Accelerate Legacy Code Migration with Stride 100x
Transform complex, legacy codebases into modern, maintainable systems that minimize risk and maximize value.
Problem, Meet Solution
Stride100x leverages GenAI and automation to reduce the time consuming aspects of code modernization — while keeping you and your experts in control. Shrink massive challenges into manageable tasks, like visualizing complexity, translating code into human-readable language, creating implementation plans, and generating code.
Remove Tech Pain Points
Many of the largest organizations still run on outdated systems. This often results in tech debt buildup and business-impacting situations, such as:
Difficulty deploying new changes
Random outages
Performance issues that cause customer dissatisfaction
Stride100x addresses the underlying code issues that cause scaling and post-scaling challenges, providing a comprehensive, customizable solution that gets you up and running faster.
Re-Architecting +10-year-old Spaghetti
25-year-old FinTech player faced a significant challenge with its 10+ year-old Microsoft .NET applications, which were bogged down by over 600 classes, 10,000 code files, and 2,000 database tables. This bloat lead to slow, timing-out functions and halted user interactions, resulting in disrupted sign-ups and payments.
Stride brought its 100x GenAI-powered services to our client, automating key processes such as dependency visualization and code generation. This reduced human effort from 3 days to 3 hours per service, cutting the projected migration time in half and saving BillHighway at least 12 months of development time.
Key Features
Understand your system dependencies with Class, Sequence and Entity Relationship diagrams
Generate human-readable requirements about what your code is actually doing
Provide feedback, context, and direction at key moments in the process
Automate and augment implementation plans
Tailor code outputs to your organization’s specifications
Self-service capabilities to work at your own pace and budget
Stride100x: How it Works
PHASE 1 | Inspect and Trace
Our team starts with a one-week integration phase where we connect an AI-powered toolkit to your system. During this phase, we analyze 1-2 key aspects of your application, generating detailed insights, complexity reports, and visual documentation that reveal your system's current state.
PHASE 2 | Modernization Slice-by-Slice
The next phase is a repeatable six-week process that modernizes an initial vertical or domain slice of your application. The Stride100x AI toolkit accelerates what traditionally takes months of architectural planning down to weeks of iteration as we repeat the modernization process on each “slice” of code. The result is architecturally-aligned sample code that your development team can quickly implement and test.
PHASE 3 | Support Mode
Over time, Stride100x shifts to a support model where our experts provide guidance, troubleshooting, and thought partnership while your team completes the modernization process.
PHASE 4 | A New Day
The end result is a codebase that:
Is clearly understood by both technical and business teamsEnables faster feature deliveryReduces defects and operational burdenIs easier to maintain and extendSupports smoother onboarding of new team members
PHASE 1 | Inspect and Trace
Our team starts with a one-week integration phase where we connect an AI-powered toolkit to your system. During this phase, we analyze 1-2 key aspects of your application, generating detailed insights, complexity reports, and visual documentation that reveal your system's current state.
PHASE 3 | Support Mode
Over time, Stride100x shifts to a support model where our experts provide guidance, troubleshooting, and thought partnership while your team completes the modernization process.
PHASE 2 | Modernization Slice-by-Slice
The next phase is a repeatable six-week process that modernizes an initial vertical or domain slice of your application. The Stride100x AI toolkit accelerates what traditionally takes months of architectural planning down to weeks of iteration as we repeat the modernization process on each “slice” of code. The result is architecturally-aligned sample code that your development team can quickly implement and test.
PHASE 4 | A New Day
The end result is a codebase that:
- Is clearly understood by both technical and business teams
- Enables faster feature delivery
- Is easier to maintain and extend
- Reduces defects and operational burdenIs
- Supports smoother onboarding of new team members
Let’s Talk.
Learn how we can to unlock your team’s potential and solve your biggest challenge.
Still Have Questions?
Traditional AI and machine learning is essentially pattern recognition at scale. ML can be used in various predictive settings, but it typically requires customized training and rarely applies to anything it hasn’t been specifically trained on.
GenAI builds on traditional ML – you can do a lot of the same things, like predictions based on data – but it doesn’t usually need specific training on your data set, and it adds the ability to create new digital assets upon request. This applies to all sorts of asset types – text, images, videos, code – and can be applied in some way in almost any business setting.
Learning to use GenAI and to leverage it for your business requires a focused investment of time and effort. Even if the technology evolves and improves, the investment in figuring out how it might work in your environment is worth making now. And if you do find a way to drive ROI, the benefits of that optimization will start to compound as soon as you get started. You also don’t want to lag your competitors, who may figure out how to optimize their workflows and products before you do.
Generally speaking, no. The only people who need a custom hosted or fine-tuned LLM are either working with highly unusual data sets, have cost considerations that make cloud solutions too expensive, or security concerns that take cloud off the table. For most internal use cases (knowledge bases, code generation, content creation) off-the-shelf LLMs are going to be fine.
As noted above, costs can be an issue, but typically only if security considerations require you to host your own model or if you are running at consumer scale. We can help predict costs by calculating expected usage over time, and match those costs to technical solution requirements.
The best thing about LLMs is that they will improve on their own! Stride can help you balance expected model evolution with pure infrastructure investments that will apply to any LLM-powered application stack. Of course you don’t want to overbuild your plumbing, but good architecture makes it easier to leverage the power of LLMs.
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