G.Tx - Agentic AI platform for application modernization at scale

Some legacy codebases were written decades ago by people who have since moved on. Others were never really written by people at all: a previous modernization vendor ran a COBOL system through a mechanical translator, the output Java compiled and shipped, and the original team dispersed before anyone documented what it produced. Either way the code is opaque to the team that owns it now.

The question this article addresses is what happens when that team decides to replace it. Replacing a feature in a system nobody fully understands is a different engineering problem from green-field work, and it goes wrong in characteristic ways. Done manually, a rewrite turns into a long archaeology project: engineers read the legacy code, hold a mental model of what it does, write a replacement, and then argue about whether the replacement matches. With no automated tests, "matches" is a judgement call. With AI assistance, the failure modes shift but do not disappear: code shaped by translation patterns rather than by the feature's actual behaviour, code shaped to pass whatever tests happen to be in front of the model, defects that compile cleanly and ship.

This article describes the process engineers design and run to address those failure modes. We call the engagement shape the Transformation Pilot: a focused pass that takes a single feature out of the legacy codebase and carries it through Design, Build, and a phased Run to production. The pilot consumes the artefacts produced by the Understand phase. It produces a working component the team can own and extend, and a process the engagement can iterate for the next feature.

The kind of code this is about

The same shape recurs across legacy Java modernization engagements. The code compiles and runs and carries the business. There are no tests. There is no documentation worth trusting. The team that wrote or translated the code is long gone. What is left is opaque code, an unknown blast radius for any change, and a current team that avoids modifying it because nobody can predict what will break.

Two flavors show up most often. The first is genuine long-lived legacy: code written years ago, modified by many hands, with documentation that drifted out of sync long before anyone noticed. The second is auto-translated legacy: Java emitted by mechanical translation from COBOL or a similar source, where the surface is opaque and the translation team has dispersed. The end state is the same. The methodology generalizes across both.

The Transformation pilot

A Transformation Pilot takes one scoped unit through the modernization process end-to-end. It is a focused engagement, not a system-wide commitment. The output is a new component running in production, validated against the legacy behavior it replaces, and a process the team can re-run on dependent components.

The G.Tx modernization process organises the work into four phases: Understand, Design, Build, and Run. The pilot runs the last three. Understand happens before the pilot and produces the artefacts the pilot consumes. We covered Understand in a separate case study showing how dead code analysis alone can reveal that nearly half of an auto-translated codebase carries no semantic weight.

What follows is a walk-through Design, Build, and Run, in the order a pilot runs them.

Design: shaping the transformation strategy

Design begins once Understand has produced the system picture. The phase shapes the strategy for the pilot: which unit to take through, the order in which dependent parts of the codebase will be transformed if the pilot expands, the contract the new component must satisfy, the integration tests that capture what the legacy version does today, and the workflows that will run in Build.

The process does not arrive fully assembled, but it does not start from a blank page either. Years of engagements have produced a library of validated workflows: extraction patterns, evaluation shapes, and prompt structures for common transformation steps. Engineers start there. They analyze the codebase, the available evidence, and the constraints of the engagement, then compose and shape the process for this particular challenge.

Choices made up front include what counts as a feature in this codebase, what the integration tests need to capture, where coding agents are required, where a simple LLM prompt is sufficient, where deterministic scripts or programs are the right tool, what each step's contract looks like, and how the evaluations score outputs.

A generic "transform any legacy" recipe does not exist. A reusable shape does, and each engagement instantiates that shape against its own evidence. The steps engineers design are what carries the work. The prompts, agents, scripts, and evaluations all run inside that shape.

The integration tests are authored in Design against the legacy feature behavior. Inputs are synthesized from the artefacts Understand produced: method signatures, example values, dependency information. The tests themselves are generated by a workflow step that runs against legacy behavior. Engineers review them, refine where coverage is thin or the inputs are unrealistic, and only then are the tests treated as the behavioral standard downstream work is held to.

This is test-driven development applied to modernization. The tests come first, the new code is written against them, and the same tests judge whether the result is equivalent. Nothing downstream begins until the integration tests are in place and approved.

Build: generating and refining the modernized component

Build begins with the artefacts in hand: the contract, the integration tests, and the workflows engineers composed in Design. The phase produces a new component implementing the feature from scratch in modern Java.

