Enterprises tell us an in-house AI analytics build prices north of $1M and 12 to 18 months. A decision guide for enterprise AI leaders.

A customer asks your insurance AI about adding travel cover. The AI answers the question and moves on. But that question contained a purchase signal - the customer just told you what they want to buy next. Upsell signals appear in customer AI conversations every day, across every industry. This article explains what they look like, how they are captured at scale, and what commercial, product, and CX teams can do with them.

McKinsey found a 6x engagement gap between AI power users and typical employees in the same organizations. EY found that 88% of employees use AI daily but only 5% use it in advanced ways. High adoption and low proficiency is the most common state enterprise AI deployments find themselves in. AI Proficiency is the metric that measures the gap: not whether employees are using AI tools, but how effectively those interactions are actually working. This article explains what AI Proficiency measures, how it is calculated from behavioral signals in conversation data, and why it is the leading indicator of whether your AI investment will generate lasting business value.

Fastweb + Vodafone invested in Nebuly through Founders Factory, and they're already our customers. Here's what that says about the problem enterprises are facing with AI agents today.

Only 1 in 26 unhappy customers complain. The rest leave silently. Many of them express their dissatisfaction inside your AI agent conversations first. Competitive mentions, repeated task failures, price objections, and abandonment patterns are all visible in conversation data before they show up in your CRM or renewal pipeline. This article explains what these signals look like, why standard AI metrics miss them, and how to start measuring them at scale.

ICONIQ Capital surveyed approximately 300 software executives building AI products and found one clear pattern: the companies scaling AI successfully are not the ones with the best models. They are the ones with the best measurement infrastructure. Only 52% use automated evaluation. 37% plan to change how they price AI in the next year. And the shift toward outcome-based pricing is accelerating. This article unpacks what the data reveals and what it means for enterprise teams trying to close the gap between AI deployment and measurable ROI.