A fresh player in the financial tech world just got a serious cash boost. Hyperbots announced yesterday they’ve raised $6.5 million to build what they’re calling “agentic AI” for finance teams. The funding comes from Gradient Ventures, Google’s AI-focused investment arm, with additional backing from Foundation Capital and other investors.
The San Francisco startup aims to automate the repetitive tasks that eat up finance professionals’ time. Their platform connects to common financial systems and learns how staff handle everyday processes. It then creates automated workflows, or “hyperbots,” that can take over these duties.
“Most finance teams waste hours each week on manual data entry and reconciliation,” said Hyperbots CEO Mike Ng. “Our technology watches how people work and builds custom automations that handle these tasks exactly as a human would, but without the errors or burnout.”
The company’s approach differs from traditional automation tools. Rather than requiring IT departments to program rigid workflows, Hyperbots observes actual user behavior. This allows the system to mimic human decision-making when handling exceptions or unexpected data formats.
Early customers report significant time savings. One mid-sized manufacturing firm claims they’ve reduced month-end close procedures from five days to just two after implementing the technology. Another user mentioned that tasks like invoice matching now happen automatically instead of requiring constant manual attention.
Financial leaders seem particularly drawn to the platform’s ability to work across different systems. “The real pain for most finance departments isn’t individual tasks – it’s the constant switching between QuickBooks, Excel, banking portals, and email,” explained Jamie Cohen, former CFO of Asana and an advisor to Hyperbots. “This solution bridges those gaps without expensive integration projects.”
The timing of this funding appears strategic. Finance departments face growing pressure to do more with less while maintaining accuracy. A recent McKinsey report indicated that up to 30% of finance activities could be fully automated with current technology, potentially saving companies billions in operational costs.
Hyperbots plans to use the new capital to expand its engineering team and develop more specialized capabilities for accounting, accounts payable, and financial planning processes. The company also intends to build partnerships with major financial software providers to streamline connections to their platform.
Industry experts see this as part of a larger trend toward AI-augmented financial work. “We’re moving beyond simple robotic process automation to systems that can actually reason about financial data,” noted Sarah Guo from Conviction Capital, which participated in the funding round. “The most valuable tools won’t replace finance professionals but will handle the tedious aspects of their jobs.”
The market for AI in finance continues to heat up. Competitors like Vic.ai and Auditoria have also secured significant funding in recent months, suggesting strong investor confidence in this sector. However, adoption challenges remain, particularly around trust and control.
Hyperbots addresses these concerns through what they call their “glass box” approach. Users can review exactly what actions the system plans to take before execution and adjust parameters as needed. This transparency stands in contrast to many AI tools that operate as mysterious black boxes.
Security represents another critical factor for financial automation. Hyperbots emphasizes their SOC 2 compliance and enterprise-grade data protection measures. The platform also maintains detailed audit trails of all automated activities, helping companies meet regulatory requirements.
The company currently targets mid-market businesses with finance teams of five to fifty people, though they plan to expand to larger enterprises as the platform matures. Their pricing model follows a subscription approach based on the number of workflows automated rather than user counts.
While the technology shows promise, questions remain about how extensively AI can handle complex financial judgments. Tasks requiring contextual understanding or ethical considerations will likely remain in human hands for the foreseeable future. Nevertheless,
