The proliferation of AI-driven development tools has dramatically accelerated the creation of innovative software, yet a staggering number of these products still fail to gain commercial traction or meaningful user engagement. This growing disconnect highlights a critical “execution gap” within the software industry, where the speed of innovation outpaces the ability of users to adopt and extract value from new technology. Helsinki-based startup Skene aims to close this gap, having secured €800,000 in pre-seed funding to scale its autonomous AI agents that learn how a product works by reading its source code directly. The round, led by Superhero Capital with participation from NVIDIA executives, empowers Skene to build a future where software becomes its own best advocate.
The Paradox of Innovation without Adoption
The modern software landscape presents a confounding paradox: while AI accelerates product development to unprecedented speeds, commercial success and user adoption lag significantly behind. This disparity creates an “execution gap” where groundbreaking technology remains underutilized, leading to a substantial waste of venture capital and engineering resources. Companies launch feature-rich platforms that, despite their potential, fail to connect with users who struggle to navigate complex workflows and discover core value propositions on their own.
This challenge is not merely about poor marketing but a fundamental breakdown in the user onboarding and value-discovery process. The inability of software to effectively guide its users results in high churn rates and a failure to achieve product-led growth (PLG). Consequently, many promising innovations never reach their intended audience, becoming casualties of a system that prioritizes creation over comprehension and adoption.
Why Traditional Growth Metrics Fall Short
For years, SaaS companies have relied on conventional methods to understand user behavior, primarily analyzing usage metrics and event data. These approaches track clicks, session times, and feature usage, offering a surface-level view of what users are doing within an application. However, this data provides little insight into a software’s true purpose or the intended workflows designed by its developers. It answers the “what” but completely misses the “why.”
This shallow understanding forces businesses to depend on manual-intensive sales and customer success teams to bridge the knowledge gap. These teams act as human interpreters, guiding customers through complex processes that the software itself fails to communicate effectively. This reliance on what Skene founder Teemu Kinos calls “human-shaped sticking plasters” creates significant inefficiencies, inflates customer acquisition costs, and limits the scalability of SaaS growth models.
A Fundamental Shift to Reading Code not Clicks
Skene introduces a radical departure from traditional analytics with its core innovation: autonomous AI agents that directly interpret a product’s source code. Instead of observing user behavior from the outside, Skene’s technology delves into the foundational logic and structure of the software itself. By analyzing the code, the AI gains a deep, native understanding of how the product was designed to function and the specific outcomes it was built to deliver.
This code-level insight enables the AI to generate continuously improving, context-aware guidance directly within the application. These automated interventions steer users toward high-value actions and help them navigate the most effective workflows, significantly boosting activation, adoption, and retention. The result is a system where the software proactively teaches users how to succeed, transforming the product into a self-sufficient growth engine.
Replacing Manual Support with an Autonomous Value Layer
The vision behind Skene is to move beyond temporary fixes and create an “autonomous value layer” for every SaaS product. Founder Teemu Kinos aims to eliminate the industry’s dependency on costly manual support and build a truly scalable, capital-efficient growth model. This approach is designed for modern, AI-native teams that need to grow rapidly without a corresponding increase in human overhead.
This forward-thinking strategy has attracted strong investor confidence. Juha Ruohonen, General Partner at lead investor Superhero Capital, described Skene’s code-analysis approach as a “fundamental breakthrough” capable of reshaping how software companies achieve product-led growth. The backing from key executives at NVIDIA further underscores the technological potential and credibility of Skene’s mission to automate the path to user success.
Fueling Product Led Growth Automation with New Capital
With its €800,000 in new capital, Skene is strategically positioned to accelerate its product development and expand its technological footprint. The funding will be primarily allocated to enhancing the capabilities of its AI agents, enabling them to cover the entire user journey, from initial activation and engagement all the way to revenue expansion. The company, which already has its first agent live with early customers since its founding in 2025, is on an aggressive path to scale its impact.
Demonstrating a firm belief in its value proposition, Skene has implemented a unique outcome-based pricing model. This strategy directly aligns its success with that of its customers, as clients are charged only when their users achieve predefined, meaningful goals within the product. With this model in place, the company is targeting an ambitious goal of reaching €1 million in Annual Recurring Revenue (ARR) this year.
The investment in Skene represented more than just a financial transaction; it was a significant endorsement of a new philosophy for software growth. The funding validated the idea that products could, and should, be intelligent enough to guide their own adoption. This move signaled a pivotal shift away from reactive, human-led support and toward a future of autonomous, code-aware systems that made exceptional user experiences scalable and inherent to the software itself.
