A 40-Seat SaaS Deployed 24/7 Global Support in 11 Days

The Scaling Ceiling

For Chartify, a 40-person data visualization SaaS, growth was a double-edged sword. Their user base was expanding rapidly across North America, Europe, and the Asia-Pacific region. But their four-person customer support team, based entirely in San Francisco, was buckling under the strain. Key metrics told a story of a system at its breaking point.

• First Response Time (FRT): What was once a respectable two hours had ballooned to an average of eight hours. For a user in Sydney, that meant an entire business day wasted waiting for an initial reply.
• Ticket Backlog: The team ended every week with over 200 unresolved tickets, creating a persistent drag on morale and customer sentiment.
• Customer Satisfaction (CSAT): The gold standard metric had dipped from a healthy 95% to a worrying 88%. Users weren't just waiting, they were growing frustrated.

Leadership faced a classic scaling dilemma. The traditional solution, building a global, follow-the-sun support team, was financially prohibitive. The estimated cost for hiring, training, and managing six additional support representatives to cover the remaining time zones was over $450,000 in first-year, fully-loaded costs. For a lean company focused on product development, this was a non-starter. They were hitting a ceiling, not of market demand, but of operational capacity. The cost of inaction was mounting in the form of trial abandonment and churn risk.

Beyond Deflection: The Search for a Resolution Engine

Chartify’s leadership briefly explored the standard alternatives, but found them fundamentally inadequate for their needs. The goal was not to deflect inquiries, but to resolve them with speed and precision.

Offshore BPOs presented challenges in quality control, security, and the deep product expertise required to support a technical SaaS product. The ramp-up time alone would take months, with no guarantee of success.

Traditional chatbots were dismissed even more quickly. Their rigid, rule-based logic was a poor fit for the dynamic and varied queries of Chartify’s users. These systems are designed to answer simple, repetitive questions from a static knowledge base. They cannot access user data, they cannot take action within other applications, and they frequently frustrate users by failing to understand context, escalating the very tickets they are meant to prevent.

Chartify needed a new class of solution. They needed an engine that could not only understand and converse, but also act. It had to operate with the context of a human agent, accessing the same tools (Zendesk, Jira, Stripe, and their internal admin panel) to deliver complete resolutions, not just canned responses. This led them to Autonome and our customer service agent, Luna.

The 11-Day Deployment Blueprint

The implementation was not a months-long consulting project. It was a focused, 11-day sprint from initial integration to full operational autonomy. Here is the detailed breakdown.

### Phase 1: Integration and Knowledge Ingestion (Days 1-2)

The first step was connecting Luna to Chartify’s core operational stack. This was accomplished via secure, pre-built API integrations:

• Help Desk: Zendesk, for ticket history and communication.
• Knowledge Base: Notion, containing all technical documentation and internal guides.
• Billing: Stripe, for subscription and payment context.
• Engineering: Jira, for escalating verified bugs.
• Internal Tools: A secure connection to the company’s own admin panel for user-specific actions.

Once connected, Luna ingested the entirety of Chartify’s historical support tickets and knowledge base articles. This process is critical. Luna does not just perform a keyword search. It builds a semantic understanding of past issues, successful resolutions, and the company’s specific support language and tone.

### Phase 2: Defining Actions and Guardrails (Days 3-5)

This is where an autonomous agent diverges from a chatbot. The Chartify team, led by their Head of Support, defined a clear set of actions Luna was permitted to execute. These were not abstract goals, but concrete, tool-specific operations.

Initial Action Set: * Extend Trial: If a user on a trial asks for more time, extend their trial by 7 days via the internal admin panel. * Process Refund: For any customer on the 'Pro' plan (under $50/month), process a full refund for the last billing cycle via Stripe. * Password Reset: Guide a user through the self-serve password reset flow and, if that fails, trigger a secure reset link. * Feature Clarification: Answer “how-to” questions by synthesizing information from the Notion knowledge base into a clear, step-by-step response. * Bug Escalation: If a user reports a bug that can be replicated based on provided information, create a Jira ticket with a standardized format, including user ID, logs, and a link to the original Zendesk conversation.

Crucially, they also established guardrails. For example, refund requests for Enterprise customers or amounts over $50 were automatically flagged and assigned to a human agent for approval.

### Phase 3: Sandbox Simulation and Calibration (Days 6-8)

With actions defined, Luna entered a sandbox mode. It began analyzing live, incoming tickets in real-time, but did not respond to customers directly. Instead, it proposed a response and a sequence of actions in a private Slack channel for the human support team to review.

This feedback loop was invaluable. The team could approve Luna’s proposed action with a click, or provide corrective feedback like, “For this type of user, first offer to extend the trial before suggesting a plan downgrade.” Each piece of feedback refined Luna’s decision-making model, aligning it ever closer with the team’s best practices.

### Phase 4: Phased Rollout and Go-Live (Days 9-11)

On Day 9, confident in Luna’s performance, Chartify activated it for 10% of new incoming tickets. They monitored its live performance closely, tracking resolution rate and CSAT for the AI-handled cohort. The results were immediately positive.

By Day 11, they moved to full deployment. Luna was set as the default first responder for 100% of incoming support inquiries via email and web chat, 24 hours a day, 7 days a week.

The Quantitative Impact: 90 Days Later

The results, measured over the first 90 days of full deployment, represented a step-function change in Chartify’s operational efficiency and customer experience.

• Autonomous Resolution: Luna successfully handled 68% of all incoming support tickets from start to finish, without any human involvement.
• First Contact Resolution: For the tickets it handled, Luna achieved an 85% first-contact resolution rate.
• First Response Time: Global FRT fell from 8 hours to a median of 52 seconds, regardless of the customer’s time zone.
• Customer Satisfaction: CSAT rebounded from 88% to 96%. Feedback frequently praised the “incredibly fast and helpful” support.
• Financial ROI: The company completely avoided the $450,000 annual expenditure on a global support team. The ROI on their Autonome subscription exceeded 30x in the first year alone.

More importantly, the role of the human support team was elevated. Freed from the deluge of repetitive, Tier 1 requests, they now function as high-value Customer Advocates. Their work has shifted to managing complex escalations, proactively engaging with high-value customers, and using the insights surfaced by Luna to improve the product and knowledge base. Support was no longer a cost center, but a strategic driver of retention and growth.

The infrastructure for autonomous operations is no longer a multi-year project or a tool reserved for hyperscalers. With Autonome, you can deploy your own AI worker, trained on your data and integrated with your tools, in minutes. Start building your 24/7 autonomous workforce on Getautonome.com. No sales call required.