Build. Modernise. Reinvent with AI.
We engineer new digital products, transform complex legacy applications and introduce intelligence directly into enterprise workflows. From re-architecting monoliths and modernising user experiences to deploying copilots and AI agents, we make applications cloud-native, intelligent, and ready for continuous evolution.
AI is changing both what enterprise applications can do and how they are built. Our AI-Fused Application Services bring together modern application engineering, application modernisation and AI to help enterprises build faster, transform intelligently and embed AI where it can create meaningful business impact.
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Enterprise applications have traditionally been designed to execute transactions, manage workflows and provide access to information. AI creates the opportunity to make these applications more contextual, adaptive and intelligent.
We help enterprises introduce AI into applications and workflows while maintaining the foundations that enterprise technology demands—scalable architecture, security, integration, governance, reliability and performance.
The result is not simply AI added to an application. It is intelligence engineered into the application experience, the workflows behind it and the way software itself is built and operated.
Traditionally designed to
With AI, more
On the foundations enterprise technology demands
Cloud-native products, enterprise platforms, full-stack development, APIs and event-driven architectures.
We engineer modern applications and digital platforms designed for scale, integration and continuous change. Modular architectures, APIs and cloud-native engineering create the foundation for new digital capabilities and future AI integration.
Legacy assessment, code refactoring, monolith-to-microservices transformation, cloud re-platforming and UX renewal.
We modernise complex application estates by identifying what should be refactored, re-platformed, re-architected or selectively rebuilt. AI-assisted analysis, documentation, code transformation and testing can accelerate parts of the journey while modern architectures prepare applications for cloud, data and AI integration.
Explore in depthRole-specific copilots, conversational interfaces, intelligent search and contextual decision support.
We embed AI into the applications and workflows employees and customers already use—helping them find information, understand context, navigate complexity and make better-informed decisions without constantly moving between systems.
Task-oriented and multi-agent solutions that reason, act and orchestrate multi-step business processes across enterprise systems.
We engineer AI agents that operate within defined enterprise boundaries to perform tasks, retrieve information, invoke tools and coordinate workflows across applications—with appropriate permissions, controls and human oversight.
AI-assisted discovery, code transformation, documentation, testing, and quality engineering—accelerating modernisation and software delivery.
We apply AI across selected stages of the software development lifecycle to augment engineering teams, accelerate repetitive development activities and help teams focus more of their effort on architecture, business logic and complex problem-solving.
DevSecOps, observability, SRE, performance engineering and continuous optimisation to keep applications secure, resilient and evolving.
We establish the engineering practices, automation and observability required to operate applications reliably, improve performance and continuously evolve both traditional and AI-enabled application capabilities.
AI-assisted discovery, code analysis, documentation, transformation and testing can accelerate selected stages of application modernisation while helping engineering teams understand complex legacy environments.
ImpactShorter modernisation cycles, reduced technical debt and faster time to value.
Connect AI with enterprise applications, data, APIs and workflows so users can retrieve information, understand context, receive recommendations and take action within the flow of work.
ImpactImproved productivity, faster access to information and better decision support.
Move beyond repetitive task automation with AI agents capable of coordinating multi-step processes across systems, data and people within defined enterprise controls.
ImpactFewer manual hand-offs, faster process execution and greater operational efficiency.
Introduce conversational interfaces, intelligent search, contextual recommendations and role-specific copilots into complex enterprise applications.
ImpactReduced user friction, simpler access to complex information and more intuitive employee and customer experiences.
Use AI-assisted discovery, coding, documentation, testing and quality engineering to augment development teams across the software lifecycle.
ImpactImproved engineering productivity, faster development cycles and accelerated delivery of business capabilities.
Combine cloud-native architecture, APIs, event-driven design, DevSecOps and observability to create applications capable of evolving as business requirements, data and AI capabilities change.
ImpactGreater agility, resilience and adaptability.
AI does not have to begin and end with a chatbot or copilot. It can create value across the application lifecycle.
Understand business requirements, application estates, workflows, dependencies and modernisation opportunities.
Reimagine user journeys, workflows and application architecture around intelligent experiences.
Build modern applications using cloud-native engineering and AI-assisted development.
Refactor, re-platform and transform legacy applications while preparing them for cloud, data and AI integration.
