Frequently Asked Questions

What does Mechanical Orchard do?
Mechanical Orchard is a legacy application modernization technology company headquartered in San Francisco, CA. We apply AI-enhanced tools and disciplined methods to continuously modernize, operate, and innovate on critical business applications, especially those on mainframes, allowing our customers to regain an innovative posture and win in their markets with the least risk possible.
Haven’t most companies already moved most systems into the cloud?
Not really. Take mainframes, for example: 71 percent of Fortune 500 companies rely on mainframes as their systems of record. This means mainframes support roughly US$13 trillion of revenue each year, half of the entire US GDP of US$25 trillion (2022). Every time you bank, purchase an item online for delivery, or book travel, you’re likely interacting with a mainframe.
How does Mechanical Orchard’s approach to modernization differ from what’s available today?
Mechanical Orchard helps companies de-risk and accelerate the transition from traditional applications, especially those on mainframes, to state-of-the-art cloud applications through a data-centered, reverse-engineering and replication approach.
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Unlike emulators and transpilers, we aim to understand the behavior of the system and its interdependencies with other systems. The working system is the spec for a cloud-based instance that we build and cutover, workflow by workflow, orchestrating between the original and the replica. Our disciplined transformation techniques— equivalence-focused, highly iterative, test-driven—create a malleable and modern foundation that keeps all the critical functions you need, and none of the layers of old workarounds and outdated adaptations that you don't.
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Our iterative approach, grounded in always-working software, means that even critical workflows can begin migrating onto the cloud in weeks, not years. Organizations can capture market shifts and opportunities quickly and elegantly, and accelerate or decelerate as timing, executive support, and market cycles allow.
Why should I take an incremental approach that replicates my existing system when it's far better to reimagine my entire system with new workflows and features?
The comprehensive “big bang” approach to digital transformation suggests there's a magical end state, much like a remodel of an old house: they combine updating old systems while installing new features. This approach addresses multiple dimensions simultaneously, introducing a compounding set of risks that multiply across costs, timelines and stakeholders. Unsurprisingly, they take forever and have an eye-wateringly high failure rate of 70 percent.

Because legacy systems are so critical, we believe it's essential to replicate them in the cloud first. Put another way: add nothing, change nothing, delete anything you can get away with. Only then can you truly begin to modernize continuously in a confident way.
What legacy languages are you expert in?
The beauty of our approach is that it creates a replica in the cloud of a particular workload based on its data flows across component interfaces. Because we can capture sufficiently representative data flows into and out of a given component, we can treat the component itself as a black box and reproduce its behavior in any language. Language choice, either source or target, doesn’t matter.
What do you mean by “AI-enhanced” when referring to your approach?
We weave AI into everything we do. For example, our engineers use generative AI as a “pair” to help us reproduce the behavior we’ve captured from the data flows in a test-driven fashion, treating the black box tests via the captured data flows as integration tests. We also currently use AI to help us visualize and map dependencies between workloads. As we build up our tooling and increase our proficiency with and leverage of AI, the process will become orders of magnitude faster.
When you say you operate the system, isn’t that the same as the typical vendor lock-in that currently exists?
Continuous modernization means continuous delivery: that’s how we reduce risk and get workflows in production faster. That means we need to run the system while we incrementally modernize it until the entire system is migrated. During that time, customers benefit from a finely-tuned, test-driven, automated cloud environment that is doing everything the mainframe did, with the same performance and functionality—except more and more components are running on modern code. When the modernization of the system is complete, customers can pair in with us and learn and/or take over anytime — the company retains the intellectual property and maintains control.
What does Mechanical Orchard do?
Mechanical Orchard is a legacy application modernization technology company headquartered in San Francisco, CA. We apply AI-enhanced tools and disciplined methods to continuously modernize, operate, and innovate on critical business applications, especially those on mainframes, for the Global 2000, allowing our customers to regain an innovative posture and win in their markets with the least risk possible.
Haven’t most companies already moved most systems into the cloud?
This is a problem that’s been swept under the rug for too long. Take mainframes, for example. Seventy-one percent of Fortune 500 companies rely on mainframes as their systems of record. This means mainframes support roughly US$13 trillion of revenue each year, half of the entire US GDP of US$25 trillion (2022). Every time you bank, purchase an item online, or book travel, you’re likely interacting with a mainframe.
How does Mechanical Orchard’s approach to modernization differ from what’s available today?
Mechanical Orchard helps companies de-risk and accelerate the transition from traditional applications, especially those on mainframes, to state-of-the-art cloud applications through an incremental, highly-disciplined approach. Or, “through a data-driven, reverse-engineering and replication approach.
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Unlike emulators and transpilers, we aim to understand the behavior of the system through AI-enhanced reverse engineering, build a replica operating in the cloud, where we use disciplined transformation techniques— equivalence-focused, highly iterative, test-driven—to manage performance, security, disaster recovery, multi-nodes and regions.
‍
Because workflows that run critical business processes can begin migrating onto the cloud in weeks, not years, organizations can capture market shifts and opportunities quickly and elegantly, and accelerate or decelerate as timing, executive support, and market cycles allow.
Why should I take an incremental approach that replicates my existing system when it's far better to reimagine my entire system with new workflows and features?
The comprehensive “big bang” approach to digital transformation aims to update old systems while installing new ones. They address multiple dimensions simultaneously, introducing a compounding set of risks that multiply across costs, timelines and stakeholders. Unsurprisingly, they have an eye-wateringly high failure rate of 70 percent.
What legacy languages are you expert in?
The beauty of our approach is that it creates a replica in the cloud of a particular workload based on its data flows across component interfaces. Because we can capture sufficiently representative data flows into and out of a given component, we can treat the component itself as a black box and reproduce its behavior in any language. Language choice, either source or target, doesn’t matter.
What do you mean by “AI-enhanced” when referring to your approach?
We weave AI into everything we do. For example, our engineers use generative AI as a “pair” to help us reproduce the behavior we’ve captured from the data flows in a test-driven fashion, treating the black box tests via the captured data flows as integration tests. We also currently use AI to help us visualize and map dependencies between workloads. As we build up our tooling and increase our proficiency with and leverage of AI, the process will become orders of magnitude faster.
When you say you operate the system, isn’t that the same as the typical vendor lock-in that currently exists?
Continuous modernization means continuous delivery: that’s how we reduce risk and get workflows in production faster. That means we need to run the system while we incrementally modernize it until the entire system is migrated. During that time, customers benefit from a finely-tuned, test-driven, automated cloud environment that is doing everything the mainframe did, with the same performance and functionality—except more and more components are running on modern code. When the modernization of the system is complete, customers can pair in with us and learn and/or take over anytime — the company retains the intellectual property and maintains control.
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