Dili Raises $15M to Scale AI Compliance for Infrastructure Projects
Dili secures $15M Series A to expand AI compliance tools for construction and federally funded infrastructure, helping builders navigate complex wage and safety rules.
Dili, a startup that blends machine learning with deterministic rule engines, announced a $15 million Series A financing to accelerate AI compliance tools for large infrastructure and construction projects. The funding, which follows a $6.7 million seed round, brings the company’s total capital raised to $21.7 million as it targets the surge in data center, energy, and manufacturing builds. The announcement positions Dili squarely in the growing market for automated compliance solutions that can handle the tangled regulatory requirements of federally supported projects.
Funding and investor lineup
The Series A was led by Khosla Ventures and included participation from Allianz, Rebel Fund, Brick & Mortar Ventures’ Darren Bechtel, and Y Combinator partner Garry Tan. Dili previously emerged from Y Combinator’s Summer 2023 cohort and has used its early capital to develop product integrations and customer deployments. Company executives say the new financing will fund engineering hires, expanded regulatory coverage, and additional product rollouts tailored to infrastructure contractors and owners.
The compliance problem in construction
Construction projects that receive federal support are subject to a mesh of wage, apprenticeship, safety and environmental rules that vary by program and region. Prevailing wage laws, such as rules that permit the Department of Labor to set wage floors on certain contracts, sit alongside separate prevailing wage and apprenticeship requirements tied to clean energy funding. Layered OSHA and EPA obligations add further complexity. For contractors and project sponsors, mistakes in applying these rules can trigger audits, stop-work orders and multi-million-dollar fines.
Dili’s technical approach
Dili combines contemporary AI models with a deterministic compliance engine to translate unstructured documents and systems data into rule-evaluable records. The company uses machine learning primarily in the data layer to ingest and extract information from contracts, payroll feeds, vendor documents and ERP systems. Once structured, a deterministic system applies static legal and regulatory rules to produce compliance outputs and reports, a design Dili says reduces the risk of probabilistic errors in final decisions.
Performance claims and use cases
The startup reports its platform is active on roughly 700 projects spanning data centers, manufacturing facilities and other infrastructure builds. Dili says the software can compress tasks that historically took a full day of manual review into minutes, enabling continuous monitoring rather than random sampling. Customers are using the technology both as an in-house compliance tool and via an outsourced contractor model, with the company noting an approximately even split between the two approaches so far.
Business model and market shift
Dili offers both software licenses and managed compliance services, allowing general contractors, owners and specialty builders to choose the workflow that best fits their capacity. Company leadership predicts a move toward more in-house software adoption as organizations grow comfortable with automated data extraction and reporting. The shift, they argue, will reduce reliance on traditional professional services while creating new software-driven workflows for compliance and reporting.
Regulatory trust and reliability concerns
Because regulatory outcomes carry financial and legal consequences, reliability is central to customer adoption. Dili emphasizes that its architecture confines probabilistic AI to data extraction and relies on deterministic logic for rule application and final outputs. That separation is intended to make audit trails clearer and to ease regulatory scrutiny, but adoption still hinges on third-party verification, internal controls and the willingness of regulators to accept machine-assisted reporting as part of official submissions.
Implications for the infrastructure boom
The timing of Dili’s funding coincides with rapid expansion in data centers, renewable energy installations and other capital-intensive projects that often involve complex funding and compliance regimes. As the scale of construction grows, so does the volume of documentation and the likelihood of overlapping legal requirements. Automated compliance platforms aim to reduce administrative burden, lower audit risk and speed project closeouts, which could influence project economics in sectors where margins and timelines matter.
Dili’s CEO has framed the opportunity as one where software can read across an organization’s entire document corpus—vendor agreements, payroll registers, and ERP records—to surface the precise datasets needed for regulatory reporting. That capability addresses a practical gap for owners and contractors who must reconcile disparate data sources to demonstrate adherence to wage, apprenticeship and safety rules.
Looking ahead, Dili will need to expand its legal rule library, deepen integrations with common construction and payroll systems, and build trust with regulators and large enterprise customers. The company’s new capital will support those efforts as demand for automated compliance grows alongside the infrastructure wave.
