Eve knew they had product-market fit when customers found a bug that let them bypass invite restrictions. Users onboarded their entire firms months ahead of schedule, demonstrating an urgent, unmet need for the product.
To explore a pivot without alarming existing customers, Eve's founders framed discovery calls as "product roadmap brainstorming." This allowed them to validate new, high-value problems with their best clients without disrupting current operations or revealing their pivot intentions.
Eve discovered the true product-market fit for their old product only when they announced its shutdown. The most passionate customers protested vehemently, revealing the product's actual value and core user base, a high-stakes but effective test.
Eve discovered that plaintiff law firms, while competitive locally, actively share strategies with non-local peers. By delighting early adopters, they organically gained access to this national referral network, turning a fragmented market into a powerful go-to-market channel.
Eve's new legal AI product saw a 40% conversion rate from cold outreach to demo requests, compared to 1% for their old product. This massive quantitative jump was an undeniable signal of a burning market need and strong product-market fit.
Despite having a working product and millions in revenue, the team realized their low average contract value (ACV) of $6K would prevent them from scaling to hundreds of millions. This economic reality forced them to pivot and find a new, higher-value vertical.
In the legal industry, where clients buy human services, Eve's live software demo was a revelation. Showing the product solve a real problem on the spot, rather than just talking about it, blew potential customers' minds and led to a 90% demo-to-pilot conversion rate.
Jay Madheswaran transitioned from VC at Lightspeed back to founder because his conviction in AI's potential was too high to express through investing alone. He felt a compelling need to build directly in the space while he still had the "operational chops."
Eve found Big Law wanted bespoke AI projects with marginal gains. In contrast, plaintiff firms had highly repeatable workflows where AI could drive massive efficiency, perfectly aligning with their contingency-fee business model, making them a far better target market.