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Dili automates prevailing wage compliance that Big Four consultants have billed manually — across $1B+ in projects.
ENTRY ANGLES
Build domain-specific compliance automation for a single high-value regulatory category (e.g. environmental impact compliance for offshore wind) · Expand the compliance automation model to international infrastructure markets where similar manual audit processes exist · Build the post-audit investigation and reporting layer on top of compliance platforms
VERTICALS
CAPABILITIES
Regulatory document ingestion and parsing, AI-driven compliance logic application, Project portfolio management, Domain-specific regulatory data corpus
The Big Four get paid to read federal wage schedules and match them against contractor payrolls, project by project, line by line. It is expensive not because it requires expertise, but because it requires scale — and nobody automated it until IIJA made the market large enough to matter.
Every dollar of federal infrastructure funding in the United States arrives with a compliance obligation attached. Projects receiving IIJA and Inflation Reduction Act money must verify prevailing wage rates, apprenticeship requirements, and Buy America provisions — or risk losing grants, triggering audits, and clawing back tax credits already deployed. The traditional answer has been to bring in Deloitte, KPMG, or Ernst & Young and bill by the hour for the duration of the project.
Dili was founded by three former Coinbase executives who recognized the pattern: high-stakes, document-heavy compliance that looks manual not because it requires expert judgment, but because nobody has automated it yet.
The platform ingests project documentation and applies AI to verify compliance against current prevailing wage schedules, track apprenticeship ratios in real time, and flag discrepancies before they become enforcement actions. For investment firms managing portfolios of infrastructure projects — renewable energy, data centers, transportation — Dili functions as a continuous compliance layer rather than a periodic audit event.
The company has processed compliance across more than 700 projects and protected over $1 billion in funding and tax credits from clawback risk. The $15 million Series A was led by Khosla Ventures, with Allianz joining as a strategic investor alongside YC CEO Garry Tan, Rebel Fund, and Darren Bechtel of Brick and Mortar Ventures.
The timing is not coincidental. The Infrastructure Investment and Jobs Act committed $1.2 trillion over ten years. The Inflation Reduction Act added hundreds of billions more in clean energy and manufacturing tax credits, with prevailing wage and apprenticeship compliance as the explicit condition for the enhanced credit rates. This compliance obligation did not exist at scale three years ago and now applies to essentially every major infrastructure project in the United States.
What is being automated here is not peripheral paperwork. Prevailing wage compliance requires tracking wage rates that change quarterly by county, occupation, and project type — the Department of Labor publishes over 12,000 wage determinations. An apprenticeship compliance audit for a single wind farm involves verifying thousands of individual employment records against union agreements across multiple contractors and subcontractors. The audit surface per project is large, and the portfolio of projects a single investment firm manages runs into the dozens or hundreds.
The Big Four have a profitable business in this category because the work is tedious at scale, not because it requires expert judgment. Dili's positioning as a replacement for outsourced consulting, rather than a supplement to it, is the correctly aggressive framing. Allianz as a strategic investor is telling: large institutional investors in infrastructure projects have a direct financial stake in getting compliance right before the auditors arrive, and they want a technology solution they can standardize across their portfolio rather than bespoke consulting engagements on each deal.
Dili's current focus is prevailing wage and apprenticeship compliance — the obligations that attach to federally funded projects. Its stated expansion is into broader audit and waste detection, work traditionally outsourced to the Big Four across multiple lenses: ESG reporting, cost certification, project milestones, procurement fraud.
Each of those audit lenses is the same underlying AI problem: ingest regulatory text, apply it to project documentation, produce a compliance checklist, flag exceptions. Dili has validated the model against one of the more complex variants. The transfer to adjacent domains is largely a data and regulatory mapping problem, not an AI capability problem.
The specific entry angle for a competing builder: prevailing wage is a US-specific compliance type with a well-defined regulatory source. Construction permits, environmental impact assessments, zoning variance compliance, and procurement regulations are the same class of problem in different jurisdictions. A builder who picks a single high-value adjacent domain — say, environmental impact compliance for offshore wind development, where the regulatory surface is complex and the stakes per project are nine figures — and goes deep on the domain-specific context model would be building against the same structural dynamic Dili validated. The AI infrastructure exists; the moat is the regulatory data corpus and the customer relationships.