Today is the big day. Maybe your product just launched, your new feature went live, or the results of the survey are in. Thousands of comments, reviews, support tickets (or whatever other data you can imagine) are flooding your system, and your team desperately needs to make sense of it all. But first, someone has to organize all this feedback into a meaningful form.

Using traditional methods, a team might need 3-4 weeks to taxonomize the data, assuming they can work on the problem full-time. By then, the crucial issues will have festered, opportunities will have passed, and the critical window for rapid response will have already slammed shut. You can also use a pre-built taxonomy, but this will capture only the categories that you were able to anticipate in advance.

An AI-powered Just-in-Time Taxonomy system solves this problem. Instead of spending weeks manually creating categories or trying to force your data into prechosen boxes where it does not really fit, the Just-in-Time Taxonomy system employs a pair of collaborating AI agents to analyze your data and build a custom taxonomy in under an hour. This gives you a taxonomy that was built from your own data on day one.

But how does this work? And when does it make sense to use this approach instead of traditional methods?

How it works: A two-agent system

The Just-in-Time taxonomy system uses two specialized AI agents that work in tandem to analyze your data.

The Category Building Agent (aka Category Builder) is responsible for discovering what categories actually exist in your data. It works through a three-stage process: first, it reads through your content and generates candidate labels for the themes it finds. For example, if your data was customer feedback about a mobile app, the agent might identify candidate labels like “battery drain,” “login problems,” “slow loading,” “confusing navigation,” “crashes on startup,” “great design,” “helpful support,” and dozens of others as it processes each piece of feedback.

The Category Builder is a three-stage pipeline

In the second stage, the agent groups these candidate labels through semantic clustering. It recognizes when different labels are referring to the same concept. For example, if customers mentioned “battery life,” “battery drain,” and “poor battery performance,” these would be clustered together because they’re all talking about the same issue using different words. Similarly, “log in issues,” “login problems,” and “can’t sign in” would group into one cluster, while “crashes,” “app crashes,” and “freezing” might form another cluster.

Finally, in the third stage, the agent selects the best representative label for each cluster. From the battery-related cluster, it might choose “Battery Issues” as the clearest label. From the login cluster, it might select “Login Problems” as the most straightforward term.

This process eliminates redundancy and creates a clean taxonomy where each category represents a distinct concept, even when customers express the same issue in many different ways.

The Document Tagging Agent (aka Document Tagger) takes any new piece of content and maps it to the appropriate categories from the taxonomy the Category Builder created. Think of it as the agent that actually does the organizing work once the categories exist.

The Document Tagger maps records to the categories discovered by the Category Builder

When a new piece of feedback comes in—let’s say a customer writes “This app is killing my phone’s battery and takes forever to load anything”—the Document Tagger reads this content and identifies which categories apply. In this case, it would likely tag this feedback with both “Battery Issues” and whatever category the system created for loading/performance problems.

The Document Tagger doesn’t just look for exact keyword matches. It understands the meaning and context of the content. So if someone writes “My phone dies so fast when I use this app,” the agent recognizes this as a battery issue even though they didn’t use the word “battery.” Similarly, “The app is super sluggish” would get tagged appropriately even though “sluggish” wasn’t in the original candidate labels.

This agent can process content as it arrives, instantly categorizing new feedback, support tickets, or survey responses using the taxonomy that emerged from your initial dataset. It can also handle cases where a single piece of content belongs in multiple categories—like our example above that touched on both battery and performance issues.

Building a Synergy: Agentic Collaboration

A snapshot of a three-level taxonomy

The Category Builder and the Document Tagger work together in a closed loop to build multi-level taxonomies that capture both broad themes and specific details.

Let’s continue with our mobile app example. During the initial three-stage process, the Category Builder might create a top-level category like “Performance Issues.” But as the Document Tagger processes more content, it notices that “Performance Issues” is getting tagged very frequently and contains distinct sub-themes.

When the Category Builder runs another round of analysis, it discovers that Performance Issues actually break down into: “Battery Drain,” “Slow Loading,” and “App Crashes.” The Category Builder creates this second level of hierarchy, with “Performance Issues” as the parent category and these three as subcategories.

Now the Document Tagger can work with this richer structure. When new feedback comes in saying “The app froze three times today and completely drained my battery,” the agent can tag it at multiple levels: the broad “Performance Issues” category, and the specific “App Crashes” and “Battery Drain” subcategories.

