5x faster. Enterprise legacy modernization at scale

The honest answer is that most cost estimates fail because they're built on assumptions about a codebase nobody fully understands. Grape Up approaches this differently: before any transformation begins, the G.Tx platform runs an automated analysis of the actual source code to generate budget estimates and timelines. The output is a concrete modernization plan with accurate cost projections — derived from what the codebase genuinely contains, not from what the team remembers about it.

In a production aviation project, Grape Up's G.Tx platform automatically generated 70% of the output files. Of the code lines produced, 33% required no changes from the initial AI generation pass; the remaining 67% were refined through a combination of developer edits and continued G.Tx assistance. The transformation compressed a 24-week timeline to 5 weeks. The platform uses reusable, audited transformation templates rather than freeform generation — which is what makes the output controllable and traceable at enterprise scale.

Most modernization failures happen because the scope, cost, and complexity were misjudged before the programme started. Grape Up's process separates the decision to transform from the commitment to a full programme: a one-week Assessment first qualifies whether the system is actually a candidate for AI-driven transformation and surfaces the real risks. Only then does the Feasibility Analysis produce a plan and budget. The Transformation Pilot that follows validates the approach on real code before full-scale investment is made.

Grape Up's G.Tx Feasibility Analysis is designed to answer exactly this question before any commitment is made. It evaluates the system's modernization potential and produces a transformation plan — or identifies cases where a different approach is more appropriate. The Assessment phase is available for free and takes one week, so organisations can get a data-driven answer without investing in a full programme upfront.

Yes. G.Tx generates agent-ready specifications compatible with AI coding tools including Codex and Claude Code, and supports integration with any external custom agent. The platform is designed to work alongside existing AI development tooling rather than replace it.

Grape Up's G.Tx platform is built around phased transformation: the legacy system stays operational while modernized components are progressively deployed, validated, and cut over. The process starts with a 1–3 month pilot on a scoped slice of the system — proving the approach works before the full programme scales. Business continuity is a design constraint of the methodology, not an afterthought.

G.Tx extracts business and technical documentation directly from the source code as part of the Feasibility Analysis phase — before any transformation work begins. This creates a verified baseline of what the system actually does, independent of whatever documentation exists or doesn't. Automated testing and validated deployment strategies are then used throughout transformation to confirm the modernized system matches that baseline.

Using the G.Tx platform, Grape Up delivers transformation up to 5x faster than traditional approaches. The Assessment takes 1 week; Feasibility Analysis takes 2–4 weeks; the Transformation Pilot runs 1–3 months. Full-scale transformation follows in phased iterations.

G.Tx builds business and technical documentation from the codebase itself during the Feasibility Analysis — it doesn't depend on institutional knowledge, interviews, or existing documentation. The platform is specifically designed for situations where the only reliable source of truth about the system is the code.

Grape Up's process is explicitly designed to surface complexity before it becomes a cost problem. The Feasibility Analysis uses automated code analysis to map the full scope of the system — including risks and challenges — and produces a phased modernization plan. The pilot phase then validates the transformation approach on real code before the full programme scales, so surprises are caught early rather than mid-programme.