在概率模型之上构建确定性系统
1 分•作者: rchaudhary26•6 个月前
过去十年，我在公开市场投入了约 10 亿美元的资金。令我印象深刻的是：令人惊讶的是，大量核心市场基础设施仍然依赖人工操作。
关键工作流程始于混乱的原始信息，这些信息在可用之前，需要被解读、清理和核对。 甚至有整个团队专门负责弥合这一差距。
一小群工程师和领域专家开始构建 Auxage，看看是否可以用不同的架构来解决这个问题。
到目前为止，我们已经在标普 500 指数中生成了与人类分析师水平相当的输出，准确率约为 99%，已经超越了 Claude 等工具以及一些大型现有企业在该工作流程上的表现。
有趣的是，最难的部分不是模型能力，而是数据架构和系统设计。
很好奇这里是否有人从事过将混乱的真实世界信息转化为可靠、高精度基础设施的系统。
如果对您有帮助，我很乐意分享更多关于我们在 Auxage 正在构建的内容。
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I spent the last decade in public markets deploying ~$1B in capital. One thing that always struck me: a surprising amount of core market infrastructure still runs on manual work.<p>Critical workflows start from messy source information that has to be interpreted, cleaned, and reconciled before it becomes usable. Entire teams exist just to bridge that gap.<p>A small group of engineers and domain practitioners started building Auxage to see if this could be solved with a different architecture.<p>So far we’re generating human-analyst–grade outputs across the S&P 500 with ~99% accuracy, already outperforming tools like Claude and several large incumbents on this workflow.<p>Interestingly, the hard part hasn’t been model capability — it’s data architecture and system design.<p>Curious if others here have worked on systems that turn messy real-world information into reliable, high-accuracy infrastructure.
If helpful, happy to share more about what we're building at Auxage.