Koala in the Field: Finding Buyers on Competitive Solutions

This generative AI platform recently scaled to over 23,000 developers while processing more than 60 billion tokens daily.

A prime example of exceptionally well-timed product<>market fit, this startup is capitalizing on an abundance of open-source LLMs, and scarcity of GPUs. But even while adoption of their self-service product soars, sellers must still find target accounts that haven’t yet found them.

They turned to Koala to help.

The problem: Surface-level intent ≠ real opportunity

Sellers at this company have their hands full. There’s a whole market of potential demand, but there’s also plenty of inbound interest. The team can’t afford to waste energy on companies that don’t have the team, budget, or infrastructure to make use of the enterprise platform.

Of course, in an emerging market, there’s a big difference between organizations that could make use of their platform, and those ready to do so. So how would this company find the ‘ready to buy’ segment of their ICP?

The solution: Nuanced detection, strict qualification, and deep enrichment

One of the most successful Koala Plays run by this team involves hunting for use of competitive, but legacy solutions. Because this company sells to AI and data development teams, the documentation of their target accounts reveals much of the infrastructure powering those platforms. The goal here was to identify companies actively using these legacy solutions in a way that this company could easily replace with a more flexible, and cost-effective alternative.

AI Qualification agents apply a hard limit on which companies actually get enrolled in this Play, while AI-research infuses outreach with nuanced competitive differentiators.

Here’s what everything looked like end-to-end:

1. Only enroll companies in their ICP—which in this case is active target accounts under 999 FTE, that are not in a current sales cycle, and have already raised more than $50M.

2. Deploy AI research for Play qualification: Scan public docs, blogs, social posts, and marketing properties for any mentions of competitors—Gemini, or Vertex. If found, provide model specs.

3. Bring in additional research for hypers-specific messaging: Find business drivers to might support switching to a lower cost, more flexible AI model development strategy, and check for multi-lingual support (a key differentiator for this company).

4. Use Koala Coach to generate personalized messaging that explains how this company can solve some of the problems that the prospect might be experiencing using a legacy solution.

The outcome is a list of cleanly qualified accounts, backed by evidence, and ready for direct competitive positioning.

How to run the same Play

To replicate a Play like this, you only need a strong competitive signal and a binary decision rule. Koala handles the rest:

1. Define what counts as a high-signal reference to a competitor.
2. Track mentions across technical content.
3. Use AI agents to qualify (or kill) based on one clear question.
4. Route good fits to reps automatically.
5. Let Koala Coach build targeted outreach based on their current setup.

This turns passive competitive intelligence into active pipeline.

Want help building this Play for your team?

Let’s build it together →