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.
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.
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.
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.
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.
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.