Dili Raises $15 Million to Use AI for Construction and Energy Compliance

Dili has raised $15 million in Series A funding to expand its AI-powered compliance software for infrastructure projects, a category that is growing alongside the U.S. buildout of data centers, manufacturing plants and clean-energy facilities. The company says its tools can help contractors and owners navigate dense federal rules more quickly and with fewer costly mistakes.

The San Francisco-based startup, which previously raised a $6.7 million seed round, has now collected $21.7 million in total funding. The latest round was led by Khosla Ventures and included support from Allianz, Rebel Fund, Brick and Mortar Ventures partner Darren Bechtel and Y Combinator president Garry Tan.

Dili’s pitch is straightforward but timely: if the artificial intelligence boom requires more power, more steel and more concrete, then it also creates a larger need for software that can keep those projects compliant with labor, environmental and funding rules. That need is especially acute when public money is involved, because federal requirements can change the economics of a project if they are not tracked correctly from the start.

Why Dili thinks infrastructure compliance is an AI opportunity

Dili is targeting a problem that rarely gets attention outside construction and project finance circles, but can become a major liability for developers. Large infrastructure jobs often involve many moving parts, including subcontractors, wage records, apprenticeship documentation, safety reporting and environmental filings. If any of those pieces fall out of alignment with the relevant rules, the consequences can be severe.

Chief executive and co-founder Anand Chaturvedi says the company is focused on the compliance burden tied to projects receiving federal support. That includes projects that must follow labor standards such as Davis-Bacon wage rules, which allow the Department of Labor to set prevailing wages on certain jobs. It also includes clean-energy work funded under the Inflation Reduction Act, where prevailing wage and apprenticeship requirements can apply alongside OSHA and EPA obligations depending on the project.

For builders, the challenge is not just understanding the rules but managing them continuously as information flows in from multiple systems. Dili says that is where AI can create value: by extracting details from unstructured documents and organizing them into a format that can be checked against static compliance logic.

Dili’s founders argue that the risk is not theoretical. Chaturvedi says a compliance error on a large project can lead to millions of dollars in penalties, which is why constant review matters more than occasional sampling.

How does Dili use AI without letting the model make the final call?

Dili says it designed its product to avoid relying on a large language model to decide whether a project complies with the rules. Instead, the AI handles document processing in the data layer, while a deterministic rules engine performs the actual compliance checks. In other words, machine learning helps turn messy information into structured inputs, but the final judgment is made by a system that follows fixed logic.

That architecture is meant to address one of the biggest concerns in enterprise AI: reliability. Compliance software cannot afford hallucinations, and a tool used to police wages or apprenticeship records needs to be precise. Dili says the AI is used to read through documents, vendor files, payroll data and enterprise systems, then surface the fields that matter for reporting and verification.

The company says this setup can compress work that once took a full day into a matter of minutes. For teams juggling dozens or hundreds of projects, that difference can affect staffing, deadlines and audit readiness.

What kind of data does the platform review?

The platform is built to pull together information from multiple sources that typically sit in different systems. According to Chaturvedi, that can include internal company files, vendor documents, ERP data and payroll records. The point is not simply to store those documents, but to extract the fields needed to support compliance reporting and project oversight.

• Project contracts and supporting documentation
• Vendor and subcontractor records
• Payroll and labor data
• ERP and back-office system information
• Environmental or safety records where applicable

How big is the market Dili is targeting?

Dili says its software is already in use on about 700 projects, spanning sectors such as manufacturing facilities and data centers. That customer base suggests the startup is riding a wave that extends beyond traditional public works and into the broader infrastructure expansion tied to artificial intelligence, industrial reshoring and energy transition spending.

Data centers in particular have become one of the clearest symbols of the AI buildout. They require power, land, construction labor and long planning horizons, which means they often trigger a web of regulatory obligations. Manufacturing projects can bring similar complications, especially when they are financed with incentives or public programs.

Dili appears to believe those pressures will only intensify. The company’s bet is that infrastructure owners will eventually want the same kind of software discipline that finance, sales and human resources teams already use in other parts of the enterprise.

Who is using Dili’s software model today?

Dili says its customer base is split between companies that use the software internally and organizations that outsource compliance work to Dili as a service. Roughly half of the projects are managed with the platform as an in-house tool, while the other half rely on a contractor-style arrangement where Dili handles the process more directly.

