Eastgate Software, VCCI Tower, No. 9 Dao Duy Anh Street, Kim Lien Ward, Hanoi (2026)

05/10/2026

We’re proud to share that Eastgate Software has achieved the Select partner tier in the Claude Partner Network Services Track.

This recognition reflects the path we’ve been taking at Eastgate, bringing our engineering skills into new ways of working with AI. As we explore agent-based software development, we’re looking at how Claude, powered by Anthropic, can support teams as they build and deliver software.

We believe AI should support engineers, not replace their role. That means keeping human input at the heart of the work, while using AI to help teams move faster and take on more complex tasks. Our work with Claude is part of this ongoing shift in how we approach software development.

Explore more about us in the Claude Partner Network: https://partnerhub.claude.com/directory/partner/eastgate-software-jsc-partner

25/09/2026

If AWS, Azure, and Google Cloud can all run your workload, why does the choice between them still have such an impact on your migration costs?
Flexera's 2026 state of the cloud report estimates that 29% of IaaS and PaaS cloud spend is wasted, covering both the infrastructure used to run workloads and the managed platforms used to build and operate applications.

How workloads are matched to a cloud environment is one part of that picture. AWS, Azure, and Google Cloud differ across services, pricing models, performance characteristics, and operating environments, so the same workload can cost more and take more effort to run on one than on another.
Gartner's 2026 analysis of workload placement calls this “right workload, wrong cloud”, pointing to placement decided by preference or ad hoc choices instead of a clear policy.

At Eastgate, that assessment runs on a 52-point migration readiness checklist covering 10 areas, with each checkpoint classified by risk level so the issues that would cause the most damage after cutover get addressed first.

Access the checklist to find the gaps in your migration plan before any production workload moves: https://lnkd.in/eZw_am-3

18/09/2026

A behavior that looks wrong in the code can make perfect sense once you see everything that depends on it.

Kien, our QA/QC Manager, ran into exactly that while working on a healthcare platform modernization project in Israel. What looked like a bug had actually shaped how other systems, historical data, and business processes ran for years. So before touching the legacy code, our team started by "photographing its behavior" through characterization tests.

Kien walked us through how the team captures a system's current behavior, where agents help flag what's worth investigating, and why the final call still has to come from someone who understands what a change would actually break.

16/09/2026

More than 61% of healthcare security incidents trace back to software or hardware vulnerabilities, according to ENISA, which found that 80% of the organisations it surveyed had experienced them firsthand.

We saw that risk play out when a medical device company in Israel came to us. Their Windows Forms application was approaching end of life, but replacing it wasn't simply a matter of swapping in newer technology. The business needed to keep its existing workflows intact, and remain compliant throughout the transition.

With those constraints in mind, we modernized the technology without changing what the business already relied on. We kept the existing workflows and data semantics in place, replaced the Windows Forms interface with a browser-based platform, and used RESTful APIs to make data more accessible. A phased migration ran the legacy and new environments side by side, so operations continued without disruption during the switch.

After the migration, 100% of core legacy functionality stayed intact. Data accessibility improved by 30 to 40%, operational maintenance effort dropped by 20 to 30%, and regulatory compliance held the whole way through.

If your team is running an aging system the business can't afford to disrupt, where's the harder call: preserving the workflows already in place, or staying compliant while you migrate? Read the full case study below to see how we handled both.

15/09/2026

A whitepaper can be completely right and still not tell you anything about your own project.

That’s where our check tools come in. Every whitepaper on our website now comes with one, built around that whitepaper's own topic: a short set of focused questions, each pointing at one concrete part of your situation, so you can walk through it and see where it actually lands.

Right now that includes tools for checking if your system is ready for a cloud migration, whether a post-migration effort is actually on track, and whether a PoC is ready to move into production, plus other topic-specific tools tied to different whitepapers.

Take a look at the whitepapers and their check tools on our website. To use a check tool, simply leave your email to access it and start working through the questions.

Missing one for something you'd want to check yourself? Tell us what it is, and we'll help build it.

14/09/2026

In 2026, AWS outlines seven modernization paths. Picking the most advanced option on that list isn't always the right business call.
Our founder, Paul, has seen this trade-off play out in real modernization projects. A more advanced approach can require significant investment and take longer to deliver, while a simpler path can bring value much sooner. The challenge is knowing which trade-off makes sense for a particular application.

That decision starts with understanding the role an application plays in the business, the risks it carries, and what the company needs to achieve. From there, the modernization path becomes a business decision as much as a technology decision.

