Intelligent Application Engineering

AI only performs when the applications running it do. We modernise and rebuild enterprise applications on any hyperscaler — Azure, AWS, or GCP — in any language your teams work in. Faster delivery, less risk, AI embedded by design.

Book an Application Portfolio Assessment
Why legacy systems keep winning

Your Applications are the Ceiling on
Your AI Ambitions.

Over 37% of enterprise applications still run on monolithic stacks that can't support real-time data or agentic workflows. IT spends 70–80% of its budget maintaining what exists. Release cycles stretch to quarters. And every AI initiative hits the same wall: the applications underneath it weren't built for intelligence.

Monolithic legacy systems

Monolithic legacy systems

Every new feature takes months. Every AI integration hits a wall.

Manual deployments and siloed teams

Manual deployments and siloed teams

The business stops asking IT for innovation and works around it instead.

Technical debt blocking AI adoption

Technical debt blocking AI adoption

AI stays a dashboard feature rather than becoming an operational capability.

No platform discipline or developer experience

No platform discipline or developer experience

Your best engineers spend their time managing infrastructure instead of building products.

Integration sprawl and API debt

Integration sprawl and API debt

System changes become risky, expensive, and slow — regardless of how modern the individual applications are.

Quality gaps that compound in production

Quality gaps that compound in production

Releases slow down just when competitive pressure demands they speed up.

What We Develop and Modernise

Six Connected Capabilities.
One Goal: Applications That Work Harder for Your Business.

Our application engineering practice spans the full lifecycle — from modernising what you have to building what's next, governing how it connects, testing that it works, and ensuring it stays reliable.

Every capability is delivered across your chosen stack and hyperscaler.

01

Legacy to Cloud-Native Modernisation

  • AI-assisted legacy code analysis and monolith decomposition
  • Microservices and event-driven architecture migration
  • SAP, Oracle, and legacy systems to Azure, AWS, or GCP
02

DevOps, Platform Engineering & SRE

  • CI/CD pipelines — Azure DevOps, GitHub Actions, GitLab CI
  • Infrastructure as Code — Terraform and Bicep
  • SRE frameworks — SLOs, error budgets, DORA metrics
03

AI-Enabled Application Engineering

  • Azure OpenAI, Bedrock, and Vertex AI integration
  • Agentic workflows embedded in business processes
  • MCP server integration for governed AI agent access
04

API Management & Integration

  • API design and lifecycle — Azure APIM, Kong, Apigee
  • Event-driven architecture — Kafka, Service Bus, EventBridge
  • MCP server design for governed AI agent connectivity
05

Quality Engineering

  • AI-assisted test generation and self-healing automation
  • GenAI output validation — accuracy, bias, hallucination detection
  • Shift-left security — SAST, DAST, OWASP Top 10
06

Application Security by Design

  • Secure-by-design architecture and secrets management
  • CI/CD security gates and IaC policy scanning
  • AI guardrails — prompt injection, agent RBAC, responsible AI governance
How we build it

Hyperscaler-Agnostic. Language-Agnostic.
Depth across all of it.

Our engineers have production experience across all three major clouds - and the languages, frameworks, and tools that enterprise application estates actually run on. We go where your stack is.

Category Tools & Platforms
Cloud / Hyperscaler AzureAWSGCP
Languages .NET / C#Java / Spring BootPythonNode.jsGo
Frontend & Full-Stack ReactAngularVue.jsMERNMEAN
Application Platforms AKSEKSGKEAWS LambdaAzure App ServiceCloud Run
DevOps & CI/CD Azure DevOpsGitHub ActionsGitLab CITerraformBicepArgoCD
AI / Intelligent Apps Azure OpenAIAWS BedrockGoogle Vertex AIAIONIQCopilot StudioPower Platform
Quality & Observability Playwrightk6DatadogVectorApplication Insights
Turning solutions into real enterprise results.

AI Investment Turned into Measurable Enterprise Outcomes.

Predictive Maintenance
Manufacturing
90% reduction in unplanned downtime

Predictive Maintenance Transformation in Mobility & Asset Manufacturing

Global mobility manufacturer · Azure + Databricks · IoT data application layer + ML failure prediction

Read case study
Real-Time Fraud & AML Intelligence
Financial Services
60% faster fraud detection

Real-Time Fraud & AML Intelligence Platform

Retail/SME financial institution · Databricks + Azure · Streaming fraud detection + AML automation

Read case study
Patient 360
Healthcare
Patient 360 clinical intelligence

Real-Time Patient 360 & Clinical Intelligence

Enterprise healthcare provider · Databricks Lakehouse · FHIR integration + readmission prediction

Read case study
Certifications

Security and compliance you can count on

HIPAA Compliant ISO 27001 Certified AICPA SOC
Get started

Assess Portfolio in Month 1.
Modernise from Month 2.

The most expensive modernisation mistake is building before you understand what you have. Every Parkar engagement starts with a structured Application Portfolio Assessment - a time-boxed discovery that delivers a prioritised roadmap, recommended architecture, and business case before a single line of code is written.

01

Application Portfolio Assessment

Map your application estate across business value, technical debt, AI readiness, and integration complexity. Get a rationalisation decision for each app - retire, retain, replatform, refactor, or re-engineer - and a prioritised roadmap tied to business outcomes.

What you get: A clear view of where your budget is going, which systems are blocking AI, and where to start — with a business case your CFO can read.

02

Modernisation Pilot

Take the highest-priority application from the assessment. Deliver a working cloud-native modernisation using AI-assisted code analysis, incremental decomposition, automated quality engineering, and a governed CI/CD pipeline.

What you get: A modernised application in production on your hyperscaler of choice, a validated methodology, and a reference architecture your teams can extend.

03

Platform, Quality & Scale

Build the engineering platform your teams operate on - CI/CD, IaC templates, API governance, quality automation, and SRE observability. Extend modernisation across your portfolio and embed AI into priority applications.

What you get: A delivery platform that keeps releasing value — with measurable DORA metrics, quality gates in every pipeline, and AI embedded where it matters most.

Book an Application Portfolio Assessment