This collaborative process can continue recursively. If “App Crashes” becomes heavily populated, the agents might discover it breaks down further into “Crashes on Startup,” “Crashes During Use,” and “Crashes When Closing.” The taxonomy grows more sophisticated as it encounters more data, always staying grounded in what’s actually present in your content.

Use Cases: Beyond Product Launch Monitoring

While product launch feedback is a compelling example, Just-in-Time taxonomies (for short, JIT taxonomies) solve organizational challenges across many different types of unstructured text data.

Survey Analysis represents one of the most powerful applications. Open-ended survey responses often contain the richest insights, but they’re also the hardest to analyze at scale. Traditional approaches require manually reading through hundreds or thousands of responses to identify themes, then coding each response by hand. With Just-in-Time taxonomies, the agents automatically discover the themes present in your responses and categorize them accordingly. More importantly, you get to see the entire response distribution—not just summary statistics, but how responses cluster around different themes and what the full spectrum of sentiment looks like within each category.

Social Listening at Scale becomes feasible in ways that weren’t possible before. Because the taxonomies are auto-generated, you can monitor thousands of influencers, social media accounts, and online conversations simultaneously. Traditional social listening requires pre-defining what topics and keywords to track, which means you miss unexpected conversations and emerging themes. With Just-in-Time taxonomies, you can cast a wide net across your entire industry ecosystem and let the system discover what people are actually talking about, including topics you hadn’t thought to monitor.

Voice of Customer (VOC) programs generate massive amounts of unstructured feedback from multiple touchpoints: social media mentions, review sites, email feedback, focus groups, and customer interviews. Each of these may get their own taxonomy, or alternatively, a single monolithic taxonomy may be built that unifies all of these different data sources under a single organizational structure.

Customer support conversation analysis is another natural fit. Support tickets, chat logs, and call transcripts contain valuable information about recurring issues, but this knowledge often stays trapped in individual conversations. By automatically categorizing support interactions, teams can quickly identify systemic problems, track resolution patterns, and understand which issues are driving the most contact volume.

The common thread across all these applications is the same fundamental challenge: you have large volumes of text that need to be tagged, structured, and made searchable so that patterns become visible and actionable insights can emerge. Whether it’s understanding why customers are calling support, discovering unexpected themes in survey data, or tracking sentiment across multiple feedback channels, Just-in-Time taxonomies transforms unstructured text into organized, analyzable data.

Enriching the Taxonomy: Finding the KPI drivers

Creating the taxonomy can be just the first step in a larger business solution. The next step is enriching the taxonomy by calculating “importance scores” that reveal which categories actually matter for your business outcomes. We do this by using the taxonomic categories as predictors of a key performance metric—like customer satisfaction scores in survey data, or support quality ratings in customer service conversations. The strongest predictors are the most important drivers of your KPI. If these performance indicators are not initially present in the dataset, we may derive them using AI.

When the categories are sorted by importance scores, the most important categories are visually obvious. It is often a good strategy to go down the list in order, addressing the issues identified by the AI in the order that they occur. This simple strategy can have profound effects on KPIs.

In one case, a software company wanted to improve its customer support, but had thousands of support conversations with no quality ratings. We used AI to evaluate each conversation and generate a “support quality” score.

Once we had the AI-derived quality score, we used the taxonomic categories as predictors to see which conversation topics were associated with poor support outcomes. The results were surprising. The company expected complex technical issues to drive poor support quality, but “password resets” appeared as the strongest predictor of low-quality interactions.

Top customer satisfaction score (CSAT) drivers ranked by importance

Drilling down revealed that password reset conversations consistently involved long hold times, multiple transfers between agents, and frustrated customers. The company discovered that its password reset system was broken in a way that made simple requests unnecessarily complicated, but because these seemed like “easy” tickets, management had never paid attention to them.

They fixed the underlying system issues and streamlined the password reset process. As a result of fixing this single issue, overall support quality scores improved by 20%, and the volume of password-related tickets dropped by 60%. After all issues had been addressed, support quality improved by 40%.

Without the AI-derived quality metric and taxonomic analysis, they would have continued focusing on complex technical issues while a simple broken system was quietly frustrating thousands of customers with what should have been routine requests.

When do you need a JIT taxonomy?

Just-in-Time (JIT) taxonomies fill a gap that human taxonomists cannot fill: If you need data-driven insights faster than a human taxonomist can provide them, a JIT taxonomy is the only option. That makes them perfect for time-sensitive analysis, where waiting weeks for manual categorization means missing the opportunity entirely. But the applications extend far beyond just speed.

