Defining our ICP at this early stage involves making educated assumptions from the limited data we have. This section will naturally evolve as we gather more insights from the market.

Right now, we're focusing on startups building AI-native products, especially growth-stage companies that have achieved product-market fit with an established user base. Our goal is to deliver tangible value to product leaders while creating a solution that AI engineers genuinely love.

Our go-to-market strategy targets this core ICP while cultivating relationships with future tech-enterprise customers ($30M+ ARR) who are on the journey to becoming AI-native.

| Category | Details |
|----------|---------|
| Description | Growth-stage startups with AI products that have achieved product-market fit |
| Criteria | - Minimum $100k MRR<br>- 15-500 employees<br>- Non-regulated sectors<br>- At least 1k+ MAU |
| Job Title | We build for the AI teams:<br>Primary focus:<br>- AI Product Manager<br>- Analyst<br>Secondary users:<br>- AI Engineer<br>- Data Scientist |
| Why them? | - Native AI products align well with our value proposition<br>- Short sales cycles + Quick decision-making<br>- Growth-stage companies recognize analytics as critical |
| Examples | Wordtune, Superhuman, Jasper AI, Midjourney, Perplexity |
| Industries | Productivity, Martech, Salestech<br>

FAQs

• What qualifies as an AI product? AI products are offerings whose core value proposition depends directly on AI quality. These companies build their entire product experience around agents, assistants, chatbots, and RAG systems. They've made significant AI investments and are deeply committed to increasing these investments as they scale.

• Why focus on early-stage AI companies? We believe early-stage AI companies will lead the emerging agentic economy for several reasons:

  • They represent our High Expectation Customers—once we satisfy their demanding needs, we'll be well-positioned to serve companies that later evolve into AI-native organizations.

  • Existing analytics tools simply don't address their specific needs for tracking AI quality and user satisfaction.

  • Their agile structure (without extensive data departments) enables faster and more meaningful integration of data tools.

• Why cultivate relationships with established companies? Launching successful AI products at scale is often a lengthy journey for larger companies ($50M+). This process typically involves hiring talent, designing solutions, and iterating through pilot → beta → limited release → general availability. By supporting these companies early in their AI journey, we position Sticklight as a trusted partner who understands their unique challenges when they're ready to scale their AI initiatives.