Application, AI & Data Modernisation Strategy, Architecture on AWS

A clear, actionable pathway to modernise applications, data platforms, and AI capabilities on AWS, reducing bottlenecks, improving agility, and accelerating digital transformation with measurable business outcomes.

We assess the current environment, define a unified AWS-native target architecture, identify high-value AI and automation opportunities, and build a prioritised modernisation roadmap. This includes architecture blueprints, DevOps and Kubernetes standards, data platform modernisation, and implementation-ready guidance.

Many organisations operate legacy, hybrid, or fragmented environments that slow innovation and increase delivery risk. This engagement provides clarity before major transformation work begins, reduces uncertainty around cloud adoption, and aligns modernisation efforts with business goals.

This offering is suited to SMEs, mid-market organisations, and enterprises with legacy or partially modernised environments, technology leaders seeking a confident decision path, and teams looking for measurable ROI from AWS and AI adoption.

Key Features

• End-to-End Application & Platform Modernisation Modernise legacy or fragmented applications into modular, container-based, API-driven systems using AWS-native services and Kubernetes to improve reliability and reduce operational complexity.
• Data Platform Modernisation Create a scalable data foundation using Amazon S3 and modern data pipelines to support analytics, automation, and AI-driven use cases.
• AWS-Aligned Target Architecture Define a future-state architecture using AWS services to improve scalability, performance, security, and observability.
• Cloud-Native Delivery & DevOps Standards Adopt CI/CD pipelines, GitOps workflows, and Infrastructure-as-Code practices for secure, repeatable, and efficient delivery.
• AI & Automation Enablement Identify and prioritise AI opportunities with measurable outcomes using services such as Amazon Bedrock, Amazon SageMaker, and vector-based search patterns.
• Security, Compliance & Governance Alignment Establish security baselines, IAM and RBAC policies, audit controls, and governance practices aligned with operational and regulatory needs.
• Cost-Optimised Infrastructure Design Design efficient infrastructure using autoscaling, serverless components, and right-sized workloads to improve performance while controlling cost.
• Roadmap for Phased Execution Deliver a structured roadmap based on impact, complexity, value, and readiness to ensure business-aligned execution.

Deliverables

• Current-State Assessment Evaluation of your application landscape, data flows, workflows, and technology stack, highlighting risks, gaps, and improvement areas.
• Target Architecture Blueprint A future-state design covering AWS services, data flows, application components, integrations, deployment standards, and security layers.
• Prioritised Modernisation Roadmap A phased plan across foundation, application, data, and AI workstreams, including priorities, dependencies, quick wins, and estimated effort.
• Business Value & TCO Analysis An assessment of cost efficiency, operational improvement, scalability gains, and Total Cost of Ownership using AWS cost modelling.
• DevOps, CI/CD & IaC Standards Guidance for GitOps, Kubernetes deployment patterns, automated testing, and Infrastructure-as-Code foundations.
• Data Platform Modernisation Plan A practical plan for batch and streaming pipelines, storage layers, governance, lineage, and analytics enablement.
• AI Adoption & Implementation Guidance Recommended AI use cases, integration approaches, and pilot or PoC pathways aligned to value and feasibility.
• Implementation-Ready Guidance Clear next steps for migration, delivery phases, operational readiness, best practices, and risk considerations.

What You Will Achieve

By the end of this engagement, you will have:

• A clear view of your application, data, and workflow landscape
• A unified AWS-aligned target architecture
• A phased modernisation roadmap tied to business priorities
• Clear AI and automation opportunities linked to measurable value
• Implementation-ready guidance for modernisation and AI pilots

Business Value

• Reduced operational and infrastructure costs
• Improved scalability, reliability, and performance
• Faster release cycles and time to market
• Lower manual effort through automation
• Better data quality and analytics readiness
• Strong justification for AWS and AI investment

Timeline

• Discovery & Roadmap: ~2 weeks
• Implementation: depends on scope and complexity

Procurement

• Delivered as a Professional Services engagement
• Available via Public Listing or Private Offer
• Executed in the customer’s AWS environment