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DAMO Managed Services (Digital Application Management and Operations)

Enterprises are stuck in legacy application maintenance contracts, consuming 90% of IT budgets while hindering innovation. Traditional vendors apply outdated techniques to modern software, creating inefficiencies and growing technical debt. Thoughtworks’ DAMO™ transforms application management with the EvolvOps framework by integrating AI-driven automation, predictive analytics, and modern engineering practices. Instead of maintaining legacy inefficiencies, DAMO establishes a modern application management foundation—first optimizing Thoughtworks-built apps, then scaling modernization across the enterprise. This phased approach cuts maintenance costs by 60%, reinvesting savings into innovation for AI-powered, future-ready systems. Don’t let legacy vendors hold you back—modernize with DAMO and cross the chasm to next-gen application management.

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About

Legacy application maintenance consumes up to 90% of IT budgets, hindering innovation and increasing technical debt. Traditional vendors rely on outdated techniques that sustain inefficiencies rather than transforming IT ecosystems.

Thoughtworks' DAMO™ (Digital Application Management & Operations) replaces legacy models with an AI-powered modernization and AIOps enablement, leveraging Amazon CloudWatch and AWS X-Ray, Amazon CodeGuru, Amazon Q and Amazon Bedrock to transition enterprises from reactive maintenance to proactive modernization. This shift frees up resources for innovation and accelerates AI and cloud adoption at scale.

How Thoughtworks optimizes application management with AWS and AIOps

AI-driven predictive and autonomous operations—Using Amazon Q and Amazon Bedrock to power incident triage, root cause analysis, and resolution generation, reducing MTTR and preventing recurrence through self-healing workflows.

Integrated observability with intelligent correlation—Enabling end-to-end visibility and context-rich alerting with Amazon CloudWatch, AWS X-Ray, and custom metrics pipelines, enhanced with AI-based anomaly detection and event correlation.

Cloud-native automation at scale—Leveraging Amazon EKS, AWS Lambda, and Step Functions to build event-driven, auto-remediating workflows orchestrated by AI agents.

AI-augmented runbook and knowledge ops—Using generative AI to automate playbook generation, change risk analysis, and knowledge base enrichment, enabling faster onboarding, fewer escalations, and higher SRE efficiency.

Cost optimization through data shaping and AI insights—Reducing legacy maintenance costs by up to 60% through observability-driven data control (CloudWatch Logs Insights, Systems Manager) and AI-assisted efficiency recommendations.

End-to-end modernization and AIOps readiness—Combining application modernization with an AIOps readiness framework using Amazon CodeGuru, AWS AppComposer, and the Model Context Protocol (MCP) for AI agent integration into operations.

Thoughtworks’ AWS-centric application management approach

Our phased modernization strategy ensures a structured transition from maintaining legacy applications to AI-driven, scalable IT operations, leveraging advanced AWS technologies.

Engagement phases and example timeline:

Phase 0: Portfolio Assessment (Weeks 1–2) Inventory applications, assess modernization and AIOps potential, define observability baselines.

Phase 1: Transition (Weeks 3–6) Set up teams, transfer knowledge, deploy observability and AI telemetry. Light-touch AIOps setup.

Phase 2: Expansion (Weeks 7–30) Increase automation coverage, integrate AIOps agents, deploy LLMs for triaging and remediation.

Phase 3: Steady State (Week 31+) SLA-backed delivery with AI-powered incident handling, runbook generation, and continuous optimization.

Systemic Solutioning (Weeks 25–36) Identify opportunities for full-stack automation, embed agentic workflows, and optimize run cost.

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