Empromptu (200 万美元种子轮前融资)：具备自管理上下文功能的 AI 应用构建器
1 分•作者: anaempromptu•21 天前
嗨，HN 社区，
我是 Shanea，Empromptu 的创始人。我们刚刚从 Precursor Ventures 融资 200 万美元，旨在解决我一直遇到的一个问题：大多数 AI 功能最终都无法投入生产。
问题在于：现在你可以在几小时内原型设计 AI 功能，但要让它们达到生产就绪状态仍然非常困难。上下文窗口会膨胀到超过 100 份文档。准确性会漂移。与现有代码库的集成也很混乱。大多数团队要么重写他们的平台，要么聘请专业的 AI 工程师。
我们构建了什么：一个 AI 应用构建器，可以生成全栈功能（前端、后端、模型、可观测性），并将它们集成到现有的 SaaS 平台中。新功能是自管理上下文——一种图-RAG 架构，可以处理 100GB 以上的文件，通过多级摘要保持准确性，并旨在随着时间的推移从使用中不断改进。
目前状态：
* 2,000+ 家企业正在使用
* 98% 的生产准确率
* 本地部署或云部署
* 一位医疗保健 SaaS 创始人使用它添加了一个 AI 驱动的 CRM 功能，而无需扩大团队
很乐意回答有关架构的技术问题，特别是关于我们如何大规模处理上下文管理。也很好奇其他创始人遇到过哪些将 AI 功能投入生产的障碍。
试用：empromptu.ai
查看原文
Hey HN,<p>I'm Shanea, founder of Empromptu. We just raised $2M from Precursor Ventures to solve a problem I kept hitting: most AI features never make it to production.<p>The problem: You can prototype AI features in hours now, but getting them production-ready is still brutal. Context windows explode past 100+ documents. Accuracy drifts. Integration with existing codebases is messy. Most teams either rewrite their platform or hire specialized AI engineers.<p>What we built: An AI application builder that generates full-stack features (frontend, backend, models, observability) and integrates them into existing SaaS platforms. The new piece is Self-Managing Context - a graph-RAG architecture that handles 100GB+ worth of files, maintains accuracy through multi-level summarization and is designed to improve from usage over time.<p>Current state:
2,000+ businesses using it
98% production accuracy
On-prem or cloud deployment
One healthcare SaaS founder used it to add an AI-powered CRM feature without expanding their team<p>Happy to answer technical questions about the architecture, especially around how we're handling context management at scale. Also curious what blockers other founders have hit moving AI features to production.<p>Try it: empromptu.ai