AI cannot act on a system it cannot reach. We re-architect, migrate and support legacy platforms in increments, so the business keeps operating and AI agents get clean APIs, data and permissions to work with.
Legacy modernization is our service for keeping ageing systems in production while re-architecting, migrating and exposing them through modern APIs. We use AI-assisted analysis and code migration to shorten the work and finish by making the system agent-ready. It is for CTOs and operations leaders whose core platforms limit what AI can do.
MIT NANDA found that 95% of generative AI pilots show no P&L impact, and Gartner expects 40% of agentic AI projects to be cancelled by 2027. Across the 2026 studies the root causes repeat: data readiness, integration with legacy systems, missing governance and skills. In logistics, 65% of operators say they are stuck in ad-hoc experimentation because legacy TMS and WMS systems limit deployment.
The systems are not bad. They are the business. An order platform built a decade ago encodes rules nobody has written down, feeds downstream systems through nightly files and cannot be paused for a rewrite. A big-bang replacement risks the operation; doing nothing means every new AI initiative starts by scraping screens.
Modernization has changed. LLM-assisted code analysis, test generation and language migration remove much of the manual translation that made these projects multi-year. The hard part remains judgment: what to keep, what to strangle, in what order, and how to prove nothing broke.
A clear plan for migrating off outdated platforms safely, and a reason for every step.
Monolith to services without a rewrite, using the strangler pattern and stable interfaces.
LLM tooling does the reading and the first draft. Engineers own the review.
Data integrity first, then speed. Historical data becomes usable for analytics and AI.
On-premises or mainframe to AWS, Azure or Google Cloud, with the infrastructure defined as code.
The interfaces that let AI agents work with the system instead of around it.
For systems that are staying: keep them performing, secure and understood.
Modernization is measured by what did not break. These practices keep it that way.
Functions move one at a time behind a stable interface. The old path stays available until the new one has run clean in production, and traffic can be routed back in minutes.
Automated tests, generated from real transactions where documentation is missing, define what the system does before we change it. Every increment must pass them.
For data and financial flows, old and new run side by side and their outputs are reconciled until they match. Discrepancies are investigated, not tolerated.
Monitoring, alerting and on-call cover both the legacy and the modernized parts. Support continues after the last increment; 89% of clients stay longer than 12 months.
40+US carriers aggregated in the platform
A San Francisco logistics SaaS re-architected to microservices on AWS in two years, with carrier rate comparison and portals used by Fortune 500 shippers.
25printing shops in Germany running the new ERP
A decade-old print POS modernized into an ERP with CRM, cash desk, pricing, stock and production control, with accounting integrations, in 25 German shops.
6 monthsto complete the migration to AWS
A marketing SaaS serving 3,500+ brands in 70+ countries moved from IBM to AWS in six months, with weekend cut-over and one-minute replication.
Under a yearfrom Salesforce no-code to a full property-management SaaS
Hoste's short-term rental management product rewritten from Salesforce no-code into a Node.js, React and Google Cloud SaaS with a self-service portal.
A full assessment of the legacy estate: code, architecture, data, infrastructure, security and the people who know it. Output: a ranked list of constraints and a modernization roadmap that protects existing investment.
Before new technology, we refine what exists: reduce technical debt, define the target architecture and mark the seams where the system will be split. Your technical stakeholders sign off the plan.
Each increment is scoped, tested against pinned behavior and released with rollback ready. AI-assisted tooling speeds the translation; engineers own the review. First results within 2 weeks.
Performance monitoring, operational insight and adjustments after each release. The modernized system becomes the platform for the next automation and AI work.
Updating software that still runs the business but has become expensive, fragile or impossible to extend. It ranges from moving an application to the cloud unchanged, to replacing the database or language, to re-architecting a monolith into services, to a full rebuild. The goal is not newer technology for its own sake but lower maintenance cost, reliability, and the ability to integrate new capabilities, including AI, without risking the operation.
Rehost (move as-is to new infrastructure), replatform (change the runtime or database with minimal code change), refactor (restructure code without changing behavior), re-architect (split into services or change the architecture), and replace (rebuild or buy). Most programs combine them: rehost first to stop the bleeding, then refactor and re-architect the parts that block the business. The assessment tells you which combination fits which module.
A single application replatformed to the cloud can take a few months. Re-architecting a core platform is a program of one to several years, delivered in increments that each go to production. The supply chain platform we re-architected to microservices has been evolving for 10+ years with 99.999% uptime; a decade-old POS system became an ERP for 25 print shops over a longer engagement. AI-assisted migration shortens the translation work substantially, but the schedule is set by verification, not by typing speed.
Undocumented behavior that breaks when it is changed, data loss or corruption during migration, integrations that fail silently, a team without expertise in the old technology, extended parallel maintenance of two systems, and a big-bang cutover that cannot be reversed. We manage them with behavior-pinning tests, profiled and reconciled data migration, incremental cutover with rollback, and support that covers both systems until the old one is retired.
Technically, yes; economically, not always. If the business logic is sound and the cost is in the platform, modernize. If the application no longer fits the process it was built for, replacing it, sometimes with an off-the-shelf product, is cheaper. If it works, is stable and is not blocking anything, supporting it as-is can be the right decision. The assessment answers this per application with numbers, and we will say so when modernization is not worth it.
An AI agent needs three things a legacy system usually lacks: a stable API it can call instead of a screen, an identity with scoped permissions so it can only do what it should, and an audit trail of what it did. Agent-ready modernization adds those in front of the existing system, often before any deeper re-architecture, so automation and AI work can start while the modernization program continues underneath.
A 45-minute discovery call. Bring the system everyone is afraid to touch; we will tell you what to keep, what to change and in what order.
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