The component generator works from the artefacts that describe the feature and a contract that specifies what the component must do: its interface, scope, and constraints. It does not see the integration tests themselves. Hiding the validation surface from the generator prevents a common failure mode where the output is shaped to pass a specific set of tests rather than implementing the feature correctly.

Legacy source code stays out of the generator's input by default. It is provided only where the engagement requires a specific integration to be preserved, for example a SQL stored procedure or an external API the new component must call in the original form. Outside those cases, the new component is shaped by the description of the feature, not by the patterns of the translator or the developers who wrote the original Java.

Most of the steps that compose the component are simple LLM prompts. Coding agents are used where files must be read and written holistically across the input set. Some steps are not models at all. Structural transformations, packaging, file scaffolding, and similar work runs as ordinary scripts and programs where deterministic compute is the right tool. Each step has a narrow, named job and a reviewable output. That is how engineers keep the work decomposable.

The result is a single component that passes the integration tests authored against the legacy feature.

Build runs two kinds of evaluation against each step's output. LLM evaluation lives inside an LLM or agent step. The step's output is scored by a judge prompt against the contract the engineers set for that step: shape match, scope, constraints. This is how a step decides whether its own output is acceptable before passing it forward. Functional evaluation is a dedicated step on its own. It runs the integration tests against the new component and reports the result. This is the only evaluation that sees the tests; nothing upstream has access to them. Both produce evidence the team reads.

Engineers do not review every intermediate prompt output or every agent diff. The process produces too much volume for that, and approving everything at every stage would defeat the point of decomposing the work. What engineers approve is the transformation output: the new component plus the evidence that it satisfies the integration tests. When an evaluation fails or surfaces a weakness, they refine the step that produced it. The refinement loop is part of the design. Each pass that does not yield an approvable result becomes the input to the next iteration of the prompts, both within the engagement and across the library of workflows we maintain.

Run: phased rollout to production

Run takes the approved component through production deployment. Engineers stay involved through the rollout: integrating the new component into the surrounding system, retiring the legacy code it replaces, and handling the cutover the production environment requires.

The rollout follows an incremental strategy. The component goes into production behind whatever controls the team uses to limit blast radius: feature flags, canaries, gradual traffic shifting, observation periods. The pilot is complete when the new component is carrying production traffic and behaving as the integration tests promised.

From there the same process applies to dependent components in the same area of the codebase, reusing the contract patterns, integration tests, and workflows from the first pass. Each subsequent pilot builds on the one before, and the outcome accumulates into a working modern subsystem rather than a single proof point in isolation.

A brief note on scale

An agent driving the work end-to-end can take on one feature at a time. Beyond that, its context window and judgement run out. The process above scales differently: each step is named, evaluated, and approvable on its own, so the same shape applies whether the target is one feature or the whole system.

The platform: G.Tx

The work in this article runs on G.Tx, Grape Up's agentic platform for enterprise legacy modernization. G.Tx organizes modernization into the four phases shown at the top of this article: Understand, Design, Build, and Run. The Transformation Pilot is the engagement shape that bundles Design, Build, and Run into a single focused pass on a scoped unit.

Each phase is backed by reusable workflows, structured context, and engineering governance. Engineers compose the workflows from a library validated across previous engagements, then shape them for the specific challenge in front of them.

Understand is a valuable output on its own. Many engagements stop there because the picture it produces is already enough to ground a modernization decision. The Transformation Pilot is what happens when the engagement continues.

What a legacy modernization pilot leaves behind

A Transformation Pilot leaves three things behind. The first is a modernized component running in production, validated against integration tests authored against the legacy behaviour. The second is a process the team can re-run on dependent components in the same area of the codebase, with the contract patterns, integration tests, and workflows from the first pass ready for reuse. The third is the workflow library used during the pilot, now enriched with whatever was learned from this engagement.

Every piece of the result is traceable. Engineers can show what produced the component, what evaluated it, what test results it passed, and who signed off. That traceability is what makes the pilot reviewable as evidence, and what makes the methodology repeatable across the dependent components that follow.

