Bye, Bye Inconsistent Research Tagging

A pile of hash tag puzzle pieces

In the rapidly changing landscape of product delivery, the effectiveness of tools used to store and retrieve insights can significantly impact a team’s ability to innovate and respond to customer needs. Traditional repositories, while necessary, have often fallen short, relying heavily on a flawed pillar — consistent research tagging across projects. This expectation of uniformity, while idealistic, clashes with the reality of dynamic, fast-paced work environments. The result? Inefficiency, frustration, and, ironically, insights lost in the very systems meant to preserve them.

The challenge with consistent tagging is not just theoretical; it’s practical and pervasive. We’ve encountered companies going to lengths as extreme as hiring librarians to manage and enforce research tagging consistency. Despite these efforts, both librarians in our case studies left within a year, citing the task as Sisyphean. The reason is clear: expecting an entire organization to adhere to a rigid tagging system is not only unrealistic; it’s counterproductive.

At Rutabaga, our approach to building a repository diverges from the traditional path. Understanding that modern systems should adapt to user behavior rather than requiring user behavior to adapt, we’ve integrated a RAG (Retrieval-Augmented Generation) AI system for insight retrieval. RAG systems extract information from pre-existing sources, in our case, only the vetted research data published to the repository, so you can trust the results. This system doesn’t just liberate teams from the tyranny of tags; it meets them where they are, respecting and adapting to their mental models and ways of working.

Rutabaga’s AI does the heavy lifting, enabling users to find connections and insights without the prerequisite of company-wide tag uniformity. This is not just a feature; it’s a philosophy. We believe that tools should enhance productivity, not hinder it with additional layers of bureaucracy. By focusing on the needs of researchers and their stakeholders, we’ve designed Rutabaga to seamlessly fit into existing workflows, facilitating a smoother, more intuitive process for uncovering and leveraging customer insights.

In crafting Rutabaga, we’ve been meticulous in understanding the intricacies of how research teams and their stakeholders operate. This foundational knowledge has allowed us to build a repository that not only stores insights but makes them effortlessly retrievable and actionable. With Rutabaga, we’re not just offering a solution; we’re setting a new standard for how insights are managed and utilized in product delivery.

In conclusion, while consistent research tagging might remain a requirement within many systems, Rutabaga represents a leap forward. By embracing AI to understand and work within the natural behaviors of its users, Rutabaga ensures that insights—no matter how they’re tagged—are never out of reach.

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