Introduce copilots, intelligent search, decision support, workflow automation and AI agents where they create meaningful value.
Apply DevSecOps, observability, SRE, performance engineering and continuous optimisation to keep applications secure, resilient and evolving.
Adding AI capabilities to an application is only one part of the challenge.
Enterprise AI must work within
It must also be
That is why we approach AI-Fused Application Services as an engineering discipline—not simply an AI implementation exercise.
By bringing together application engineering, cloud, data and AI capabilities, we help enterprises build intelligent applications that can operate reliably within the realities of the enterprise.
Turn business needs into clear user journeys, prototypes and an actionable backlog. We define business rules, integration needs and acceptance criteria so stakeholders can make informed scope and delivery decisions.
Build customer, employee and partner applications, from self-service portals to operational tools. We connect the interface, application logic and data services, with attention to accessibility, performance and maintainability.
Assess dependencies and technical debt, then refactor, upgrade, replatform or selectively rebuild. We choose modular applications, services or serverless designs to suit the workload, with regression testing and a planned transition.
Connect applications with ERP, CRM, identity, payment and other business systems. We design interfaces, data mappings and workflow states, including retries, reconciliation and exception handling when a transaction cannot complete.
Embed knowledge search, document extraction, summaries and role-specific assistance into business applications. Where tasks require actions across systems, we use workflow automation or task-specific AI agents with authorised access, approval steps and a clear route for exceptions.
Build automated checks, release pipelines and application monitoring into delivery. Test critical journeys, integrations, security and performance, then define support and continued improvement around the application’s business needs.
Help users retrieve approved information, prepare a transaction, review a document or decide the next step. The experience includes source context where relevant, user permissions, review points and a fallback when AI cannot complete the task reliably.
Use approved AI tools to help understand code, prepare documentation, generate test cases and support implementation. Engineers review outputs and remain responsible for correctness, security and release quality. We assess delivery improvements after review, testing and rework.
Apply reusable capabilities where they fit your application. Codincity’s accelerator portfolio spans AI, integration, cloud automation and observability. We assess the required integration, deployment and support before selecting an asset for an engagement.
Support document understanding and enterprise knowledge search within business workflows.
Relevant to reviewer workbenches, servicing assistants and employee self-service.
Support cloud-native integration between applications and business systems.
Relevant to connected portals and workflows that depend on information from multiple platforms.
Support infrastructure automation and Kubernetes cost and compliance needs.
Relevant to repeatable application environments and container-based delivery.
Support unified observability and AIOps.
Relevant to application monitoring and operational investigation across connected services.
Our delivery approach applies the following practices in proportion to the scope and risk of the application.
Discovery to controlled release
Evaluation to operational ownership
Accelerators complement engineering and customer-specific integration. The delivery frameworks describe working practices, not separately licensed software products.
Technology choices follow your application requirements, existing estate and operating model. The following representative options are selected per engagement rather than applied as a mandatory stack.
Build web and mobile experiences, application services and APIs. Select the framework and runtime for maintainability, integration needs and team ownership.
Assist code understanding, implementation, documentation and test preparation. Use approved configurations and protect customer code and data; retain human review and release accountability.
Automate builds, environments and deployment; test critical browser journeys. Add security checks and release approvals appropriate to the customer’s toolchain.
Correlate telemetry across application services and monitor performance. Choose data stores to suit transaction, reporting and operational requirements.
Compose managed services for application hosting, integration, persistence and AI access. Select services against workload, security and cost requirements.
Compose managed services for business applications and AI-enabled workflows. Align identity, networking, monitoring and deployment with the customer’s Azure environment.
Cloud consumption, third-party licences, service availability and operating responsibilities are agreed during solution design. Additional platforms can be assessed where the customer’s estate requires them.
Not every application needs to be rebuilt.
Not every workflow needs an agent.
And not every business problem needs AI.
The opportunity is identifying where modern engineering creates a stronger foundation, where AI can materially improve an application or workflow, and where the combination can deliver a better business outcome.
Whether you are building a new digital product, modernising a critical legacy platform, introducing copilots into existing workflows or exploring agentic automation, Codincity helps you move from opportunity to production-ready engineering.
Build. Modernise. Reinvent with AI.
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