That split is important because it shows the company is serving both software buyers and buyers of managed services. For now, Dili says it can support either structure. Over time, however, Chaturvedi expects the market to move toward software ownership as teams increasingly bring once-outsourced professional workflows inside their own organizations.

Chaturvedi says that as software and AI absorb more professional services tasks, more customers are likely to prefer owning the workflow themselves rather than paying for it as an external service.

That prediction fits a broader industry trend. Across many business functions, companies have been replacing manual labor with software tools that are faster, cheaper and easier to audit. Dili is essentially trying to do that for compliance work in infrastructure, where the stakes are high and the paperwork can be overwhelming.

Why investors are paying attention now

The timing of Dili’s raise suggests investors see compliance as a practical AI category rather than just another generic enterprise pitch. Startups often claim to automate paperwork, but the infrastructure sector presents a specific and expanding need. Federal support for energy and manufacturing projects can create dense compliance obligations, while the data center boom adds more construction volume and more opportunities for mistakes.

Khosla Ventures has a long history of backing companies in technical and industrial markets, and the rest of the round brings together investors with exposure to insurance, construction and startup ecosystems. Allianz’s participation may be particularly notable because it signals interest from a major financial institution in software that can reduce project risk.

For venture investors, compliance software can also be appealing because it sits close to measurable pain. The problem is not abstract. Companies miss deadlines, lose records or misclassify workers, and the result can be financial penalties, delayed payments or audits. If software can reduce that risk, buyers may be willing to pay for it quickly.

What makes the infrastructure boom different from other AI trends?

The infrastructure boom is different because it is being driven by physical buildout, not digital adoption alone. AI may be creating demand for models and applications, but it is also creating demand for power plants, transmission upgrades, data centers, factories and complex permitting processes. Those projects are document-heavy, rule-heavy and expensive to get wrong.

That makes them a strong fit for automation tools that can handle repeatable compliance tasks. Unlike creative AI applications, infrastructure compliance software does not need to sound clever. It needs to be accurate, auditable and fast.

What rules is Dili trying to manage?

Dili is built around a broad set of overlapping requirements that can vary by project type, funding source and jurisdiction. Some rules are tied to labor standards, while others relate to safety or environmental review. For federally supported work, the complexity increases because multiple agencies and programs can impose their own obligations.

1. Prevailing wage requirements for certain federally funded construction jobs
2. Apprenticeship documentation tied to clean-energy incentives
3. Occupational safety compliance under OSHA rules
4. Environmental obligations under EPA-related standards where applicable
5. Project-specific reporting linked to grants, loans or tax credits

That mix is exactly what makes the category hard to serve with a generic software product. Dili’s founders are betting that a tool built specifically for infrastructure compliance can create more value than broad document automation systems.

How the company plans to grow

Dili’s near-term growth strategy appears to rest on two forces: expanding the number of projects it serves and convincing more customers to buy software instead of managed compliance services. Those two goals are related. As customers gain confidence in the platform, they may bring the work in-house and use Dili as a system of record rather than a service provider.

That path could also help the company deepen its product footprint. A compliance platform that begins with document review can potentially expand into workflow management, audit preparation, reporting, and integrations with payroll or ERP systems. The more deeply embedded the software becomes, the harder it is to replace.

Still, the company will need to prove it can remain both accurate and adaptable. Infrastructure rules are not as fluid as many consumer AI use cases, but they can still change with policy updates, new funding programs or shifting enforcement priorities. A product that touches regulated work must stay current in order to remain useful.

Timeline: Dili’s funding and growth so far

What this says about the next wave of enterprise AI

Dili’s raise is another sign that the most durable AI businesses may be the ones solving narrow, expensive, operational problems rather than chasing broad consumer attention. In this case, the problem is compliance on regulated infrastructure projects, where every document matters and every misstep can have financial consequences.

The company also illustrates a broader evolution in AI adoption. Rather than asking whether a model can replace an expert entirely, many startups are now asking where AI can reliably handle the hardest and most repetitive parts of expert workflows. Dili’s answer is to let AI read, organize and extract, then let deterministic software decide.

That approach may prove especially attractive in sectors where accountability matters as much as speed. Construction owners, energy developers and industrial builders are unlikely to trust a black box with their compliance obligations. But they may embrace a tool that makes the process faster while preserving a clear rule-based decision trail.

If Dili can continue expanding its customer base, it may become one of the clearer examples of AI moving from hype into the unglamorous back office of the physical economy. In a market defined by data centers, factories and power projects, that may be exactly where the money is.