In early 2023, Mac Liu noticed a pattern. Former colleagues, early-stage contractors, even billion-dollar enterprises were all hitting the same wall: federal proposal development was broken. The tools hadn’t changed in a decade, timelines were accelerating, and the pressure to do more with less was mounting across the board.
Within months, Liu had launched Vultron, a San Francisco-based startup focused on automating one of the most cumbersome and high-stakes processes in government contracting. This week, the company raised a $17 million Series A led by Greycroft, with participation from Craft Ventures, Long Journey Ventures, and South Park Commons, bringing its total funding to $22 million.
The market is paying attention. “Three shifts converged to make right now the right moment for Vultron,” Liu told AIM. “Agencies are issuing more solicitations with faster timelines. Contractors can’t keep up using manual, decade-old processes. And general-purpose AI models are now powerful enough to serve as the foundation for domain-specific AI systems.”
Modernizing Bureaucratic Workflows with AI-Native Infra
Federal proposal development is bureaucratic and costly. The process often consumes 3% to 5% of the value of the contract, or over 10% for complex pursuits. Companies must juggle compliance requirements, technical writing, internal approvals, and formatting demands across compressed timelines. Conventional tools, from SharePoint to word processors, leave much of the work error-prone.
Vultron offers an alternative: a platform that embeds domain-specific AI across the entire proposal lifecycle. The system handles everything from automatic compliance checks to drafting red team revisions, scoring submissions, and synthesizing subject matter expertise. Liu describes it as a new “intelligence and knowledge layer” built to work across the systems contractors already use. “We designed Vultron to fit into existing tech stacks without requiring process overhauls,” he said, pointing to built-in integrations with Word and SharePoint.
The company claims significant impact. One Fortune 500 customer reportedly saves over 20 hours per user each week. Internal data shows a 92% faster time to pink team drafts and a 54% reduction in development costs. Customers like Bright Apps and FCR Consulting say Vultron helped them deliver proposals five times faster and win multi-million dollar deals.
According to Liu, Vultron’s models are trained on licensed, winning proposal data, rather than generic internet content. “That gives us unmatched output quality,” he said. “Our clients are turning weeks of effort into days.”
A Competitive Edge in a Growing Market
The company’s timing coincides with shifts in the federal contracting landscape. As agencies push digital modernization and AI-readiness, they are increasing both the volume and velocity of solicitations. Contractors are expected to move faster and demonstrate value more clearly, all without expanding staff.
“Contractors are under pressure to do more with less,” Liu told AIM. “Winning used to mean adding headcount. Now, it means modernizing the system.”
While large players like Anduril and Shield AI have shown that private companies can compete for defense and federal work, few startups have focused squarely on the RFP process itself. Many incumbent platforms are adding AI features, but Liu argues that bolting AI onto old systems won’t work. “Most incumbents are trying to bolt AI onto outdated systems and user interfaces,” he said. “Vultron was purpose-built as an AI-native platform from day one.”
He points to three strategic advantages: workflow depth, a vertical data advantage, and a proprietary learning loop that improves output quality over time. “Every workflow run in Vultron makes the platform smarter,” Liu said. “That’s a compounding advantage incumbents can’t easily replicate.”
Vultron plans to use its latest funding to expand its research team and scale its go-to-market strategy. The company’s positioning as an “Agentic Operating System” for federal growth reflects its ambition to become the primary platform federal contractors rely on.
Still, the space is growing, and evolving. AI-native tools face scrutiny around data security, accuracy, and explainability, especially in high-stakes government contexts. Vultron’s systems are hosted in FedRAMP-compliant infrastructure and advised by security experts from the Department of Defense, but success will ultimately hinge on continued performance and trust at scale.
