Data and AI Readiness Assessment
Get clear, in-depth and independent expert analysis and recommendations to evolve your data strategy and jumpstart your adoption of AI technologies
Learn moreACCELERATORS

Unsure whether your engineers can work effectively with AI tooling in their daily practice.
Concerned that code quality will limit what AI tooling can safely do in your systems.
Worried your competitors are pulling ahead by adopting AI in their engineering?
Want to move faster with AI in software delivery but don't know where to start?
Need a clear business case for investing in agentic development practices?
Struggling to know whether your platforms are structurally ready for AI agents to work within them reliably?
Unsure whether your engineers can work effectively with AI tooling in their daily practice.
Concerned that code quality will limit what AI tooling can safely do in your systems.
Worried your competitors are pulling ahead by adopting AI in their engineering?
Want to move faster with AI in software delivery but don't know where to start?
Need a clear business case for investing in agentic development practices?
Struggling to know whether your platforms are structurally ready for AI agents to work within them reliably?

WHAT WE ASSESS
The assessment runs two parallel tracks simultaneously, examining your delivery process and your codebase together, because both must be ready before AI adoption is safe at scale.
We score your software delivery lifecycle across eight stages against the Codurance Agentic AI Maturity Model – from planning and design through to deployment and governance.
AI adoption across all 8 SDLC stages scored 0–4
Human oversight and governance practices
Agent loop maturity and self-reinforcement
Tooling landscape assessment (Claude Code, Sonnet, Codex)
Gap analysis against the Codurance preferred agentic model
We analyse your codebase directly - identifying the structural characteristics that determine whether AI tooling can operate within it safely, reliably, and without compounding technical debt.
Code quality, complexity, and naming clarity
Test coverage and mutation score
Documentation and architectural boundary definition
Security posture and dependency health
AI interpretability; how well agents can reason about the code
ASSESSMENT OUTPUT
You receive a single structured report that is evidence-based, specific, and actionable. Not a generic set of recommendations, but a gap analysis and roadmap built from what we actually found in your systems.

HOW IT WORKS
Four steps to a clear understanding and agentic AI roadmap, typically completed in two weeks.
We agree which platforms and teams are in scope, establish access to your source control and toolchain, and schedule sessions with key engineering stakeholders.
Our AI-powered assessment engine analyses your codebase across both tracks, producing scored findings on code quality, test coverage, security, and dependency health.
Our Principal Craftspeople conduct structured interviews and toolchain reviews, scoring each delivery lifecycle stage against the Codurance Agentic AI Maturity Model.
We present findings, walk through the roadmap together, and agree next steps – whether that is implementation support or a follow-on engagement.
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