Build Effective Internal Tooling with Claude Code | Towards Data Science

Towards Data Science

by Eivind Kjosbakken

February 23, 2026

AI-Generated Deep Dive Summary

Building effective internal tooling has become more accessible than ever with the help of AI-powered coding agents like Claude Code. These tools enable developers to quickly create personalized applications, particularly from scratch, which can significantly streamline repetitive processes and boost efficiency. Traditionally, building such tools required substantial time and resources, making it difficult for teams to justify the effort unless the task was highly repetitive or time-consuming. However, with AI-powered agents like Claude Code, the cost of development has decreased dramatically, allowing teams to build internal tooling faster and more efficiently than ever before. The article emphasizes the importance of identifying tasks that are repetitive and time-intensive—such as document processing, customer request routing, or API documentation generation—as prime candidates for automation. By finding these bottlenecks, teams can prioritize building internal tools that address specific needs. For example, automating document checks or customer service workflows not only saves time but also ensures deterministic results, reducing variability and improving consistency across processes. For AI enthusiasts, this development is particularly impactful because it democratizes the ability to create custom solutions. By lowering the barrier to entry for internal tooling, Claude Code enables teams to focus on high-value tasks while letting AI handle routine operations. This shift not only enhances productivity but also allows companies to scale their solutions more effectively, ultimately driving innovation and efficiency in their workflows. In an era where automation is key to staying competitive, leveraging AI tools like Claude Code to build internal tooling is no longer a luxury but a strategic necessity. By embracing these technologies, teams can unlock new levels of productivity, consistency, and scalability—ultimately setting themselves apart in the ever-evolving landscape of data science and artificial intelligence.

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Originally published on Towards Data Science on 2/23/2026