JIT taxonomies really shine when you’re exploring uncharted territory—entering new markets, launching innovative products, or researching unfamiliar customer segments where you genuinely don’t know what themes will emerge. Traditional taxonomies require you to define categories upfront, but sometimes the most valuable insights come from patterns you never would have anticipated. This makes them invaluable for competitive intelligence, emerging trend detection, or any situation where you’re trying to understand “what’s really going on” rather than confirming what you already suspect.

They’re also ideal for evolving content streams where the conversation itself is constantly changing. Social media discussions, ongoing customer feedback, live chat transcripts, and user-generated content don’t stay static—new themes emerge, language evolves, and yesterday’s taxonomy might miss today’s most important topics. A JIT taxonomy adapts as these changes happen, making it perfect for brand monitoring, community management, and any application where staying current matters more than maintaining rigid consistency.

The approach works particularly well with moderate-scale datasets—typically thousands to tens of thousands of documents. This covers most business scenarios perfectly: quarterly feedback analysis, event-driven research, pilot program evaluation, or departmental data organization. It’s large enough that manual analysis becomes impractical, but focused enough that AI can identify genuinely meaningful patterns rather than getting lost in noise.

JIT taxonomies are also excellent for proof-of-concept work. Before committing to a major taxonomy project, they can quickly demonstrate what’s possible with your specific data. This makes them invaluable for pilot programs, budget justification, or simply exploring whether text analysis will provide value for your particular use case. And when you’re resource-constrained—whether that means lacking dedicated research staff, facing tight deadlines, or working within a limited budget—they can deliver insights that would otherwise require weeks of manual work or expensive consulting engagements.

JIT taxonomies are less suitable for massive enterprise taxonomies requiring thousands of predefined categories, situations demanding strict compliance with industry standards or regulatory frameworks, or long-term strategic classification systems where consistency across years matters more than speed. If you’re integrating with established enterprise systems or building something that needs to conform to specific legal or industry requirements, traditional approaches may be more appropriate.

But rather than viewing this as an either-or choice, many organizations find the most value in hybrid approaches where JIT taxonomies extend and enhance traditional human-built structures. Consider a large enterprise that has spent years developing a comprehensive taxonomy for customer feedback—they have well-established top-level categories like “Product Quality,” “Service Experience,” and “Billing Issues.” These broad categories work well and align with their reporting structures and business processes.

However, at the detailed level, their traditional taxonomy might categorize everything under “Product Quality” without capturing the specific nuances that emerge in real customer feedback. This is where JIT taxonomies excel. By running the system specifically on feedback already categorized as “Product Quality,” it can automatically discover granular subcategories that reflect what customers are actually saying: “Packaging Damage,” “Assembly Instructions,” “Color Accuracy,” “Durability After 6 Months,” and dozens of other specific issues that human taxonomists might not have anticipated or prioritized.

These AI-generated extensions can serve end users directly, giving them immediate access to detailed categorization while the broader enterprise taxonomy remains stable and compliant. Alternatively, the JIT results can guide human taxonomists, showing them exactly where their existing categories are insufficient and what new subcategories would capture the most meaningful distinctions in the data. Instead of guessing which detailed categories to add, they can see empirically what themes are emerging and how frequently they appear.

This approach gives organizations the best of both worlds: the governance and consistency of traditional taxonomies at the strategic level, combined with the responsiveness and data-driven precision of JIT taxonomies where detailed categorization matters most.

Conclusion: The Future of Data Organization

Just-in-Time taxonomies represent a fundamental shift in how we approach unstructured data. Instead of forcing your data into predetermined boxes or waiting weeks for manual analysis, you can let the data reveal its own structure and start extracting insights immediately.

The technology works because it mirrors how human experts actually think about categorization—identifying patterns, grouping similar concepts, and refining understanding as more information becomes available. But it does this at machine speed and scale, processing thousands of documents in the time it would take a human to read dozens.

Whether you’re launching a product, analyzing survey responses, monitoring social conversations, or trying to understand customer support patterns, the fundamental challenge remains the same: transforming unstructured text into actionable intelligence. Just-in-Time taxonomies make this transformation immediate rather than eventual, comprehensive rather than sampled, and adaptive rather than static.

The question isn’t whether AI will change how we organize and analyze text data—it already has. The question is whether you’ll take advantage of these capabilities to understand your data while the insights still matter, or continue waiting for perfect taxonomies that arrive too late to drive decisions.

Would you like to see how JIT taxonomy works? Interested in trying it on a sample of your data? Let’s talk!