Know before you commit.  See if G.Tx is the right path for your legacy modernization - and what it could look like

The assessment is free and carries no commitment to proceed further. It covers codebase analysis, AI-transformation qualification, risk identification, and a recommended next-steps report — all at no cost. The output is a fact-based report; what you do with it is your decision.

Before any analysis begins, Grape Up agrees on the LLM deployment model and security measures with you. There are four deployment options: Customer On-Premises, Customer Private Cloud, GU Managed Private Environment, or a Public LLM approved by the client. Your code is never used to train or fine-tune any AI model, is processed only within the agreed scope, and is isolated per engagement.

The report covers four areas: (1) system and code assessment — codebase structure, complexity, and core functional areas; (2) G.Tx qualification — where AI-driven automation brings the highest value, whether that's documentation, refactoring, migration, or test generation, with indicative efficiency gains; (3) risks and challenges — structural complexity, test coverage gaps, and knowledge constraints; (4) guidance and next steps — actionable priorities and a recommended path forward, either a focused Proof of Concept or a deeper G.Tx Feasibility Analysis. A full example report is publicly available on the Grape Up website.

The discovery workshop is a collaborative alignment session — you walk Grape Up through the product, its pain points, modernization goals, and system landscape, and together you define the scope of the analysis. No formal preparation is required beyond having the right people in the room: those who understand the system's business context and the team's technical constraints.

Previous analyses were likely based on interviews, documentation review, and expert judgment. G.Tx Assessment runs automated code analysis using the G.Tx Platform — mapping structure, dependencies, business logic, and transformation potential directly from the codebase. The output is grounded in what the code actually contains, not in what the team remembers about it or what documentation was written years ago. The report also specifically identifies knowledge constraints — including gaps in documentation and test coverage — as part of the risk picture.

You can share either a full or sample codebase — the scope is agreed with you before any analysis begins. The code sharing step is preceded by a discovery workshop where you and Grape Up align on goals, system landscape, and boundaries together.

The full process — discovery workshop, code sharing, AI-powered analysis, and report delivery — takes under one week.

The Assessment comes first and is free — it answers whether your system is a candidate for AI-driven transformation and at what level of automation. The Feasibility Analysis is the next phase, if you choose to proceed: it produces a full modernization plan, pilot scope, budget estimation, and business and technical documentation. The Assessment's recommended next steps will specify which path makes sense for your system.

The report is described as "zero sales pitch" — it includes transparent identification of where AI-driven automation brings the highest value, as well as structural complexity and constraints that could impact modernization. If the system isn't a strong candidate for AI-driven transformation, the report will reflect that. The guidance and next steps section will recommend the most appropriate path forward based on what the analysis actually found.

Grape Up has delivered assessments and modernizations in automotive, finance, aviation, and manufacturing. The most detailed public case study is in aviation, where G.Tx reduced a 24-week modernization to 5 weeks.