Koala in the Field: Resolving Gmails for Enterprise Upsells

This company powers generative media for over 1 million developers, giving them the tools to customize and scale AI models on serverless GPUs. But with most of that growth happening through self-serve adoption, the team needed a repeatable way to identify when power users were ready to convert into enterprise customers. They turned to Koala to make it happen.

The problem: Scaling past self-serve (with a lot of gmails)

By intentionally choosing not to require a business email at sign-up, this company faced a very common conundrum. On the one hand, hobbyists and dabblers could engage without company approval (or even a company at all!). On the other, gmail signups made it more difficult to separate accounts most in need of certain enterprise features like API access, private model hosting, and inference and training kernals.

This company needed a way to systematically qualify and engage only those accounts that would benefit from these features, without spamming users that might have no interest or need.

The solution: Turn high usage into high-intent pipeline

The Play this company built in Koala focused on converting anonymous power users into enterprise pipeline:

1. Track high-usage self-serve accounts: Create a dynamic list of accounts with high product usage in the past 7 days that weren't current enterprise customers or in active sales cycles. Further filtered to include only companies with high website traffic according to Clearbit enrichment.

2. Use AI qualification agents to answer a single critical qualification question: "Does the company have a product or service that leverages generative AI for media?" This focused pre-qualification step ensured sales reps only engaged with accounts that had confirmed commercial AI implementations.

3. Discover additional contacts: Automatically identify and enriched contact information for key buyer personas including:

• Head of Generative AI
• Director of AI Media
• AI Innovation Leads
• CTOs/CEOs (especially for startups)
• Technical Decision Makers
• ML Engineers (as potential champions)

4. Account Enrichment: For qualified accounts, AI agents gathered additional valuable context by researching:

• Technology stack details (references to Stable Diffusion, DALL-E, etc.)
• Developer ecosystem information (API marketplaces, plugin systems)
• Growth indicators (AI/ML engineering job postings)
• Recent product announcements related to generative AI

5. Personalized Engagement: Sales reps received comprehensive account briefs including specific usage patterns, enrichment findings, and personalized talking points highlighting how enterprise features would address their specific implementation challenges.

By adding a tight layer of AI qualification on top of product usage, this company gave their reps a signal-rich, pre-qualified list of accounts to work—without wasting time chasing hobbyist devs.

How to build your own self-service upsell motion

This company has an extremely healthy self-service motion, but you don’t need 1M users to run the same play! This blueprint will walk you through how to set up your own self-serve upsell motion:

WHO: Strategic Targeting

Target the intersection of three key factors:

• Active product users approaching usage limits or showing sustained adoption
• Companies meeting firmographic thresholds (traffic rank, employee count)
• Organizations with confirmed commercial implementation of your technology

Key Personas: Focus on both technical champions (initial users) and economic buyers (their leadership), creating multi-threaded relationships that bridge from implementation to strategic value.

WHEN: Timing for Maximum Impact

Trigger the play when accounts exhibit features like:

• Significant usage increase (>30% week-over-week)
• 80%+ consumption of self-serve tier limits
• Multiple feature adoption indicating production implementation
• Consistent usage patterns over 14+ days
• Enterprise documentation or pricing page visits

WHAT: AI-Enhanced Execution

Four-step engagement process:

1. AI Qualification: Deploy research agents to verify if the company has a product or service that leverages generative AI for media - the essential qualifying question
2. Account Enrichment: For qualified accounts, gather additional context about technology stack (e.g., Stable Diffusion, DALL-E usage) and growth indicators (hiring for AI/ML roles)
3. Stakeholder Discovery: Use waterfall prospecting to identify and gather contact information for key buyer personas (Heads of Generative AI, Innovation Leads, CTOs, ML Engineers) beyond initial technical users
4. Personalized Engagement: Deliver account-specific insights and messaging that connects product usage to enterprise value
5. Define high-usage thresholds for self-serve accounts
6. Layer in AI research to verify key use cases or buying power
7. Enrich accounts with tech stack, growth signals, and org maturity
8. Discover decision makers with auto-prospecting tools
9. Craft personalized outreach with AI in Koala Coach

This playbook helps turn happy users into paying customers—exactly when it benefits both sides.

Want help building a version of this Play for your team?

Let’s build it together →