Users Don't Churn Because They Dislike Your Product – They Never Learned to Use It

01 /The Concept

TruvaAI is an "AI concierge" for cloud services and apps – helping users work with them more intuitively and effectively.

The startup's target customer is the developer of cloud services and applications, who can embed TruvaAI to improve new user activation and long-term retention.

It's no secret that most user churn isn't caused by product-market fit failures. Users leave because they never really started – they didn't figure out what the product could do, couldn't find how to complete their task, or found the learning curve too steep.

With TruvaAI embedded, users no longer need to dig through menus or read documentation. They can ask the built-in chat "how do I do this?" – and the AI assistant returns a concise answer with a direct link to where they need to go.

What's clever is that TruvaAI doesn't just help users start things – it helps them finish. After a user completes an action – say, creating a new customer record – the AI nudges them toward the logical next step: adding products this customer might be interested in, or generating a proposal for them on the spot.

Beyond reactive guidance, TruvaAI proactively surfaces insights based on what the user is working on. For example, it might show a client activity chart and suggest actions to re-engage users whose activity has dropped recently.

The goal is to accompany the user across their entire journey: automating routine tasks, delivering relevant analytics, and pulling them deeper into active engagement with the service.

The founders went through Y Combinator in summer 2022, collecting the standard $500K. TruvaAI has now raised $4 million in seed funding.

02 /Why It Matters

Some services are already embedding AI assistants – but in most cases, these assistants function as a "smart help center" that answers user questions in plain language. Reactive, not proactive.

TruvaAI's key distinction is that its assistant doesn't just wait to be asked. It analyzes user behavior patterns and at the right moment suggests actions – or even executes them on the user's behalf.

A startup that went down a similar path – and was [covered previously](/review/skoro-on-pojavitsja-na-kazhdom-sajte-v-internete) – is OpenCopilot, which also built an embeddable AI assistant for cloud services. Since then, however, OpenCopilot has pivoted toward AI agents for technical support teams, moving away from the generative interface concept.

The original idea behind OpenCopilot was sharp: change how users interact with cloud services entirely – replacing menu navigation and button-clicking with natural language commands.

WalkMe is heading in a similar direction. It originally built a platform for embedding on-screen arrows and popups into services – contextual guidance at every step. Now it's pushing developers to replace those guides with AI chatbots, turning the interaction model from "click here" to "just tell it what you want."

But a chatbot may not be the ideal solution.

That's the argument made by Zylon, [covered here](/review/vzleti-na-kryljah-interfejsa). Its founders believe chat gives users too much freedom – they have to learn how to phrase requests correctly before the AI does what they actually want. Zylon's approach: keep the familiar button-based interface, but make it dynamically adapt to the user's typical patterns and current task.

Ultimately, whether it's buttons or chat isn't the real question. What matters is the underlying trend that Replit founder Amjad Masad recently described as "generative UI."

And generative UI isn't a chat box. Formulating the right AI prompts is a skill – one users have to learn. And staring at a blank input field, users can't see the hidden capabilities of the service they're not asking about.

Masad's view: future interfaces should remain visual, but dynamically adapt to whatever task the user is currently performing – showing exactly what's relevant and hiding everything else. He calls these "Just-in-time UI" interfaces. What's "just in time" at any given moment? That's exactly what the embedded AI should decide.

03 /Opportunities

WalkMe – which started by bolting guidance arrows and popups onto standard interfaces – became a billion-dollar company and went public. Its valuation has come down since, alongside the broader decline in cloud software multiples.

But the problem WalkMe set out to solve hasn't gone anywhere. Product developers still spend enormous energy on onboarding and retention.

The core issue remains: users don't want to figure out how an interface works. So they don't start using the service, or they don't use all its capabilities, and they churn out. The more features a product has, the higher the barrier to entry – and the higher the dropout rate.

A number of startups are attacking this problem. Appcues, [covered here](/review/chto-ja-tut-mogu-poleznogo-dlja-sebja-sdelat), raised $52.8 million on a platform that automatically segments users and delivers tailored onboarding flows by segment. More investment followed after that review.

The opportunity now is the emergence of a new generation of billion-dollar companies – ones that solve onboarding and retention using the generative interface concept. Where AI plays a proactive role: not just adapting the UI to the user's current task, but nudging them toward next steps based on real-time analysis.

The substantive opportunity splits at product level. For teams building their own services, the real design challenge is mapping user intent in real time – detecting which task a user is trying to accomplish before they ask, and surfacing the right next step before they get lost. That's not a chatbot problem; it's a behavioral inference problem, and the teams that solve it will build something meaningfully stickier than anything with a blank text field at the bottom.

For platform builders, the adjacent play is infrastructure: tools that let other developers add proactive, adaptive interfaces to their products without building the inference layer themselves. A vertical-first entry – starting with one service category where user drop-off patterns are well understood – is almost certainly more defensible than going broad from day one.