Risotto has raised a $10 million seed round to automate help desk work with AI, betting that the toughest part of fixing tickets isn’t the chatbot but the orchestration behind it. The financing was led by Bonfire Ventures, with participation from 645 Ventures, Y Combinator, Ritual Capital, and Surgepoint Capital, giving the early-stage company capital to scale an approach that treats AI as a reliable operator inside existing ticketing stacks rather than a flashy interface on top.
A Middleware Brain for Tickets That Handles Orchestration
Risotto positions itself between ticket managers like Jira and the internal tooling that actually resolves requests, from permissions systems to payroll workflows. The company uses a third-party foundation model but emphasizes the guardrails, state management, and policy logic wrapped around it. In practice, that means the system determines whether a ticket can be handled autonomously, fetches the right context, executes approved playbooks, and leaves an audit trail that humans can review or override.
Early deployments suggest meaningful lift. In a pilot with payroll provider Gusto, Risotto says its system automated roughly 60% of support tickets, a result that aligns with broader research indicating that repetitive, rules-driven tasks are prime candidates for AI-driven resolution. The company stresses that it is not replacing core ticketing platforms; it is cutting through the glue work that bogs them down.
Why Help Desks Are Ripe for Change Across Enterprises
Help desk automation is a large and growing category dominated by ServiceNow, Zendesk, and Freshworks. Incumbents have shipped their own AI features, yet many enterprises still wrestle with fragmented systems, inconsistent data, and manual triage. Analysts at Gartner and IDC have highlighted IT service management as a top area for generative AI investment because the workflows are measurable, repetitive, and highly instrumented—ideal conditions for proving ROI.
The cost side is real. One Risotto customer reportedly employs four people full-time just to wrangle Jira administration—before even layering in any AI. By automating assignment, enrichment, and known fixes across the sprawl of tooling, vendors like Risotto aim to shrink time-to-resolution, reduce escalations, and free up specialists to focus on genuinely novel work.
From Interfaces to Orchestration in Modern Ticketing
CEO Aron Solberg argues the industry is moving from human-centric interfaces to AI-led orchestration. Today, most customers still resolve the vast majority of tickets traditionally—he estimates around 95%—but he expects a shift toward LLMs acting as the primary interface that routes and executes tasks. In that model, general-purpose assistants such as ChatGPT for Enterprise or Gemini would call specialized tools like Risotto to perform reliable, policy-aware actions.
To prepare, Risotto has built integrations that connect through the Model Context Protocol, an emerging standard for secure tool use by AI systems. The practical benefit is twofold: central AI agents can delegate ticket work to a service that understands enterprise context, and enterprises get deterministic controls, approvals, and logs to satisfy security and compliance requirements.
Execution Risks and Differentiation in AI Ticketing
Autonomous remediation is not trivial. Models can hallucinate, permissions can drift, and a single misrouted workflow can create new tickets faster than it closes them. Risotto’s pitch is that reliability comes from the scaffolding—policy checks, identity-aware actions, rollback paths, and human-in-the-loop confirmations—rather than from the base model alone. That emphasis on control layers may be the wedge against incumbents whose AI features are often embedded but generalized.
The fresh capital will likely go toward deepening connectors to mainstream systems, expanding guardrail libraries, and hardening enterprise features like role-based access, auditability, and data residency. Buyers should watch a handful of metrics to judge maturity: autonomous resolution rate, average handle time reduction, cost per ticket, and change-management friction for frontline teams.
What This Means for IT Teams Adopting AI Automation
If the orchestration-first vision holds, help desks could evolve into AI-managed back offices where human agents supervise exceptions and focus on complex root-cause analysis. That shift won’t happen overnight; most organizations will blend automation with existing workflows and tighten governance as they go. But the direction is clear: as AI agents become competent at routine support work, tools that manage context and reliability—not just conversation—will determine who wins the next wave of ticketing automation.
