reviewed after it.

    Explore more

  • Platform Engineering

    Turn infrastructure, environments, delivery and standards into self-service capabilities, so teams move without waiting and the enterprise keeps control.

    Explore more

  • SRE & Observability

    Connect telemetry, reliability objectives and incident response into one system, with AI doing the correlation work your engineers currently do by hand.

    Explore more

  • Technology Modernization

    Transform legacy applications, databases, middleware and infrastructure into scalable, AI-ready foundations, with AI accelerating the work and engineering keeping control.

    Explore more

  • Dev & Quality Engineering

    AI is changing how software gets designed, built and tested. We combine product engineering, AI-augmented development and quality engineering so velocity doesn’t cost you confidence.

    Explore more

  • Devsecops

    Standardise delivery, automate the workflows around it and put security and policy inside the pipeline, across cloud, hybrid and on-prem.

    Explore more

  • Cloud Transformation

    Migrate, modernise and operate cloud environments that hold under load, pass audit and stay affordable, with the foundation AI workloads actually need.

    Explore more

FAQ

Questions we hear most.

Direct answers from engineers operationalizing AI.

01 / 07

What is Enterprise AI Adoption?

Enterprise AI Adoption is the process of integrating AI into engineering, applications, data and operations with the governance, security, infrastructure and operating practices required to use it at scale.

02 / 07

How is Enterprise AI Adoption different from AI experimentation?

Experimentation validates whether an AI capability can work. Enterprise adoption requires integration into production workflows, security and governance controls, operational support, cost management and measurable outcomes.

03 / 07

Where can AI be applied in software engineering?

AI can assist with requirements, architecture, coding, testing, code review, modernization, delivery, operations and engineering productivity.

04 / 07

How do enterprises govern AI usage?

Governance can include controlled model access, identity and permissions, policy enforcement, data protection, auditability, usage monitoring and AI traffic controls.

05 / 07

What is an AI Gateway?

An AI Gateway provides a controlled access layer between applications and AI models. It can manage model routing, access policies, usage, cost controls, security and provider failover.

06 / 07

Can AI agents operate inside enterprise engineering workflows?

Yes. Agents can be integrated with defined tools and workflows to perform tasks such as issue analysis, remediation or engineering automation, with appropriate permissions and human approval boundaries.

07 / 07

How do you control AI costs?

AI economics can be managed through usage visibility, token optimization, model routing, workload analysis, cost allocation and budget controls.

Move AI from experimentation to engineering.

Talk to Our AI Engineers

Ready to engineer what's next?

Move AI from experimentation to engineering.

Build AI capabilities that are useful, governed, measurable and ready for enterprise scale.

Talk to Our AI Engineers Explore Engineering Capabilities
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