AI agents step into enterprise workflows
Enterprise AI automation refers to the use of AI agents and platforms to perform end-to-end business tasks, connecting data, workflows, and decisions so that processes can run with minimal human intervention while still delivering measurable, auditable outcomes. In this context, two new startups, Opereit and Zaro, highlight how AI agents are moving beyond chatbots into autonomous business process execution. Together they have secured USD 7.6 million (approx. RM35.0 million) in pre-seed AI agents funding to solve narrow but painful operational problems: logistics claims processing and fragmented enterprise workflows. Instead of generic copilots, both firms focus on specific, high-friction processes where automation can show immediate ROI. Their emergence suggests a shift in how investors and customers view AI workflow automation: less as an experimental tool and more as an infrastructure layer for revenue recovery, coordination, and institutional memory.
Opereit targets trillion-dollar logistics claims gaps
Opereit has emerged from stealth with USD 2.5 million (approx. RM11.5 million) to automate logistics claims recovery using AI agents. The company focuses on logistics claims processing, where billing errors, lost shipments, and unclaimed credits often slip through manual checks. According to Opereit, the logistics industry leaves more than $1 trillion (approx. RM4.6 trillion) in value unrecovered each year due to inadequate tracking and limited follow-up processes. Its AI-powered platform scans transportation invoices, shipment data, and carrier agreements to identify discrepancies and automatically pursue recoveries, turning a labor-intensive back-office task into a continuous, software-driven revenue stream. By targeting a specific, fragmented workflow, Opereit illustrates how enterprise AI automation can pay for itself quickly: every recovered claim is direct found money. The new capital will fund continued product development and expansion of its AI-driven logistics recovery platform.
Zaro builds an AI-native workspace for fragmented enterprises
Zaro has raised USD 5.1 million (approx. RM23.5 million) in pre-seed funding to build an AI-native workspace that connects enterprise data, workflows, and AI tools in a single adaptive environment. Founded by former Convergence team members who later contributed to Salesforce’s Agentforce, Zaro focuses on a core enterprise problem: AI agents and automation tools often run in isolation, so knowledge produced in one system does not carry over to others. Zaro’s platform introduces a shared context layer where company data, decisions, workflows, and operational history are stored and reused. AI agents and applications then operate on top of this layer, allowing information from one process to inform the next. Co-founder Qian Zheng said, “Context compounds. Models become increasingly interchangeable over time, but the value created from an organisation’s accumulated knowledge remains unique.”
From chatbots to autonomous AI workflow automation
Both Opereit and Zaro reveal how AI agents are evolving from conversational helpers into operational actors embedded in enterprise AI automation. Opereit deploys agents that audit logistics invoices and file claims without human initiation, while Zaro aims to coordinate many such agents around a shared context. Together, they show that AI workflow automation is moving deeper into core business processes instead of sitting at the UI layer as chatbots. Their focus on narrow, measurable outcomes—recovering lost logistics revenue or unifying scattered workflows—also signals a shift in AI agents funding. Investors are backing vertical-specific automation layers where ROI can be quantified through recovered cash, lower tool sprawl, or reduced operating costs. As more enterprises experiment with AI-native workspaces and automated claims recovery, the expectation will shift from AI as a pilot project to AI as an everyday operator inside the business.
