The Problem: An ambitious AI transcription platform had built the technology but not the market. Customers were interested, but no one could articulate why it mattered or how to make it work in the real world. The product was fast in the lab. In live sales calls, it felt sluggish. Raw AI outputs were worthless without a business context.
The Insight: This wasn’t a marketing problem or a product problem—it was a translation problem. Someone needed to sit with customers, understand their workflows, and funnel those insights back into both the go-to-market narrative and the product roadmap.
What I Did: Over three months, I became that bridge. I ran 12+ discovery calls with logistics and SaaS companies, capturing exactly how they used sales data, managed their CRM, and measured success. Instead of generic feature conversations, I documented real workflows: how sales managers used call data, when they needed automation, and why they’d pay.
Then I built a framework—Company → Project → Prompt—that let the AI understand business context. Suddenly, the product went from generating transcripts to generating decisions. With that clarity, we shipped 10 features in weeks. We cut 96% of the latency from 8 seconds to 0.3 seconds. We signed the first external customer and ran a paid pilot.
Why It Mattered: This shows how to work in ambiguity. No roadmap, no ICP, no validated GTM. But if you listen closely, customers will tell you everything. You just have to translate it back to the product and sales teams in a language they understand.
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