Application and API readiness
Decouple priority capabilities, expose controlled APIs, improve integration patterns, and prepare workloads for AI-enabled workflows.
Cloud modernization for AI readiness
Modernize the foundations that prevent enterprise AI from scaling
DigiScience targets the application, data, cloud, security, platform, and operating gaps that block governed AI delivery—without forcing an unrelated, all-at-once modernization programme.
Modernize with a purpose
Every modernization workstream maps to an AI use case, production control, scalability requirement, or operating outcome.
AI-readiness workstreams
Data and retrieval readiness
Improve data access, quality, classification, metadata, lineage, vector retrieval, document pipelines, and governed data paths.
Cloud and platform readiness
Strengthen landing zones, identity, private networking, Kubernetes, automation, observability, resilience, and cost governance.
Security and compliance readiness
Establish IAM/RBAC, secrets, encryption, data boundaries, policy checks, monitoring, audit evidence, and residency controls.
Delivery and operations readiness
Introduce infrastructure as code, DevSecOps, MLOps, LLMOps, evaluation gates, release control, rollback, and runbooks.
FinOps and capacity readiness
Plan model consumption, GPU or managed service capacity, unit economics, budgets, chargeback, utilization, and optimization.
Assessment outputs
Current-state dependency view, AI-readiness gaps, target platform pattern, security control requirements, prioritized modernization backlog, cost and risk considerations, and delivery roadmap.
Production design considerations
High availability, disaster recovery, regional strategy, private connectivity, environment isolation, observability, capacity management, incident handling, data lifecycle, and cost governance.
Modernize what the AI roadmap actually needs
Connect modernization investment to the use cases, platforms, controls, and operational outcomes it enables.
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