Modernize legacy applications, connect enterprise systems, and develop cloud and data foundations that support your AI priorities. Sequence the work around business continuity and a practical migration path.
Technology leaders working with legacy systems, fragmented data, or infrastructure that constrains new products.
A useful AI workflow often depends on existing systems that were not designed to work together. Resolve the integration and architecture constraints alongside the new capability.
Understand application dependencies, technical debt, infrastructure readiness, and data access. Identify what must change to support the prioritized use case.
Plan APIs, data flows, cloud services, and application boundaries. Consider AWS and cloud-native approaches where they suit the workload and operating model.
Sequence application changes, integration work, and data movement around agreed acceptance criteria. Make dependencies, cutover decisions, and recovery plans explicit.
Review performance and operational behavior after each increment. Update runbooks and support ownership so the modernized system can evolve with your team.
We agree the scope and outputs around your starting point. A typical engagement can include:
A business wants an assistant to work with information held in a legacy application. We assess the data and permissions, define an integration boundary, and deliver a bounded workflow before deciding whether wider re-platforming is needed.
Bring a priority workflow and the people who own it. We will review the intended outcome, dependencies, and constraints, then define an initial scope and acceptance criteria.
Migration and cloud choices are scoped against your existing estate and operating requirements.
Plan this engagementBring product design, architecture, and full-stack engineering together. Build custom AI applications, retrieval-augmented generation, machine learning solutions, and intelligent workflows around a defined business outcome.
Explore this serviceApply Safe AI practices across the development lifecycle: specification, implementation, review, testing, and release preparation. Combine automation with clear acceptance criteria and accountable decisions.
Explore this serviceCombine application maintenance, performance support, and AI evaluation with a clear operating model. Give the product a path to reliable operation, ongoing improvement, and team ownership.
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