On one of our client engagements, we ran a deep dead code analysis against a Java codebase of 654,273 lines. Roughly 275,000 of those lines sat in the business-logic layer that had been auto-translated from COBOL by a previous modernization vendor. After deep static and semantic analysis, we estimated that between 120,000 and 150,000 of those lines would not exist in a hand-written Java equivalent. Nearly half the code carried no semantic weight.
What matters more than the numbers is how we got to them, and why no off-the-shelf static analyzer would have produced the same answer. The ratios here are specific to this particular auto-translated project. Hand-written legacy systems behave very differently. Without structured understanding of the codebase before transformation, none of this would have surfaced, and the modernization plan would have been built around the wrong codebase.

Why understanding comes before transformation

Modernization teams routinely jump from "we have legacy code" to "let's prompt an AI to rewrite it." That approach fails at enterprise scale for a simple reason: the first question is not how do we migrate but what do we actually have.

This is also where the difference between prompt engineering and a modernization workflow becomes concrete. A prompt is a single instruction handed to a model. A workflow is a repeatable, governed sequence of operations with structured inputs, validated outputs, and traceable evidence. Prompts produce snippets. Workflows produce decisions that a CTO can defend in a steering committee.

Before any transformation, you need structured knowledge of the system you're working with: business documentation, dependency maps, architectural reconstruction, static and semantic findings. That knowledge becomes the substrate for every downstream change.  Business logic reconstruction and dependency mapping answer what is worth migrating. Dead code analysis answers a related but different question: how much of what you see is actually real?

A transformation pipeline applied to a codebase you don't understand is a parallel waste machine. It will faithfully migrate every dead branch, every ceremonial wrapper, every empty-string initializer into your modern stack. An AI agent asked to migrate tens of thousands of lines of structural boilerplate will produce tens of thousands of lines of structural boilerplate in the target language. The waste survives the transformation. This is also why "can AI agents migrate legacy code reliably?" is the wrong question. Reliability is a property of the workflow surrounding the agent, not of the agent itself.

Two categories of waste

Dead code analysis splits findings into two categories.

Strict dead code is lines whose execution has no observable effect. The IDE will usually flag these.

Translation overhead is lines that are syntactically alive but exist only because a mechanical translator emitted them. The IDE cannot see this because the surface code is well-formed; every statement looks like real work.

Static analysis tools handle the first category. The second is where the volume hides - and where modernization budgets quietly evaporate. Detecting it requires semantic reasoning, codebase-wide context, and pattern recognition that no IDE inspection provides.

The legacy codebase under analysis

The client owned a large back-office system originally written in COBOL. A prior modernization vendor had performed a mechanical COBOL-to-Java translation through a decompilation toolchain. The output Java code compiled and ran in production. There were no automated tests. The only validation performed at the time of translation was manual, and it had happened years before we arrived. By the time the system reached us, nobody on the team could fully describe what the code did - the institutional memory of the translation effort had moved on, and the surface code was opaque enough that no one was confident enough to touch it.

We began with the Understand phase, the first step of our modernization process, focused on reconstructing what the codebase actually does before any migration is scoped. The process runs on G.Tx, Grape Up's agentic platform for enterprise legacy modernization, which models Understand as a set of reusable workflows backed by AI agents, structured context, and engineering governance. The dead code analysis workflow produced the findings the rest of this article is built on.

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What static analysis caught

Some of the dead weight was syntactically obvious: indicator-variable boilerplate left over from COBOL host-variable conventions, redundant explicit casts preserved from the bytecode, discarded DAO results, duplicate branches in if-chains, redundant re-initializations of locals. The IDE could see all of it. In this codebase the relevant inspections had been silenced because the warning count was unusable. A finding technically visible to static analysis behaved, in practice, as if it were invisible.

Integer stationOutInd = 0;
// ... no writes anywhere ...
if (stationOutInd != 0) { stationOut = ""; } // always false

Even with the IDE's help, the visible findings explained only a small fraction of the auto-translated layer. The bigger story sat behind what the IDE could not see.

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What semantic analysis revealed

The architectural patterns were harder. Each one looked like ordinary Java to an analyzer. Each line allocated, called, or assigned something. The waste was architectural, not syntactic, and only became visible once we looked at the codebase as a whole.

