Own your AI support bot's performance without
Teams that deploy AI chatbots for support need to know whether the bot is accurate, safe, and affordable, without filing a ticket with engineering every time. Igris gives customer success direct visibility into bot quality, cost per ticket, and escalation alerts, so problems surface in your dashboard before they surface in a customer complaint.
Support Quality Monitoring
Your team manages the AI support bot, but real visibility into how it is performing is not yours to access. When you want to know whether it is giving accurate answers, how often it fails, or whether it is handling sensitive customer data correctly, you message engineering and wait. By the time you get an answer, the problem may have been running for days, with customers already affected. The team that owns the support experience is consistently the last to know when something goes wrong. AI chatbot monitoring should not require an engineering ticket every time you need a straight answer.
The Igris dashboard gives customer success direct visibility into the bot's connection, request volume, error rate, and latency, without asking engineering. PII redaction event counts reveal how often the bot encounters sensitive customer data, and a rising count can signal a prompt problem worth investigating. Policy violations surface when the bot attempts to reach systems it should not, billing tools, internal databases. Content guards scan responses for leaked credentials or internal information before any customer ever sees them.
Your team owns the monitoring, not the dependency. Problems surface in your dashboard before they surface in a customer complaint. And when you do escalate to engineering, you arrive with specific data, not just a report that something feels off.
Cost Per Ticket Tracking
You know AI support is driving down resolution time. What you do not know is what each ticket costs at the LLM level, which makes it difficult to present a clear ROI to leadership, defend the budget, or explain why costs spiked in a given month. When a customer pastes an enormous document into the chat or a conversation runs unusually long, there is no signal. The cost absorbs into the monthly total and nobody notices until it shows up on an invoice that is already closed.
Track the support connection's spend over time in Igris and divide it against ticket volume from your support platform to get a working cost per ticket figure. Cost anomaly detection highlights unusually expensive interactions, the outliers that would otherwise go unnoticed until month end. Token limits on input and output cap the maximum cost of any single conversation, keeping the average predictable and preventing edge cases from distorting the numbers.
You walk into a budget conversation with a cost per ticket figure, not a line on an invoice and a rough estimate. Outliers get caught automatically. When leadership asks whether AI support earns its cost, you have a real number to anchor the answer.
Escalation Alerts
Right now, the most reliable signal that the AI support bot is struggling is a customer complaint. By the time a user reports a wrong answer, a failed interaction, or something that felt off, the problem may have been running for hours. Your team finds out last, after engineering, after the monitoring alert, sometimes after the customer has already escalated. For a team that owns the support experience, that is the wrong order entirely. AI bot escalation alerts should reach the people who own the outcome, not just the people who own the code.
Igris puts the right alerts in the right hands. Policy rules trigger a webhook to your Slack or Discord channel the moment the bot attempts to access a restricted tool or model. Anomaly detection notifies your team directly when error rates spike, a reliable early signal that the bot is struggling before customers feel it. When multiple failures cluster within a short window, incident grouping surfaces them as a single event, so your team reviews one clear incident instead of dozens of scattered notifications.
Your team is no longer the last to know. Escalations reach the people who own the customer experience first, with enough context to act immediately, or to escalate to engineering with a specific, documented event rather than a vague report that something went wrong.
Put your support bot's data in your own hands
See how customer success teams monitor bot quality, track cost per ticket, and get escalation alerts the moment the bot struggles.