Lack of documentation adds complexity, but it doesn't make reliable updates impossible. With codebases developed over a decade or with multiple frameworks, we take a strategic approach: we start by analyzing the codebase, defining key test cases, and building an automation suite. This allows us to better understand the code's current functionality before intervention. Based on this foundation, we'll determine the best level to apply AI—whether it's at the API, datastore, or code contract level—to ensure reliable, consistent updates.

We design our process to fit smoothly into your workflows. First, we assess your current setup to identify potential areas of risk, then determine the best integration points for AI. Our approach emphasizes containment and control, so we typically work with isolated components of your codebase at first. This allows us to gauge impact, refine processes, and minimize disruption before applying any changes at scale. Transparency and alignment with your team's daily operations are central to this strategy.

Yes, it will save time — but with initial investment. During the early phases, we work closely with your team to fine-tune AI interventions, ensuring quality output from the start. While this initial phase may require some oversight, our goal is to reduce repetitive tasks, streamline maintenance, and allow your team to focus on high-value projects. With a clear strategy in place, AI can significantly reduce manual work, saving time and boosting productivity in the long run.