The ValueHolder marshalling dance. Wrapper-class boilerplate emulating COBOL's BY REFERENCE.  Every multi-output call became three lines of wrap-call-unwrap, often on the same variable repeatedly:

copyCountHolder = new ValueHolder(Integer.class, (Object) copyCount);
returnCode = printFilter.searchStationCopyCount(
	stationPrint, "DOCUMENT_TYPE_A", (ValueHolder<Integer>) copyCountHolder
);
copyCount = (Integer) copyCountHolder.getValue();

In idiomatic Java the same sites collapse to a return value, a record, or a small result class.

Section-global state emulation. COBOL paragraphs share state through working storage, a flat namespace visible to every paragraph. The translator preserved that model by giving each service module its own Context class and turning every former local variable into a context field accessed through a wrapping getter on every term of every expression.

this.getServiceContext().setBrand(this.getServiceContext().getBrandCode());
this.getServiceContext().getInvoice().setBrandCode(this.getServiceContext().getBrand());

The deeper finding came from cross-referencing reads and writes: many context fields were written by exactly one paragraph and read by exactly that same paragraph. They had no business being state at all. They were locals masquerading as state because the translator did not know the difference.

DTO bloat. COBOL PIC X(n) working-storage fields default to spaces, not null. The translator preserved the equivalent by initializing every Java string field to `""`. Every COBOL 01-level record became a Java DTO with one field, one getter, one setter, and one empty-string initializer per string field.

The IDE's redundant-initializer inspection only fires when the explicit value matches the JVM default. "" is not the default for String (which is null), so the inspection treated every empty-string initializer as intentional.

A few smaller patterns followed the same logic: identity assignments via UxRuntime.assign for COBOL MOVE statements that needed no coercion, and UxRuntime.memset calls on Java objects that did nothing. Each was invisible to static analysis because each looked like a real method call.

The same translator habits also produced latent correctness bugs, not just overhead. Methods that take a String parameter and reassign it across dozens of lines (a literal translation of COBOL BY REFERENCE) silently lose every write at return, because Java is pass-by-value for object references:

public void formatLetterMessage(Long period, Long invoiceId, String message) {
	// 50+ lines of work, repeatedly reassigning `message`
	message = StringUtils.replaceCharAt(message, charPos, ' ');
	// method ends — every write is lost
}

Elsewhere in the same codebase, the translator used ValueHolder precisely to emulate pass-by-reference correctly. The pattern of forgetting to wrap is the bug. Try/catch blocks that perform conditional database lookups and write a result through a setter, only to be overwritten by an unconditional setter immediately after the block, fall in the same category: dead code at the line level, latent defect at the behaviour level. In a system without automated tests, neither shape had any chance of being noticed.

Aggregate picture of the dead code

In this particular auto-translated codebase, strict dead code accounted for roughly 5–10% of the 275,000-line business-logic layer. Translation overhead accounted for another 35–45%. Together, roughly 45–55% of the auto-translated layer would not exist in a hand-written Java equivalent - between 120,000 and 150,000 lines of code carrying no semantic weight.

The bulk of that volume came from a small number of patterns:

These ratios reflect this specific auto-translated project. Other codebases, especially hand-written legacy systems, distribute their waste very differently. The methodology generalizes; the percentages do not.

In the worst-affected individual methods, 30–50% of the body was dead or boilerplate at the line level. A developer reading those methods was spending up to one line out of every two on mechanical noise before reaching anything that described the actual business behaviour.

Why this changes the modernization conversation

1. Migration cost estimation collapses by half when overhead is removed. Quoting "275,000 lines to migrate" anchors the budget. Quoting "approximately 130,000 lines of real logic, plus 145,000 lines of removable overhead" reframes the engagement entirely — both in scope and in sequencing. This is, in concrete terms, the ROI of a feasibility analysis for legacy modernization: half the scope you were about to budget for is not real.
2. AI-assisted transformation amplifies whatever you feed it. An agent asked to migrate the ValueHolder dance will faithfully migrate the ValueHolder dance. The Understand phase puts the cleanup before the migration, not after.
3. Test generation is more reliable on real code than on boilerplate. Auto-generated tests for dead branches and clobbered setters pass but verify nothing. Understand-phase outputs allow downstream test-generation workflows to skip dead surface area entirely — which matters even more in systems like this one, where no test suite existed and behaviour had to be reconstructed from code rather than from assertions.
4. Static analysis alone is insufficient. The IDE saw a small fraction of the problem here. The remaining required semantic analysis aware of the translator's idioms.
5. Modernizing in place is a real option. Not every legacy system needs to be rewritten or replatformed. Once dead code and hidden dependencies are mapped, in-place hardening like removing overhead, recovering documentation, restoring testability, is often the higher-ROI path. The decision to migrate, modernize in place, or split the system between the two should follow the analysis, not precede it.
6. Human-in-the-loop validation remains mandatory. Several findings were latent correctness bugs hiding behind dead code. Auto-deleting them without engineering review would be reckless. The output of a dead code analysis workflow is a curated, traceable finding set, not a green-light-to-delete list.

