The rapid expansion of artificial intelligence infrastructure is reshaping the American landscape. New data centers, clean energy facilities, and advanced manufacturing plants are springing up across the country, driven in large part by the AI boom and federal investment policies. But with this construction surge comes an equally large challenge: staying compliant with a dense and overlapping web of federal, state, and local regulations. A new startup, Dili, is hoping to solve that problem by applying AI to the very infrastructure projects that are enabling AI itself.
On Thursday, Dili announced that it had raised $15 million in Series A funding, bringing its total capital raised to $21.7 million. The Series A round was led by Khosla Ventures, with participation from Allianz, Rebel Fund, Darren Bechtel of Brick and Mortar Ventures, and Y Combinator's Garry Tan. The company was previously part of Y Combinator's Summer 2023 batch, and the new funding follows a $6.7 million seed round. The fresh capital will be used to expand Dili's engineering team, scale its go-to-market efforts, and deepen the capabilities of its compliance platform.
The Compliance Bottleneck in the Infrastructure Boom
The United States is in the middle of an infrastructure building spree, triggered by a combination of private-sector AI investments and public programs like the Inflation Reduction Act (IRA) and the Bipartisan Infrastructure Law. These initiatives have unlocked billions of dollars for new construction projects, ranging from massive data centers to solar farms, battery plants, and semiconductor fabrication facilities. However, every dollar of federal funding comes with strings attached. Contractors must navigate a complex matrix of rules that govern wages, apprenticeships, environmental protections, worker safety, and more.
These rules are not merely administrative inconveniences; they carry significant financial penalties for non-compliance. Mistakes can result in millions of dollars in fines, project delays, and even debarment from future federal contracts. For a large data center project, the cost of compliance errors can quickly spiral out of control. The traditional approach to compliance has been manual, labor-intensive, and prone to errors. Teams of specialists spend countless hours reviewing payroll records, apprenticeship logs, environmental impact statements, and safety inspection reports. With fewer trained compliance professionals available and a construction boom straining resources, the bottleneck is becoming critical.
Understanding the Regulatory Maze
Dili focuses on a particular set of rules that apply to construction projects, especially those receiving any form of federal funding. One of the most prominent is the Davis-Bacon Act, which empowers the Department of Labor to set prevailing wages for workers on federally funded projects. Clean energy projects funded through the Inflation Reduction Act are subject to separate prevailing wage and apprenticeship (PWA) rules. In addition, projects must comply with a variety of other regulations depending on the nature and location of the work, including Occupational Safety and Health Administration (OSHA) standards and Environmental Protection Agency (EPA) requirements.
These requirements are often layered on top of one another. A single project might need to comply with federal Davis-Bacon standards, the IRA's PWA provisions, state-specific labor laws, local zoning ordinances, and EPA contamination guidelines. The complexity makes it difficult for even experienced compliance teams to keep up. The penalties for falling short are severe. For example, a misclassification of a laborer's role, a miscalculation of prevailing wages, or a failure to document apprentice hours can lead to back-pay obligations that run into the millions.
How Dili Uses AI to Solve the Problem
Dili's approach is to combine artificial intelligence with a deterministic rule engine. The company uses contemporary AI models at the data layer to ingest unstructured documents and convert them into structured, machine-readable data. This includes everything from internal company records and vendor contracts to ERP data and payroll system information. Once the data is structured, a deterministic system applies the compliance rules to it. Because the rules themselves are complex but static, they can be codified precisely, ensuring that no AI fuzziness leaks into the final compliance output.
The architecture is designed to address a key concern in the compliance space: reliability. While AI models are excellent at extracting information from varied document formats, they are not yet trustworthy enough to make final determinations on regulatory matters. Dili's hybrid approach aims to get the best of both worlds: the natural language understanding of AI with the absolute certainty of rule-based logic.
The CEO and co-founder, Anand Chaturvedi, explained that this system can dramatically accelerate the compliance process. What used to require a full day of manual work can now be completed in minutes. Instead of sampling data to check for compliance, project managers can verify every piece of information as it comes in. That continuous and comprehensive examination is a game changer, especially when non-compliance fines can reach millions of dollars.
Real-World Implementation and Market Adoption
Dili is already showing traction in the market. According to Chaturvedi, the software is being used on approximately 700 projects, ranging from new manufacturing facilities to data centers. This early adoption suggests that the startup is solving a genuine and pressing problem. Interestingly, the company is seeing two distinct usage patterns. About half of the projects use Dili as an in-house software tool, integrated directly into their compliance workflows. The other half outsources the entire compliance process to Dili on a contractor model, letting the company handle everything from data ingestion to reporting.
Dili is comfortable supporting both business models, but Chaturvedi believes the industry will gradually shift toward the software-first approach. As AI tools become more trusted and easier to deploy, more organizations will likely want to bring compliance capabilities in-house rather than relying on third-party professional services. This trend is already visible in other areas of regulatory technology, where automation is increasingly substituting for manual professional services workflows.
The shift toward in-house software is not just about cost savings. It also offers greater control and visibility for project owners, who can monitor compliance in real time and catch issues before they escalate. For a large infrastructure project with hundreds of contractors and subcontractors, the ability to have a single, automated compliance view is immensely valuable.
The Broader Implications for AI and Infrastructure
The rise of Dili is part of a larger story about AI's role in the physical world. The same AI models that require vast data centers are also being used to make the construction and operation of those data centers more efficient and compliant. This creates an interesting feedback loop: AI enables the infrastructure that powers AI. But beyond the symbolism, there are practical benefits. Ensuring that infrastructure projects are built in accordance with labor and environmental laws can help avoid community backlash, legal challenges, and expensive delays. That, in turn, makes the infrastructure buildout faster and more sustainable.
Chaturvedi's forecast for the market is optimistic and forward-looking. He notes that software and AI are likely to erode many professional services workflows, leading more companies to adopt internal compliance platforms. The interesting question, he says, is how the market will evolve and where customer needs will go as AI develops. That evolution will likely involve new types of regulations, more sophisticated data sources, and deeper integration with existing enterprise software.
The infrastructure boom is still in its early stages. With billions of dollars in federal incentives and private investment flowing into construction projects, the demand for efficient and reliable compliance solutions will only grow. Dili has positioned itself at the intersection of two high-growth trends: artificial intelligence and infrastructure development. Its success so far, measured by both funding and project adoption, indicates that investors see the value in this intersection. As AI continues to reshape the economy, the tools needed to govern its physical footprint will become increasingly important.
Source: TechCrunch News