AuxoAI raises $20.7 million to put more AI engineers alongside enterprise teams

AuxoAI plans to put more AI specialists alongside enterprise customers, backing an approach that combines software with people who learn how a business operates. The company announced a $20.7 million Series A led by NTTVC on October 20, 2026, to scale teams that help turn business goals into working AI systems.

The round also includes George Still Jr., partner emeritus at Norwest Venture Partners; Amit Zavery, ServiceNow’s president, chief product officer and chief operating officer; and Giacomo Sonnino, a managing director at Charlesbank Capital Partners. AuxoAI said all three are joining as strategic investors and advisers. Lead investor NTTVC is a Silicon Valley venture firm formed in partnership with Japanese technology group NTT.

AuxoAI divides its intended use of the capital into three areas, spanning the people doing deployments and the technology supporting them:

• Expand its global pool of forward-deployed talent: the strategists and engineers who work directly with customers on their AI projects.
• Invest in proprietary technology and AI-native delivery capabilities, supporting the company’s approach to building and implementing AI systems for enterprise customers.
• Build its market presence and network of strategic partners as it seeks to take the same delivery model to more customers globally.

Engineers stay with the business problem

Founded in 2023 and headquartered in the San Francisco Bay Area, AuxoAI works with large enterprises on systems intended for day-to-day use. The company says its Forward Deployed Engineering team has worked with more than 40 clients across manufacturing, consumer goods, healthcare, telecommunications and other industries. That is the company’s stated customer experience behind the model it now wants to scale.

Forward-deployed means working alongside the customer rather than simply supplying a tool. AuxoAI pairs strategists with engineers to understand existing work, capture business knowledge and identify processes that AI could change. The two groups then build production systems together with customer teams. Its stated approach links the initial choice of a business problem to the engineering needed to put a system into use.

We founded AuxoAI on a simple thesis: AI will fundamentally change how enterprises work, but technology alone will not make that happen

Amaresh Tripathy, Arvinder Pal Singh and Kabir Singh, AuxoAI co-founders, in the funding announcement

Damia keeps deployment knowledge in software

The software component is Damia, AuxoAI’s proprietary engineering workbench—a toolkit for delivering enterprise AI. AuxoAI says it incorporates knowledge from previous deployments to speed implementation. The funding announcement presents Damia as an existing part of its delivery model, alongside the embedded teams, rather than as a new product being launched with the financing.

SiliconANGLE describes the workbench as combining reusable AI agent capabilities with tools that capture a company’s knowledge and processes. Those agents are systems designed to carry out tasks with limited human oversight. In that account, what the delivery team learns is preserved in software and structured knowledge, rather than remaining only in interviews or documents. That gives the engineering work a reusable software component.

AuxoAI also brings existing cloud relationships to that work. It identifies itself as a Google Gemini Enterprise transformation partner and an AWS Advanced Tier partner. Those relationships form part of the company’s current delivery base; the Series A’s stated partnership spending is intended to build its network further.

Customers describe two different jobs

Customer statements included in the funding announcement illustrate how that mix of people and software is being used. Athenahealth chief marketing officer Stacy Simpson said AuxoAI engineers embedded directly into her teams, built AI capabilities and agents, and supported training and changes to working practices. She credited the work with shortening campaign strategy-to-launch timelines, removing hours of manual review and shifting attention from backward-looking metrics toward earlier signals of performance.

At restaurant group Fogo de Chão, the described use is consumer research. Chief marketing officer Michael Breed said Damia’s synthetic consumer personas—simulated consumers—helped inform marketing, menu design and pricing decisions. He described connecting those personas to real customer segments, then comparing expected performance with actual results. The intended learning loop runs from a simulated response to an observed business result, with the comparison feeding subsequent decisions.

Neither customer statement supplies a numerical measure of the improvement. They describe specific uses and claimed benefits, but do not establish how much faster campaigns became or how closely simulated consumer responses matched real behavior.