How the G.Tx Understand operationalizes this

The dead code analysis workflow produces, for each finding, a classification of what is dead, the location in the codebase, and the rationale explaining why it qualifies as dead. Aggregate counts per classification are available as well, so engineering teams can see both the individual evidence and the overall distribution of waste across the codebase. Every classification is traceable back to source locations or runtime evidence.

And dead code is not only a code-level phenomenon. The same analytical lens applies one layer up: endpoints that no client has called in years, scheduled jobs that nobody remembers writing, service modules whose only consumer was decommissioned long ago, infrastructure quietly burning budget for traffic that no longer exists. Code-level dead code is a maintainability and correctness problem. Functionality-level dead code is a cost and risk problem. Both belonging the Understand phase, because both shape the same decision: what is worth migrating, what is worth hardening in place, and what should simply be turned off.

That last point matters for hallucination control. Models hallucinate when they infer from incomplete context. The artifacts produced during Understand, classified findings, traceable evidence, mapped dependencies, are exactly the grounding downstream agents need during transformation. Hallucination is reduced before any code is touched, because the model has real evidence to work with instead of having to guess at the codebase.

Lessons for legacy modernization

• Quantify before you migrate. The single most valuable artifact in a modernization engagement is a defensible number for real code volume. Without it, every estimate is fiction.
• Auto-translation defers cost; it does not eliminate it. A clean compile is not evidence of a maintainable codebase. In this engagement, half the code was overhead, and the only thing keeping that overhead in production was that nobody was confident enough to touch it. The code was unreadable, undocumented, and unverified. Removing anything felt riskier than leaving everything.
• The IDE is not enough. Roughly 90% of the waste in this codebase was invisible to off-the-shelf static analysis. Semantic, codebase-aware analysis closed the gap.
• Dead code is a correctness signal, not just a hygiene problem. Several of the patterns we found were latent bugs the team did not know they had.
• Modernization is an orchestration problem. No single prompt, agent, or tool produces findings of this kind. It takes reusable workflows, curated context, structured outputs, and disciplined human review.

Modernization decisions made without an Understand phase are decisions made about the wrong codebase. In this engagement, the "wrong codebase" was roughly twice the size of the real one, and the real one was the only one worth migrating.

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If you suspect your own auto-translated or long-lived legacy system is carrying overhead nobody has measured, the G.Tx Understand phase exists precisely for that conversation. Reach out - we'll start with a focused feasibility analysis for legacy modernization and produce a defensible picture of what you actually have.

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If you’re running Sybase in 2026, you already know the uncomfortable truth: the platform still works, but the ecosystem around it is quietly contracting: SAP patches ASE, yet engineering investment, tooling innovation, and community momentum are all flowing toward HANA and the cloud. ASE gets maintenance. Everything else moves on.

For most IT managers and DBA teams, the question is no longer whether to migrate. It’s how to do it without breaking things that have run reliably for 20 years.

The migration is bigger than “move the database”

When leadership hears “database migration”, they picture moving tables. What’s actually in a Sybase estate looks more like this:

• Database logic - Business logic written in T-SQL or Watcom SQL (SQL Anywhere), deeply embedded across stored procedures, functions and other database structures is the largest workstream.
• Connected applications - Every app querying data, and other Sybase products like IQ, and PowerBuilder. Each carries a distinct risk profile and should be scoped independently.
• Integration layer - CIS federation, proxy tables, driver stacks, ETL pipelines, BI connections: the glue between systems, frequently undocumented.
• Sync and replication - Replication Server is its own infrastructure layer, SQL Anywhere adds niche sync tools like SQL Remote and MobiLink. None of it has a modern equivalent - this requires redesign, not conversion.
• Operations and monitoring - scheduled jobs, alert rules, maintenance, performance monitoring, and backup strategies built on sp_sysmon, mon* tables, Backup Server, and dbcc. The knowledge transfers, the implementation doesn't.
• In-database services - Extended stored procedures, encryption, LOB handling, and in some estates SOAP endpoints or Java-in-DB. Most need external replacements on the target platform.

