The decision took 30 minutes. Intel CEO Lip-Bu Tan has invested in Nace.AI, an enterprise artificial intelligence startup with offices in Astana and Silicon Valley, DKNews.kz reports.
Nace.AI founder and CEO Dos Bakytzhan said Tan made the investment decision after a single conversation. The size and terms of the investment were not disclosed.
A 30-minute conversation led to the investment
According to Bakytzhan, Tan was drawn to Nace.AI’s strategy of building and scaling its own AI models for enterprise use.
“We were very well aligned strategically. Lip-Bu Tan liked our thesis around scaling our own AI models and developing in that direction. The decision was made very quickly — literally within a 30-minute conversation. When experienced entrepreneurs at that level see a strong team and a strong product, they tend to move quickly,” Bakytzhan said.
The distinction matters: Nace.AI says the investment decision was made within 30 minutes, not that the entire legal and financial transaction was completed in that time.
Nace.AI adds a veteran technology investor
Tan brings decades of experience spanning semiconductors, enterprise software and venture capital.
Alongside his role at Intel, he is the founder and chairman of venture capital firm Walden International and has invested in hundreds of technology companies over several decades. He has also served on the boards of major technology groups including Cadence Design Systems and Hewlett Packard Enterprise.
For Nace.AI, the investment brings in a backer with deep exposure to the industries at the center of the current AI buildout: chips, computing infrastructure, software and venture capital.
The startup is building AI that understands a company from within
Nace.AI is focused on enterprise AI rather than a general-purpose consumer chatbot.
Its technology is designed to analyze a company’s internal processes, corporate policies, financial data, documents, contracts and reporting. The longer-term ambition is to build an AI system capable of understanding an organization deeply enough to support management decisions.
That puts Nace.AI in one of the most competitive areas of the AI market, where companies are racing to turn large language models into tools capable of performing complex business workflows rather than simply answering questions.
Nace.AI says AI agents can cut 3,000 hours of work to 72
One of the company’s core areas of development is a new generation of AI agents.
Nace.AI says tasks that would typically require an employee 2,000 to 3,000 working hours can be completed by its AI agents in 60 to 72 hours.
Those figures are company-reported and were not presented as the result of an independent benchmark.
Nace.AI says its technology goes beyond automating individual tasks. The system is designed to examine an entire workflow, identify bottlenecks and errors, and highlight opportunities to improve efficiency.
If those productivity gains can be replicated across large-scale corporate deployments, the impact would extend beyond saving time. It could alter the cost structure of labor-intensive business processes.
Nace.AI previously raised $21.5 million
Tan’s investment follows an earlier funding round in which Nace.AI says it raised $21.5 million from international investors.
According to the company, that transaction was completed in less than 36 hours.
The amount invested by Tan has not been disclosed, making his involvement — rather than the size of the cheque — the central feature of the latest deal.
Astana remains part of Nace.AI’s global footprint
The Kazakhstan connection is more than a branding detail.
Nace.AI operates between Astana and Silicon Valley while developing enterprise AI products for a global market. That gives the startup a rare profile: a company with a base in Kazakhstan attempting to compete in one of the most capital-intensive and closely watched segments of the technology industry.
The next test will be less about how quickly Nace.AI can raise capital and more about execution. Investors and corporate customers will ultimately judge whether its claimed reduction of 2,000–3,000 working hours to 60–72 hours can be consistently reproduced in real-world enterprise deployments.
