Empromptu AI, a San Francisco, CA-based company enabling enterprises to move from static SaaS to self-improving self-managing AI-native applications.
Empromptu was funded $2 million pre-seed funding round. The investment, led by Precursor Ventures, with participation from Alumni Ventures, Founders Edge, Rogue Women VC, South Loop, and Zeal Capital in addition to angel investor Edith Harbaugh, Co-founder of LaunchDarkly.
The company intends to use the funds to accelerate the development of its groundbreaking Self-Managing Context Engine, a technology the company touts as the crucial missing link in making Artificial General Intelligence (AGI) a reality for businesses.
The funding comes at a critical time for corporate digital transformation. Despite a surge in AI enthusiasm, a recent MIT study revealed that a staggering 95% of AI pilot projects never make it to production due due to persistent issues with reliability, accuracy, and maintenance. Empromptu aims to eliminate this “AI reliability crisis” by empowering enterprises to build full-stack, production-ready AI applications that are capable of managing, training, and improving themselves without continuous human intervention.
Empromptu was founded by former CodeSee CEO Shanea Leven and AI researcher Dr. Sean Robinson, who together identified a critical market gap. While tools have emerged to make AI-powered prototyping easy—often termed “vibe coding”—they frequently generate fragile applications that break when deployed with real-world users and complex, enterprise-scale data.
“SaaS apps shouldn’t need a rewrite to become intelligent,” said Shanea Leven, Founder and CEO of Empromptu. “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. We are building the infrastructure that lets AI run itself safely, predictably, and profitably. This is the missing piece between today’s scripted prompts and tomorrow’s autonomous software.”
The Empromptu platform allows non-technical business teams and existing developers to describe their application needs in natural language to an AI chatbot. The platform then generates the complete, full-stack application—including the front-end, back-end, and embedded AI logic—in a production-ready format, such as HTML or JavaScript.
The company’s key differentiator is its proprietary AI optimization technology, which it claims delivers an industry-defying accuracy rate of up to 98%, a significant leap compared to the 60-70% average accuracy that plagues many competing AI builders.
This reliability is achieved through innovations like Prompt Families—specialized prompts automatically selected for granular tasks—and the newly funded Self-Managing Context Engine, which includes:
- Infinite Memory: The ability to handle vast amounts of data (250+ documents) without losing context, a common failure point for traditional large language model (LLM) applications.
- Adaptive Context Engine: An intelligent system that allows the AI to automatically refine context, synthesize across data, and focus deeply on specific user inputs, significantly reducing the problem of “hallucinations.”
- Custom Data Models: Tools to upload and structure a company’s unique operational context, ensuring the AI is tailored to the business’s specific domain knowledge, from legaltech to healthcare.
“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,” commented Charles Hudson, Managing Partner and Founder of Precursor Ventures. “Empromptu AI is innovating in a way that is actually useful to businesses immediately. Every company will eventually have an AI, but Empromptu will be the standard that helps businesses get there first.”
By automating the complex processes of prompt engineering, accuracy debugging, and ML infrastructure management, Empromptu democratizes AI development. The platform is designed for B2B SaaS companies and large enterprises under pressure to implement an AI strategy without expanding their highly specialized ML engineering teams. Over 2,000 businesses across sectors like cybersecurity, e-commerce, and healthcare are already leveraging the platform to deploy AI-native features in days, not months. The company’s focus on production-readiness and reliability is a direct challenge to the status quo, promising to redefine how enterprises approach their AI future.
By: K. Tagura
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