Where Sybase ASE migrations to Azure break

The most dangerous category isn’t missing features - it’s features that look identical but behave differently across platforms. ASE and SQL Server share a T-SQL lineage, which creates a false sense of safety. The syntax compiles - but the runtime behavior diverges in ways that pass testing and surface under production load. Here are some of the most common examples:

• Transactions: ASE's chained mode and nested transaction semantics differ from both SQL Server and Oracle. Error handling, rollback behavior, and transaction scoping each work differently across all three platforms.
• Identity: ASE isolates @@identity inside stored procedures and triggers. SQL Server's @@identity leaks across scopes, potentially returning wrong values from nested calls.
• Locking: ASE's per-table locking schemes (allpages, datapages, datarows) and lock promotion thresholds have no equivalent on SQL Server or Oracle. Concurrency patterns tuned for ASE risk deadlocks post-cutover.
• LOB access: Data types map cleanly (unitext to nvarchar(max) or NCLOB). The pointer-based access model (readtext, writetext, textptr) does not and must be rewritten.
• NULLs: In Oracle, an empty string is NULL. Code checking for empty strings silently returns wrong results without any error.
• Collation: ASE sets sort order at server level only and defaults to case-sensitive binary. SQL Server and Oracle support multiple collation levels with different defaults, changing JOIN cardinality, GROUP BY grouping, and UNIQUE constraint behavior.

Beyond syntax, cross-database patterns compound the problem: USE, db..object references, and CIS passthrough are everywhere in ASE estates and break the moment the engine changes. Migration tools like Microsoft's SSMA handle syntax conversion, but they don't detect behavioral divergence. The dangerous gaps aren't in what fails to convert - they're in what converts cleanly but runs differently.

The actual risk picture (by the numbers)

A full feature mapping of ASE (versions 15,16) against SQL Server and Oracle on Azure across all deployment tiers gives a clearer picture: (Based on Grape Up G.Tx internal analysis across enterprise migration assessments.)

• ~90% of ASE features have a clear migration path - low or medium risk
• SQL Server carries ~20% fewer high-risk items than Oracle across every ASE version - the shared T-SQL lineage makes a measurable difference
• PowerBuilder pushes the high-risk share to ~19% and should be scoped and phased independently

In ASE alone, roughly 30 features have no direct equivalent or workaround, but every one of those has a modern replacement approach on Azure, although some require significant architectural redesign rather than direct substitution.

SQL Server or Oracle on Azure - how to choose

The right target depends on what’s actually in your codebase.

SQL Server has the shorter path for most ASE estates. The T-SQL lineage reduces rewrite volume, and the platform carries fewer high-risk items across the board. Thirty years of divergence still mean real work, particularly around transaction semantics and locking behavior.

Oracle carries higher effort by default - PL/SQL vs. T-SQL is a language rewrite, NULL handling differs, and no direct replace for ASE’s nested transaction rollback semantics.

Deployment tier matters too. Azure VM, Managed Instance, and SQL Database each involve different trade-offs on compatibility, operational overhead, and cost. The right answer depends on your specific feature usage.

The operational risk of waiting

The engineers who know the platform deeply - who understand the undocumented behaviors, the operational quirks, the edge cases in the locking model - are retiring. That institutional knowledge compounds the migration effort every year it walks out the door.

Starting your Sybase migration to Azure: assessment before assumptions

A reliable migration starts with knowing exactly what you have. That means automated discovery across your live codebase - versions, features, dependencies, behavioral edge cases - not a manual audit based on what the team remembers.

This is what Grape Up’s G.Tx platform does in the assessment phase. G.Tx runs automated inventory against your environment, maps features against the target platform, identifies behavioral differences that won’t surface in standard testing, and produces a high-level risk report. The same platform then powers execution - code conversion, schema migration, test generation, and validation - so the assessment and the migration run on a single consistent picture of your estate, not on handover documents.

