The problem
AI chatbots generate thousands of conversations a day. Almost none of that data gets actioned. The typical workflow: logs exist, nobody reads them. When something goes wrong — rising support volume, churning users, a broken assistant — teams find out through tickets, customer calls, or executive escalations. By then it’s too late. OpenBat closes the loop between “chatbot runs” and “team knows what’s happening.” It transforms raw conversation streams into structured intelligence:- Which users are dissatisfied right now
- What topics are driving frustration
- Where the assistant is failing (hallucinating, dodging, giving incomplete answers)
- Which conversations represent buying signals vs churn risks
- Whether your assistant is improving or degrading over time
Who it’s for
Product managers and engineering leads who own an AI chatbot product. You visit OpenBat to understand what customers are asking, how the bot is responding, and where to focus improvement effort. Customer success teams who need to identify at-risk users early — before they escalate or churn — and proactively reach out. AI/ML engineers tuning chatbot behavior. You use OpenBat to measure response quality across dimensions (relevance, accuracy, clarity) and detect behavioral regressions.How it works in 60 seconds
- You integrate the SDK — one function call wraps your existing AI chatbot response handler
- Conversations flow in — every message is captured with user, org, and session metadata
- LLM analysis runs automatically — sentiment, intent, topics, flags, behavior, and outcome are extracted from each message
- Insights surface in the dashboard — sentiment trends, frustrated users, capability gaps, response quality scores
- Workflows fire alerts — Slack or webhook notifications trigger when conditions match (e.g., churn risk detected)
Core features at a glance
| Feature | Description |
|---|---|
| Sentiment analysis | Per-message sentiment scoring (-1 to +1) with chunk-level reasoning |
| Intent classification | Each user message classified into one intent from your custom list |
| Topic detection | Up to 3 topics extracted per user message, auto-discovered from conversation patterns |
| Flag detection | Business signals flagged per message (churn risk, buying signal, frustrated user) |
| Assistant behavior | Assistant responses classified by behavior (hallucinating, dodging, helpful) |
| Resolution outcomes | Conversation outcomes tracked (fully resolved, capability failure, deflected) |
| Response quality | Multi-dimension scoring across relevance, completeness, clarity, accuracy, and tone |
| Deep search | Semantic search across all messages — find by meaning, not just keywords |
| Auto-translation | 27 languages detected and translated automatically; analysis runs on translated text |
| Custom reports | Configurable dashboards with 14 widget types |
| Workflow automation | Trigger-condition-action chains that fire webhooks when patterns are detected |
Get started
Quickstart
Go from zero to first insights in under 5 minutes.
SDK reference
Integrate OpenBat into your chatbot with the JavaScript SDK.
API reference
Send conversations and retrieve analysis data via the REST API.