Initially building a tool for ML teams, they discovered the true pain point was creating AI-powered workflows for business users. This insight came from observing how first customers struggled with the infrastructure *around* their tool, not the tool itself.
While founder-led sales are critical, StackAI believes they waited too long to hire their first salesperson. Bringing in help earlier, around $500K ARR, would have accelerated their ability to test and refine their go-to-market strategy much faster.
To truly learn from go-to-market experiments, you can't be half-hearted. StackAI's philosophy is to dedicate significant, focused effort for 1-3 months on a single idea. This ensures that if it fails, you know it's the idea, not poor execution, providing a definitive learning.
StackAI found the bulk of enterprise revenue comes from expansion, not the initial deal. They operationalized this by creating a team of "AI strategists" who work with customers post-sale to proactively identify and build new use cases, driving deep account penetration and growth.
Spreading efforts across startups, SMBs, and enterprises created confusing signals. A deep dive into metrics revealed enterprises, despite being a smaller revenue portion, showed the highest expansion potential, prompting a decisive focus that unlocked growth.
StackAI's early attempts at using resellers were counterproductive because the product and messaging were evolving too quickly. Partners can't sell a moving target. The channel only became successful after the company established a clear ICP and repeatable value proposition.
The sweet spot for their transformational AI platform wasn't the largest corporations, which are too rigid to adopt new tech. Instead, it was mid-market companies (100-1,000 employees) that had budget and pain but were agile enough to implement new workflows successfully.