The engagement runs in four phases. Each ends with a deliverable and a client sign-off before the next begins:

1. Assessment (1–2 weeks) - free, no commitment. Automated discovery of your system, delivered as a risk report with a next steps recommendation.
2. Feasibility (2–4 weeks) - full migration plan with identified blockers, mitigations, and timeline.
3. Proof of Concept - a representative subset of your codebase migrated on real data, against agreed acceptance criteria.‍
4. Scale- full migration, AI-accelerated, with phased cutover and operational handover.

You control the pace. Nothing moves to the next phase without your approval.

Frequently Asked Questions: Sybase Migration to Azure

Is migration to another SQL database the only option?

For most estates, a like-for-like migration is the most pragmatic path — it preserves existing logic and minimizes rewrite scope. But depending on your goals and architectural dependencies, a full redesign may be the better long-term investment. This means decomposing the monolithic database into independent components that communicate with each other rather than relying on shared database logic. The result is a more flexible, extensible, and maintainable architecture that is no longer constrained by the boundaries of a single database engine. The best approach can be decided during the Feasibility phase.

What is the best migration target for Sybase ASE - SQL Server or Oracle on Azure?

SQL Server on Azure is the recommended target for most Sybase ASE estates. The shared T-SQL lineage reduces rewrite volume and lowers the share of high-risk migration items by approximately 20% compared to Oracle. Oracle remains viable for estates where PL/SQL integration or Oracle-specific features are already part of the architecture, but it carries higher baseline effort. The final choice depends on your codebase - a G.Tx assessment will map your specific feature usage against both targets before you commit.

How long does a Sybase migration to Azure take?

Timeline depends on estate size and complexity, but the structured phases give reliable checkpoints: Assessment runs 1–2 weeks, Feasibility 2–4 weeks, Proof of Concept varies by scope, and full scale migration is planned during Feasibility. The phased approach is designed to migrate and validate the most business-critical functionality first, progressively offloading the original system only as each phase proves stable on the target platform. Feasibility analysis is the most reliable way to get an accurate estimate for your specific environment.

What are the biggest risks in a Sybase ASE migration?

The highest-risk category is not missing features - it's behavioral divergence: code that converts cleanly but runs differently in production. Transaction semantics, identity scoping, locking behavior, NULL handling in Oracle, and collation defaults are examples of gaps that may not be caught in testing and only surface under real load. Standard migration tools like SSMA automate schema conversion but are not designed to detect behavioral differences. Automated analysis of your codebase can surface these discrepancies early, making sure they never reach production.

Is Sybase ASE still supported in 2026?

SAP ASE 16.0 reached End of Mainstream Maintenance on December 31, 2025, meaning SAP no longer provides new security patches or fixes for this version. ASE 16.1 retains mainstream support until December 31, 2030, giving organizations on that version more runway, but there are no new ASE versions on SAP's roadmap. New capabilities, cloud-native features, and tooling investment are being directed at HANA and cloud products. The surrounding ecosystem is contracting in measurable ways. The practical risk is not that ASE will stop working, but that maintaining it becomes increasingly expensive as the specialist talent pool shrinks, integration tooling is deprecated, and the burden of filling those gaps falls on internal teams — as unpatched vulnerabilities quietly accumulate.

How do Sybase ASE stored procedures migrate to SQL Server?

ASE stored procedures are written in T-SQL, which shares a lineage with SQL Server T-SQL - but decades of platform divergence mean direct conversion is rarely clean. Syntax differences are mostly handled by automated tools. The harder problems are behavioral: code that converts cleanly can still run differently in production, and standard conversion tools are not designed to catch them. Stored procedures rarely exist in isolation — understanding their true migration scope requires analysis in the context of the full system.

What does a Sybase migration assessment include?

An initial assessment covers automated discovery of a representative part of your system — architectural overview, feature usage, stored procedures, connected applications mapped against your target platform. The result is a report covering system health (including security findings), AI transformation feasibility, and top risks ranked by severity and impact — identifying behavioral differences that standard tools miss, with concrete next steps and PoC proposals. Grape Up’s G.Tx Assessment runs in 1–2 weeks, is free with no commitment, and becomes the foundation for all subsequent migration phases.

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