When testing ideas, the clearest signal of a top-priority problem is a customer's immediate willingness to commit to a large contract. Hesitation, requests for monthly plans, or budget excuses are strong indicators of a low-priority, "nice-to-have" problem.
In enterprise AI, competitive advantage comes less from the underlying model and more from the surrounding software. Features like versioning, analytics, integrations, and orchestration systems are critical for enterprise adoption and create stickiness that models alone cannot.
Product-market fit is confirmed through repetition. For Decagon, it was when the fifth and sixth customers independently described the same core problem, cited the same failed competitors, and expressed immediate willingness to buy, proving a repeatable market need.
Instead of starting with a scalable platform, Decagon built bespoke, perfect solutions for its first few enterprise customers. This validated their ability to solve the core problem deeply. Only after proving this value did they abstract the common patterns into a platform.
Instead of a generic presentation, Decagon scrapes a prospect's public data to build a working, tailored demo before the first sales call. This simulates the prospect's actual workflows, vividly demonstrating immediate value and accelerating the sales cycle.
If a large customer drags out a pilot indefinitely, it's a sign that your solution isn't solving a visceral, high-priority pain. When the need is urgent, enterprises will "bulldoze" through internal bureaucracy to get the product into production quickly.
By staying as a two-person technical team, Decagon's founders maintained extreme agility. They spent days talking to customers and nights coding, allowing them to iterate rapidly to product-market fit without the overhead of recruiting or managing a team.
Instead of guessing your competitive advantage, ask potential customers which other solutions they've evaluated and why those products didn't work for them. They will explicitly tell you the market gaps and what you need to build to win.
First-time founders often over-intellectualize strategy. Decagon's founder learned from his first startup that a better approach is to talk directly to customers to discover their real problems, rather than creating a grand plan in a vacuum that fails upon market contact.