Where is the debate stalling on your team right now: whether to retire an app, rehost it quickly, or commit to a full refactor?

09/09/2026

One European enterprise spent years patching over a data problem by hand before realizing SAP and its own legacy systems were the ones causing it.

The mismatch between the two showed up as hundreds of data incident tickets a year, and nobody could say which system's number was right.

To solve this problem, we built an automated integration layer that connects SAP directly to the legacy systems instead of leaving them to run side by side and out of sync. Standardized APIs replaced the manual entry that used to fill the gaps between them.

Data now moves continuously through an event-driven design instead of waiting on a nightly batch job. The dashboard updates in under five minutes, even on the days operational reporting depends on it most.

Since the integration went live, data incident tickets are down 25%; manual data entry is down 40%. Half of daily and weekly reporting now runs without anyone touching it. None of this required retiring SAP or the legacy systems it used to argue with.

If your team is still reconciling two versions of the same report by hand, where's the actual disagreement coming from? Read the full case study in the comments to see how we got SAP and the legacy systems talking to each other.

07/09/2026

Give an AI coding assistant more autonomy in an untested legacy system, and speed isn't what changes first.

A 2025 trial by Becker et al. on real, familiar codebases found that developers using AI completed the tasks 19% slower, yet still believed they had sped up. Researchers called this gap the Brownfield Paradox - the advantage only holds while the scope stays small and the constraints stay visible, both of which disappear as the codebase grows older.

When we talk about this problem, our leaders always repeat one line before any AI program starts: “Measure it properly, because these programs are unusually easy to fool yourself about.”

Escaping the paradox means building a harness around the model you already have: guardrails before it acts, a second check after, and a human who decides what an old, strange bit of behavior actually means instead of letting AI quietly rewrite it.

The carousel below breaks down what that harness looks like, along with three questions that can tell you whether your project actually has one.

Where is your team most exposed right now: unclear scope, missing context, or constraints nobody has written down?

04/09/2026

When you look at public AI coding leaderboards, the numbers are impressive. GPT-4o and DeepSeek-V3 score 92.7% and 91.5% on standardized coding tests like HumanEval. And 88% of enterprise developers surveyed by IBM Research this year say an AI assistant makes them more productive.

But testing AI on real, complete codebases tells a different story.
RealBench, published this July, tested six leading models across 61 real GitHub repositories instead of isolated coding exercises. The best average score across all six models was just 19.39%.

The gap comes down to what each test actually asks a model to do. HumanEval hands it a short prompt and asks for about 11.5 lines of code. Legacy modernization hands it hundreds of connected files, business logic nobody wrote down, and a system it has never seen before, then asks it to fit new code into all of that safely.

That’s why context matters so much. When RealBench researchers removed system-design diagrams from the prompts and asked models to infer the structure themselves, pass rates dropped by up to 12.74 percentage points.

The context is only one part of the problem. Before scaling AI into legacy systems, enterprises need to understand what coding benchmarks actually measure, and what they leave out.

In this carousel, we break down 4 critical gaps between AI coding benchmarks and real-world software development, and what enterprises should measure before putting AI into production workflows.

Which of these four would trip up your project first: missing system context, repository scale, safety and reliability, or contaminated benchmark data?

01/09/2026

Generative AI can translate thousands of lines of COBOL, VB6, or PowerBuilder into Java or C # within seconds. On narrow, isolated tasks, it can even help engineers work up to 55% faster.

That speed makes modernization look successful in year one. The proof of concept works, the budget gets approved, and everyone moves on to the next milestone because the old code now runs in a new language.

But a new language does not necessarily mean a modernized system. Translating code without changing the underlying architecture simply carries the same technical debt into a newer syntax, and the consequences often remain invisible until the system has been running in production for months.

Through our own projects across Germany, the United States, Japan, and Singapore, we have seen this pattern repeatedly. The problem is rarely visible when the project first goes live. It tends to surface later, when the missing tests, fragile integrations, or unresolved dependencies begin to affect the system in production.

That is why the real modernization challenge is not how quickly you can translate legacy code. It is whether you can prove that the new system is reliable, integrated, and ready to evolve.

The three realities below come from systems we have modernized in transportation control, healthcare, and financial services, where getting year two wrong was never an option.

Where does your team's modernization roadmap carry the most risk right now: the missing safety net of automated tests, the plan for running the old and new systems in parallel, or the data silos between legacy systems and your ERP?

Eastgate Software

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