Freehand Launches AI Teams to Automate Enterprise Supply Chain Spend

SAN FRANCISCO, CA – February 09, 2026 – A new company named Freehand officially launched from stealth mode today at the Manifest 2026 conference, unveiling a bold mission to replace decades-old manual workflows in corporate supply chain and finance departments with autonomous AI Teams. The San Francisco-based firm, founded by industry veterans Nitin Jayakrishnan and Abhijeet Manohar, is already deploying its technology within Fortune 500 enterprises, promising a radical reduction in the operational friction that erodes company profits.

Freehand's platform is designed to deploy 'agentic AI,' a more advanced form of artificial intelligence that doesn't just analyze or recommend, but autonomously makes decisions and executes tasks. These AI Teams read unstructured documents like emails and invoices, interpret internal chat messages, reason across complex contracts and company policies, and then execute actions directly within a company's existing ERP and procurement systems. Early results from live deployments are significant, with the company reporting cycle time reductions for accounts payable and reconciliation of 80-90% and cuts in manual procurement efforts between 30-50%.

"The dirty secret of enterprise operations is that companies spend tens of millions of dollars just to keep the lights on,” said Nitin Jayakrishnan, co-founder and CEO of Freehand, in the official announcement. “Armies of people and outsourced teams doing tactical work like matching invoices, chasing exceptions, negotiating contracts and reconciling purchase orders is not strategic spend but margin erosion. Freehand saves you that time and money."

Beyond Automation: The Rise of Agentic AI

For years, enterprise automation has been dominated by Robotic Process Automation (RPA), software 'bots' that mimic human keystrokes and mouse clicks to perform repetitive, rules-based tasks. However, the limitations of RPA are well-known; they are often brittle, breaking when faced with unexpected exceptions, new document formats, or changes in system interfaces. Freehand aims to transcend these limitations with a fundamentally different architecture.

At the heart of its platform is a proprietary 'context graph.' This technology acts as a dynamic, living map of an enterprise's entire operational landscape. It weaves together unstructured communications—emails with suppliers, Slack and Teams messages about order discrepancies—with structured data from ERPs, payment systems, and supplier records. This creates a comprehensive understanding not just of what happened, but why a decision was made. According to the company, this allows its AI agents to handle the messy, exception-heavy reality of day-to-day operations where most automation fails.

Industry analysts note that this shift toward agentic AI represents a significant leap. While traditional AI might flag a mismatched invoice for human review, an agentic system like Freehand's is designed to understand the context, consult the original purchase order and contract terms, and execute the necessary correction or payment directly. This moves AI from a support tool to a core operational executor. Gartner analysts have previously described this evolution as a "revolution from robotic process automation," where AI begins to make business decisions autonomously rather than simply preparing data for a human to do so.

From Proven Success to Broader Ambition

While Freehand is a new entity, the team and technology behind it have a formidable track record. The company is an evolution of Pando, and its AI platform builds on the success of 'Pi,' a production-proven AI agent for the notoriously complex freight and logistics sector. Developed by the same founding team, Pi successfully automated freight procurement, planning, and payment processes for some of the world's largest shippers.

That predecessor technology earned widespread acclaim, including being named one of TIME Magazine's Best Inventions of 2025, a Visionary by Gartner, one of Fast Company's Next Big Things in Tech, and a Technology Pioneer by the World Economic Forum. The success of Pi in a real-world, high-stakes environment demonstrated that agentic AI could manage workflows riddled with exceptions at enterprise scale. Freehand is now taking that proven model and extending it across the full spectrum of supply chain spend, including direct materials, MRO (maintenance, repair, and operations), and other complex categories.

This legacy provides Freehand with a level of credibility rarely seen in a company just emerging from stealth. It isn't just a startup with a promising concept; it is a seasoned team scaling a technology that has already delivered measurable results in one of the toughest operational domains.

The Promise of Radical Efficiency

The financial implications of this technology are profound. Freehand's claim of reducing manual procurement and sourcing efforts by 30-50% and cutting AP cycle times by up to 90% points to a direct impact on a company's bottom line. In large enterprises, procure-to-pay processes involve thousands of employees and outsourced teams performing tactical, repetitive work. By automating this "operational grind," Freehand posits that the savings flow directly to the P&L, freeing human capital for more strategic endeavors.

While independent, publicly available case studies for Freehand are still forthcoming, the performance metrics cited are consistent with the potential of advanced AI in these fields. Industry reports have shown that AI-powered AP automation can slash processing costs by over 80% and accelerate processing times by more than 70%. Similarly, analysts at firms like KPMG have suggested AI could eliminate up to 80% of time spent on basic procurement tasks. Freehand's approach, which tackles not just clean data but the unstructured chaos surrounding it, aims to capture efficiencies that even earlier AI solutions could not.

Navigating the Hurdles of an Autonomous Future

Despite the immense potential, the path to widespread adoption of autonomous AI in the enterprise is not without significant challenges. Market readiness is growing, with one Deloitte survey indicating that 74% of companies plan to deploy agentic AI within two years. However, successful implementation hinges on overcoming several key barriers.

A primary obstacle is the state of existing enterprise infrastructure. Many organizations suffer from siloed legacy systems and poor data quality, which can cripple an AI's effectiveness. Integrating a sophisticated platform like Freehand requires a robust data foundation. Furthermore, the autonomous nature of agentic AI introduces new security and governance concerns. An AI agent with credentials to execute payments and modify records in an ERP system becomes a high-value target for cyberattacks, and the risk of unintended actions or data exposure is significant.

Beyond the technical hurdles lies the critical element of organizational change management. Shifting from human-led processes to autonomous AI-driven ones requires a cultural transformation. Employees must be trained to work alongside AI, and their roles must evolve from tactical execution to strategic oversight and exception management. Without a clear framework for human-AI collaboration and governance, companies risk project failure, employee resistance, and an inability to realize the technology's full return on investment. Freehand's success will depend not only on the power of its technology but also on its ability to help customers navigate these complex organizational shifts.