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TechCrunch AI · 2026/7/30 13:00:00

Dili raises $21.7M to bring AI compliance to the infrastructure boom
AI 中文解读
Dili这家AI初创公司刚拿到2170万美元的融资,瞄准的是美国基础设施建设大潮中最让人头疼的环节——合规审查。
简单来说,现在美国大量新建数据中心、清洁能源项目,都涉及到联邦资金,而建这些项目必须遵守一堆复杂又重叠的规定,比如工人工资标准、学徒比例、环保要求等等。稍微弄错一点,就可能面临数百万美元的罚款。以前这些合规审查全靠人工,检查一份文件就要花一整天。Dili的做法是用AI先快速把各种杂乱的文件(合同、工资单、供应商资料)自动整理成结构化数据,然后再用一套固定的规则系统去核对,整个过程从一天缩短到几分钟,而且避免了AI可能出现的“胡编乱造”。
目前这个系统已经在约700个项目中投入使用,涵盖制造厂和数据中心。对于普通人来说,这项技术意味着未来我们身边的基建项目——比如新的5G基站、清洁能源电厂——能更快通过审批、减少因违规造成的工期延误和成本超支,最终让基础设施的更新换代更高效、更省钱。
We already know that making AI work will mean bringing a lot of new data centers and power infrastructure online. But all those infrastructure projects may also need a bit of help from AI.
On Thursday, a new AI compliance company called Dili that squarely targets the new crop of U.S. infrastructure projects, said it had raised $15 million in Series A funding. The round comes on the heels of a $6.7 million seed round, bringing the company’s total capital raised to $21.7 million.
The Series A was led by Khosla Ventures, with participation from Allianz, Rebel Fund, Brick and Mortar Ventures’ Darren Bechtel, and Y Combinator’s Garry Tan. Dili was previously part of Y Combinator’s Summer 2023 batch.
“AI for compliance” is already a common pitch among startups, but Dili focuses on the unique tangle of rules concerning construction projects, particularly those getting some kind of federal funding.
Asked for an example, Dili’s co-founder and CEO Anand Chaturvedi pointed to Davis-Bacon rules, which allow the Department of Labor to set prevailing wages for certain projects. A separate set of prevailing wage and apprenticeship rules (or PWA rules) apply to clean energy projects funded under the IRA, with various other OSHA or EPA rules in effect depending on the nature of the work.
It’s a complex set of overlapping requirements, and even small mistakes can be costly. “Non-compliance can result in millions of dollars of fines for those projects,” explains Chaturvedi. “So it’s really powerful to be able to check all the information as it comes in, instead of just sampling data.”
Given those high stakes, reliability is a central concern, but Chaturvedi believes Dili’s architecture will prevent any LLM-based fuzziness from sneaking into the final product. Contemporary AI models are only used in the company’s data layer, the engine for taking unstructured documents and translating them into structured data. From there, a deterministic system sorts the data according to the complex-but-static compliance rules. When it works right, a task that used to take a full day’s work can now be dispatched in a matter of minutes.
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“Imagine being able to read across the entire context of a company’s internal documents, all of their vendors’ documents, all of their ERP information, all of their payroll systems information, and then draw out the data that you need specifically for, you know, reporting or compliance,” Chaturvedi explained.
Dili is already making that system work in practice. Chaturvedi says the software is already being used at “about 700 projects,” ranging from manufacturing facilities to data centers.
Notably, Chaturvedi says roughly half the projects are using Dili as an in-house software tool, while the other half outsource the entire compliance process to the company on a contractor model. Dili is able to handle both types of contract, although Chaturvedi anticipates the industry will shift more toward the software model in the years to come.
“Software and AI are going to start eating a lot of those professional services workflows, so I think more and more people will start to bring those in-house,” Chaturvedi says. “The interesting thing will be how the market itself evolves and where the customer needs go as AI develops.”
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