Dili's $21.7M bet: use AI to parse the paperwork, then let deterministic code make the compliance call
AI compliance startup Dili has raised $21.7 million to automate the dense regulatory tangle around federally funded construction projects, from Davis-Bacon prevailing wage rules to IRA clean-energy requirements. Already deployed across roughly 700 projects, the company's architecture confines LLMs to extracting structured data from unstructured documents while a deterministic engine applies the compliance rules, a design choice that sidesteps the hallucination risk that plagues all-AI compliance tools.
Dili's $21.7M bet: let AI read the paperwork, let code make the call
Dili, an AI compliance company that has raised $21.7 million, most recently a $15M Series A led by Khosla Ventures, has built its system around a deliberate split. Large language models handle the fuzzy work of reading messy regulatory documents. Deterministic code makes the compliance judgment calls.
For anyone building AI tools for regulated industries, that separation is a trust design pattern worth studying: let the model perceive, let code decide.
The problem Dili targets is specific and expensive. Federally funded construction projects in the United States must comply with overlapping layers of regulation. Davis-Bacon rules allow the Department of Labor to set prevailing wages for certain projects 1. Clean energy projects funded under the Inflation Reduction Act face a separate set of prevailing wage and apprenticeship requirements
1. OSHA and EPA rules add further layers depending on the work involved
1. Anand Chaturvedi, Dili's co-founder and CEO, says violations on these projects can trigger fines in the millions
1.
References
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ProvenBrief (2026). "Dili's $21.7M bet: use AI to parse the paperwork, then let deterministic code make the compliance call." ProvenBrief. https://provenbrief.com/story/dili-s-21-7m-bet-use-ai-to-parse-the-paperwork-then-let-deterministic-code-make-
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