The press release
Support Teams Stop Drowning in Tickets as AI Agent Resolves Common Issues Without Human Touch
Small and mid-sized businesses can now close repetitive support tickets in seconds — without hiring more agents.
Customer support teams at growing companies spend more than half their time answering the same questions: order status, password resets, refund policies, account changes. Every hour an agent spends on a routine ticket is an hour not spent on the complex problem that actually needs a human. Hiring more agents is expensive and doesn't fix the root cause.
Today, a new AI support agent launches that reads incoming tickets, understands intent, pulls answers from a company's existing knowledge base and backend systems, and sends a verified resolution — all without a human in the loop. For tickets it cannot confidently resolve, it writes a detailed draft and routes to the right human agent with full context attached. Companies connect their helpdesk in under 30 minutes and the agent begins handling tickets the same day.
In a pilot with five mid-sized e-commerce and SaaS companies, the AI agent fully resolved 54% of incoming tickets with a customer satisfaction score within 3 points of their human-agent baseline. Support teams reported reclaiming an average of 22 agent-hours per week on routine work.
“We were at the point where we needed to hire two more agents just to keep up with volume. Instead, we connected this in an afternoon and it handled our top five ticket categories from day one. My team now actually has time to talk to customers with real problems.”
The verdict
Customer clarity
Support managers at SMB and mid-market companies are a well-defined, reachable buyer with a clear budget and direct pain — they own the problem and can sign a contract.
StrongProblem sharpness
Repetitive ticket volume is a real, universal, and growing cost center — the problem is not manufactured and every support org feels it daily.
StrongEvidence strength
A five-company pilot is promising but too small and too self-selected to validate resolution rate and CSAT claims at scale across diverse verticals.
Needs workRisk
The incumbent encirclement threat from Zendesk and Intercom is serious and near-term; the product needs a durable wedge beyond resolution rate before that window closes.
Needs work
The customer and problem are real, and early numbers are credible enough to keep investing — but the team must widen the pilot to 30+ diverse customers and articulate a defensible moat before Series A, or this becomes a feature acquisition target rather than a standalone company.
Customer FAQ
How much does it cost?
Pricing starts at $299/month for up to 1,000 AI-resolved tickets. Above that, you pay $0.18 per resolved ticket. You are only charged for tickets the AI fully closes — drafted-and-routed tickets count as zero.
How long does it take to set up and will it need our IT team?
Setup connects to your existing helpdesk (Zendesk, Intercom, Freshdesk, or Help Scout) via OAuth — no custom code required. You then point it at your knowledge base or paste in your top FAQ content. Most support managers complete setup in under 30 minutes without IT involvement.
How is this different from the AI features already built into Zendesk or Intercom?
Built-in helpdesk AI mostly suggests articles to agents or auto-tags tickets. This agent acts — it reads the ticket, queries your order management or CRM system for live data, composes a personalized reply, and sends it. It is also helpdesk-agnostic, so you are not locked into one platform's roadmap.
What happens when the AI gets it wrong?
The agent sets a confidence threshold you control. Below that threshold it never sends autonomously — it drafts and escalates. You can also review a sample of resolved tickets in a daily digest. Any customer reply to an AI resolution that signals dissatisfaction is immediately flagged and re-routed to a human.
What data does the AI store and is our customer data safe?
Ticket content is processed in-memory to generate a response and is not used to train shared models. Resolved ticket logs are stored in your account for 90 days for audit purposes and can be deleted on request. SOC 2 Type II certification is in progress; current controls are available in our security overview doc.
Board FAQ
What does it cost to build and operate this, and when does unit economics work?
Primary cost driver is LLM inference, currently ~$0.04–0.07 per ticket processed depending on length and model tier. At $0.18 per resolved ticket and a 54% resolution rate, gross margin per ticket processed is approximately 60–65% at current volume. Margin compresses if resolution rate or pricing falls. Break-even on a $1.2M build cost is modeled at roughly 18 months assuming 40% month-over-month customer growth in year one — that growth assumption is the number to stress-test.
What is the single biggest risk to this business?
Helpdesk platforms (Zendesk, Intercom, Salesforce) are all building autonomous resolution natively. If the category leader ships a 'good enough' version and bundles it for free, our standalone value proposition weakens significantly. Our defensibility window is roughly 18–24 months before incumbent features catch up for the median customer.
Why is now the right time — why didn't this work two years ago?
GPT-3.5/4-class models crossed a reliability threshold in 2023 that makes autonomous reply generation safe enough to send without human review for well-scoped ticket types. Before that, hallucination rates on factual customer data queries were too high to automate resolution without unacceptable error rates. Inference cost also dropped ~10x between 2022 and 2024, making per-ticket economics viable.
What must be true for this to be a $50M ARR business?
Three things must hold: (1) resolution rate must stay above 50% across diverse customer verticals — below that, ROI versus hiring is marginal; (2) we must retain customers beyond 12 months as their ticket mix evolves and gets harder; (3) we must expand into at least one adjacent workflow (proactive outreach, voice, in-app) before incumbents close the gap on core ticket resolution.
How do we prevent a single LLM provider dependency from becoming an existential risk?
The routing and confidence-scoring layer is model-agnostic. We can swap or blend providers (OpenAI, Anthropic, Google) within a sprint. However, prompt logic and fine-tuning investments are partially non-transferable, so a hard deprecation or pricing shock from our primary provider would cost 4–6 weeks of engineering time to fully migrate — not existential, but a real operational risk.
PRD excerpt
Goals
Autonomous Resolution Rate
Achieve a fully autonomous ticket resolution rate of ≥50% across all connected customer accounts within 60 days of each customer's go-live, measured monthly.
Customer Satisfaction Parity
AI-resolved tickets must score within 5 CSAT points of each customer's human-agent baseline, measured via post-resolution survey, for 80% of accounts.
Time-to-Value
90% of new customers complete helpdesk connection and resolve their first AI ticket within 24 hours of account creation, with no IT involvement required.
Primary persona
Sandra Park - Head of Customer Support, 80-person e-commerce company
- Her 6-agent team spends 60%+ of their day on order status, returns, and password resets — work that requires no judgment but still eats capacity.
- She cannot justify headcount to her CFO for 'more of the same' tickets, so queue times are slipping and CSAT is starting to drop.
- She has tried help center chatbots before and they damaged customer trust when they gave wrong answers — she will not deploy anything she cannot audit and control.
Functional requirements
- FR-1The agent must ingest tickets from Zendesk, Intercom, Freshdesk, and Help Scout via OAuth within 30 minutes of account setup, requiring no code from the customer.high
- FR-2For each ticket, the agent must query connected data sources (order management, CRM, knowledge base) and generate a verified, personalized reply — or escalate with a pre-written draft if confidence is below the customer-set threshold.high
- FR-3Customers must be able to set a confidence threshold (low / medium / high) that controls the boundary between autonomous send and draft-and-route, without requiring engineering changes.high
- FR-4A daily digest must surface a random sample of AI-resolved tickets for manager review, flagging any customer follow-up replies that signal dissatisfaction.medium
- FR-5The system must log every autonomous action with full input/output audit trail, retained for 90 days, exportable as CSV for compliance review.medium
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