Beyond the Pilot: Can Self-Managing AI Fix Enterprise Adoption Woes?

SAN FRANCISCO, CA – December 09, 2025 – For business leaders and CTOs, the promise of artificial intelligence has been shadowed by a frustrating reality: a staggering 95% of AI pilots never reach full production. This statistic, highlighted in recent MIT research, points not to a failure of AI technology itself, but to the immense operational friction involved. Projects crumble under the weight of data drift, integration complexity, and the relentless need for maintenance and retraining. It is this chasm between prototype and profit that San Francisco-based Empromptu AI aims to bridge, armed with a fresh $2 million in pre-seed funding and a bold new technology.

The company today announced its oversubscribed funding round to launch what it calls a Self-Managing Context Engine. The premise is a radical departure from the current MLOps paradigm, which requires constant human oversight. Empromptu proposes an infrastructure where AI features can manage, train, and improve themselves directly within a production environment. This vision of autonomous software recently attracted lead investor Precursor Ventures, along with a diverse syndicate including Rogue Women VC, Zeal Capital, and LaunchDarkly co-founder Edith Harbaugh.

“SaaS apps shouldn’t need a rewrite to become intelligent,” said Shanea Leven, Founder and CEO of Empromptu, in the announcement. “They should be able to modernize in place with no glue code or guesswork needed—just self-improving logic that works with your own custom data models and your context.” Leven's statement cuts to the core of the enterprise dilemma: how to infuse legacy systems with intelligence without embarking on a costly, high-risk overhaul.

Under the Hood of Autonomous Operations

Empromptu's solution hinges on three interconnected innovations: Infinite Memory, an Adaptive Context Engine, and Custom Data Models. While the technical specifics remain proprietary, the concepts address well-known AI failure points. The system is designed to create custom AI models using a company's unique business data, moving beyond generic, one-size-fits-all solutions.

The Adaptive Context Engine and Infinite Memory features are engineered to tackle the twin demons of AI in production: model degradation and context loss. In traditional systems, an AI model's performance decays as real-world data deviates from its original training set—a phenomenon known as data drift. This necessitates a costly cycle of monitoring, re-labeling, and retraining. Empromptu claims its engine can detect and correct this accuracy drift automatically, ensuring the AI remains relevant and effective over time. The company reports production accuracy up to 98 percent, a figure that, while impressive, awaits independent validation common for early-stage technologies.

By embedding evaluation, optimization, and observability directly into each AI application, the platform promises to transform existing codebases into self-learning systems. This could dramatically shorten deployment timelines from months to days, a compelling proposition for businesses eager to see a return on their AI investments. With over 2,000 businesses reportedly already integrating the technology across sectors from healthcare to cybersecurity, the market appears hungry for a more pragmatic path to AI implementation.

A Bridge to AGI or a Smarter SaaS?

Beyond the immediate business benefits, Empromptu is making a far more ambitious claim: that its technology represents a foundational step toward Artificial General Intelligence (AGI). The company's vision, as detailed by Leven, is an AI that reflects human cognition—one that can “refine context, synthesize across it, focus deeply, and imagine freely.”

This is where the conversation shifts from practical business solutions to the long-term trajectory of intelligent systems. The path to AGI is a subject of intense debate, with many researchers cautioning that true general intelligence requires more than just sophisticated learning algorithms. However, Empromptu’s focus on systems that understand their own limits and learn what to focus on resonates with a key school of thought in AI development.

Investors seem to be buying into this dual-track potential. “The next generation of intelligence won’t come from bigger models, it will come from systems that know when to narrow in and when to zoom out,” noted Charles Hudson, Managing Partner at Precursor Ventures. “That’s how we move from static prompts to software that actually learns.” Hudson’s perspective frames Empromptu not as a competitor in the large-model arms race, but as a crucial infrastructure player enabling software to become genuinely adaptive.

Whether self-managing context is a true milestone on the road to AGI or primarily a powerful new tool for building smarter, more resilient enterprise software is a question that will be answered over time. For now, it provides a compelling narrative that satisfies both the pragmatic CTO and the forward-looking venture capitalist.

The Business of Building and Backing AI

The story of Empromptu is also a story of its founder and backers. Shanea Leven is a second-time entrepreneur, having previously founded and led CodeSee, a developer tool for code visibility. Her experience at tech giants like Google, Docker, and Cloudflare provides a deep well of enterprise product knowledge. This background lends credibility to Empromptu's focus on solving the unglamorous but critical 'last mile' problems of AI deployment.

The investor syndicate is equally noteworthy. The participation of firms like Rogue Women VC, which focuses on women-led B2B software companies, highlights a growing investment thesis around diverse founders tackling complex technical challenges. This backing signals confidence not only in the technology but also in Leven’s leadership and vision to navigate the competitive AI infrastructure market.

For the thousands of SaaS companies watching from the sidelines, Empromptu's model presents a paradigm shift. The dominant strategy has been either to build an AI team from scratch or to rely on API calls to massive, generalized models. Empromptu offers a third way: augmenting existing platforms with self-sufficient AI that is deeply integrated with proprietary business context. If the technology delivers on its promise, it could democratize access to sophisticated AI capabilities, allowing established companies to innovate and compete without betting the farm on a complete architectural rewrite. The focus would shift from simply using AI to embedding a capacity for continuous learning directly into the core of the business.