Risotto AI Raises $10M to Transform Help Desk Ticketing
Risotto AI has raised $10 million in seed funding to automate help desk ticket resolution using artificial intelligence that actually works in complex enterprise environments. The startup tackles a persistent pain point for support teams: repetitive tickets that drain human agents' time while delaying customer resolutions. Unlike generic chatbots, Risotto integrates directly with existing ticketing systems like Jira to autonomously solve issues—without hallucinating or escalating simple problems unnecessarily.
Credit: Risotto
The round was led by Bonfire Ventures with participation from 645 Ventures, Y Combinator, Ritual Capital, and Surgepoint Capital. This influx of capital arrives as enterprises urgently seek AI tools that deliver measurable efficiency gains rather than flashy demos that falter in production. For companies drowning in support backlogs, Risotto's approach offers a pragmatic path toward meaningful automation.
Why Ticketing Automation Has Been Broken Until Now
Help desk automation isn't new. For years, companies have deployed rule-based bots and keyword-triggered responses that often frustrate users more than they help. These systems fail when tickets contain nuanced language, require cross-referencing internal documentation, or need contextual judgment calls. The result? Higher deflection rates but lower resolution rates—meaning bots "handle" tickets only to pass them to humans moments later.
Risotto's founders recognized that large language models alone couldn't solve this problem. Raw AI output is inherently non-deterministic; the same query might yield different responses across attempts. For payroll errors or access requests, inconsistency isn't just inconvenient—it's risky. As CEO Aron Solberg explains, the real innovation lies not in the foundation model itself but in the infrastructure layered on top of it.
"Our special sauce is the prompt libraries, the eval suites, and the thousands of real-world examples that train the AI to behave predictably," Solberg notes. This guardrail system ensures the AI follows strict protocols before taking action—like verifying user permissions before resetting passwords or confirming account details before processing refunds.
How Risotto Actually Resolves Tickets Without Human Help
The platform operates as an intelligent middleware layer between ticketing systems and internal tools. When a new ticket arrives, Risotto analyzes its content, determines intent, and checks against a curated knowledge base of resolved cases. If confidence thresholds are met, it executes predefined workflows: pulling data from HR systems, updating user permissions in directory services, or generating step-by-step troubleshooting guides.
Crucially, Risotto doesn't just suggest solutions—it performs them. In a recent deployment with payroll provider Gusto, the system autonomously resolved 60% of incoming support tickets. These weren't simple "password reset" requests but involved multi-step processes like correcting tax withholding errors, explaining benefit enrollment windows, and troubleshooting direct deposit failures. Each resolved ticket represented hours saved for human agents who could focus on complex escalations.
The system also learns continuously. Every ticket it processes—whether resolved autonomously or handed to a human—feeds back into its training loop. Over time, resolution accuracy improves while the scope of automatable issues expands organically based on real organizational patterns.
The Quiet Shift Toward AI-First Support Interfaces
While most enterprises still route tickets through traditional dashboards, Solberg observes a fundamental shift emerging among digitally native companies. Instead of agents scanning queues in Jira or Zendesk, frontline support increasingly happens inside conversational interfaces powered by enterprise LLMs. Think ChatGPT for Work—but purpose-built for support workflows.
In this model, the AI becomes the primary coordinator. A human agent might receive a notification: "Three high-priority tickets require review," alongside AI-drafted responses ready for approval. Or they might converse directly with the AI assistant: "Show me unresolved tickets about API rate limits from enterprise customers in the last 24 hours." The ticketing system fades into the background while the LLM handles triage, research, and drafting.
Risotto is architecting its platform to thrive in both worlds. Today, it plugs into conventional ticketing infrastructure. Tomorrow, it will serve as the reasoning engine behind these conversational support layers—translating natural language queries into precise system actions without exposing enterprises to AI's unpredictability.
Why $10M Now? The Perfect Storm for Support Automation
The timing of Risotto's funding round reflects broader market dynamics. After years of rapid hiring, enterprises now face pressure to do more with leaner teams. Simultaneously, foundation model capabilities have matured enough to handle structured business logic—but only when properly constrained.
Investors see a narrow window to back startups building the "plumbing" between raw AI and mission-critical operations. Unlike consumer AI apps where novelty drives adoption, enterprise tools must demonstrate ROI within weeks. Risotto's 60% automation rate at Gusto provides the concrete metric VCs need to justify bets in a crowded automation space.
Bonfire Ventures partner Sarah Chen, who led the round, emphasized this pragmatism: "We're not funding another wrapper around an API. Risotto built evaluation frameworks and safety rails that make AI dependable in regulated environments. That infrastructure is defensible—and desperately needed."
What This Means for Support Teams Worried About Job Loss
Automation announcements often trigger anxiety among frontline workers. But Risotto's data tells a different story. At Gusto, agent satisfaction scores actually increased after deployment. Why? Because the AI absorbed repetitive, low-satisfaction tasks—password resets, status checks, form corrections—freeing humans to handle nuanced customer interactions that require empathy and judgment.
Support leaders report another unexpected benefit: faster onboarding. New hires train alongside Risotto, which surfaces relevant knowledge base articles and suggests responses during their first weeks. Rather than memorizing hundreds of procedures, agents learn by observing how the AI resolves common cases—then gradually take over more complex scenarios as their confidence grows.
This collaborative model aligns with how most enterprises actually want to deploy AI: not as a replacement, but as a force multiplier that elevates human expertise rather than erasing it.
From Ticket Resolution to Preventive Support
Risotto's immediate focus remains expanding its library of automatable ticket types across industries—from SaaS onboarding glitches to e-commerce return authorizations. But the longer-term vision is more ambitious: preventing tickets before they're created.
Imagine an AI monitoring user behavior patterns and proactively intervening. A customer repeatedly clicks a broken link? Risotto could trigger a personalized notification with the correct URL before they contact support. An employee struggles to complete a benefits enrollment form? The system might push a contextual help video at the exact moment of friction.
This shift—from reactive resolution to predictive assistance—represents the next frontier for customer support. And it requires precisely the kind of deterministic, enterprise-hardened AI infrastructure Risotto is building today. With $10 million in fresh capital, the startup now has runway to prove that the most valuable AI applications won't feel magical at all. They'll feel boringly reliable—the kind of technology you only notice when it's gone.
For support leaders tired of overpromising automation tools, that reliability might be the most disruptive feature of all.
