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To track **AI chatbot responses across platforms**, use multi-platform analytics tools like **Dashbot (now Dimension Labs)**, **Langfuse**, or **Botium**, which aggregate data from sources such as Facebook Messenger, Google Assistant, Amazon Alexa, and Rasa for unified monitoring of conversations, metrics, and performance[The Top Chatbot Analytics Tools to Drive Conversion and ...](https://masterofcode.com/blog/top-chatbot-analytics-tools)[Chatbot Monitoring with Advanced Observability - Langfuse](https://langfuse.com [...]

December 24, 2025

[...] ls for Cross-Platform Tracking These tools support integration across multiple chatbot platforms, enabling centralized logging, visualization, and analysis of responses: - **Dashbot (Dimension Labs)**: Tracks KPIs like user engagement, conversation history, and retention across Facebook Messenger, Google Assistant, Amazon Alexa, Rasa, Genesys, and more. Visualize data for review; official site: https://www.dashbot.io/[The Top Chatbot Analytics Tools to Drive Conversion and ...](https://masterofcode.com/blog/top-chatbot-analytics-tools). - **Langfuse**: Provides observability for chatbot systems wit [...]

December 24, 2025

[...] Botanalytics](https://botanalytics.co/)** - Conversational analytics platform that tracks chatbot conversations across multiple platforms including Facebook Messenger, Slack, and Alexa - **[Dashbot](https://www.dashbot.io/)** - Analytics platform for conversational interfaces that works with Alexa, Google Assistant, Facebook Messenger, and more - **[Chatbase](https://chatbase.com/)** (now part of Google Cloud) - Analytics tool for chatbots that helps track user engagement and conversation flows ### 2. **Multi-Platform Management Tools** - **[ManyChat](https://manychat.com/)** - Chat marketing plat [...]

December 24, 2025

[...] nversations across multiple platforms including Facebook Messenger, Slack, and Alexa - **[Dashbot](https://www.dashbot.io/)** - Analytics platform for conversational interfaces that works with Alexa, Google Assistant, Facebook Messenger, and more - **[Chatbase](https://chatbase.com/)** (now part of Google Cloud) - Analytics tool for chatbots that helps track user engagement and conversation flows ### 2. **Multi-Platform Management Tools** - **[ManyChat](https://manychat.com/)** - Chat marketing platform with built-in analytics for Instagram, Facebook Messenger, WhatsApp, and SMS - **[MobileMonkey] [...]

December 24, 2025

[...] g, monitoring, and analyzing machine-generated data ### 4. **Database Solutions** - **[MongoDB](https://www.mongodb.com/)** - NoSQL database commonly used to store conversation logs - **[PostgreSQL](https://www.postgresql.org/)** - Relational database for structured conversation data storage - **[Google BigQuery](https://cloud.google.com/bigquery)** - Data warehouse for analyzing large-scale chatbot interaction data ## **API-Based Tracking** ### 5. **Integration Platforms** - **[Zapier](https://zapier.com/)** - Connect different chatbot platforms and send conversation data to your tracking tool [...]

December 24, 2025

[...] formerly Integromat)](https://www.make.com/)** - Automation platform to route chatbot responses to centralized tracking systems - **[n8n](https://n8n.io/)** - Open-source workflow automation tool for connecting chatbot APIs ## **Platform-Specific Analytics** ### 6. **Native Platform Tools** - **[Google Analytics 4](https://analytics.google.com/)** - Can track web-based chatbot interactions with proper event configuration - **[Meta Business Suite](https://business.facebook.com/)** - Analytics for Facebook Messenger and Instagram chatbots - **[Telegram Bot API](https://core.telegram.org/bots/api)** [...]

December 24, 2025

[...] marsh.com Community and user-chat moderation (keep customer/community chats safe: toxicity, harassment, self-harm, hate, sexual content) - Azure AI Content Safety — text/image moderation with granular categories: https://azure.microsoft.com/products/ai-services/ai-content-safety - Perspective API (Google/Jigsaw) — toxicity and harassment scoring for user text: https://www.perspectiveapi.com - OpenAI Moderation — safety classifiers for text content: https://platform.openai.com/docs/guides/safety-moderation - Hive Moderation — scalable text/image/video content moderation: https://thehive.ai/moderatio [...]

December 24, 2025

[...] are-bots) - Telegram Bot API (https://core.telegram.org/bots/api) - If you use bot platforms/frameworks, add middleware/hooks to log events: - Microsoft Bot Framework (https://learn.microsoft.com/azure/bot-service/bot-builder-overview?view=azure-bot-service-4.0) - Dialogflow CX (https://cloud.google.com/dialogflow/cx) - Rasa (open source) (https://rasa.com/docs/rasa) - Botpress (https://botpress.com/) 3) Attach model-level telemetry for LLM calls - Persist the provider request ID to correlate failures and measure latency/cost: - OpenAI x-request-id (https://platform.openai.com/docs/guid [...]

December 24, 2025

[...] ides/rate-limits/retrying-requests) - Anthropic request IDs (https://docs.anthropic.com/en/docs/build-with-claude/reliability#request-ids) - Also store model name, tokens, latency, and cost from the response usage objects where available: - OpenAI API docs (https://platform.openai.com/docs) - Google AI (Gemini) docs (https://ai.google.dev/) - Azure OpenAI Service (https://learn.microsoft.com/azure/ai-services/openai/) 4) Pipe events into a centralized store - Open-source observability path: - OpenTelemetry Collector → Elasticsearch + Kibana or Grafana Loki + Grafana - Elasticsearch (htt [...]

December 24, 2025

[...] - Anthropic request IDs (https://docs.anthropic.com/en/docs/build-with-claude/reliability#request-ids) - Also store model name, tokens, latency, and cost from the response usage objects where available: - OpenAI API docs (https://platform.openai.com/docs) - Google AI (Gemini) docs (https://ai.google.dev/) - Azure OpenAI Service (https://learn.microsoft.com/azure/ai-services/openai/) 4) Pipe events into a centralized store - Open-source observability path: - OpenTelemetry Collector → Elasticsearch + Kibana or Grafana Loki + Grafana - Elasticsearch (https://www.elastic.co/elasticsearch/) [...]

December 24, 2025

[...] om/) - Splunk (https://www.splunk.com/) - New Relic (https://newrelic.com/) - Product analytics/warehouse path (great for funnels, cohorts, experimentation): - Event router/CDP: Segment (https://segment.com/) or RudderStack (https://www.rudderstack.com/) - Warehouse: BigQuery (https://cloud.google.com/bigquery) or Snowflake (https://www.snowflake.com/) - BI: Metabase (https://www.metabase.com/) or Apache Superset (https://superset.apache.org/) - Product analytics: Mixpanel (https://mixpanel.com/) or Amplitude (https://amplitude.com/) 5) Add an LLM observability layer for prompt/response [...]

December 24, 2025

[...] ch.html - Azure Monitor — Metrics/logs with flexible alert rules for AI workloads. https://azure.microsoft.com/products/monitor/ - Azure Machine Learning — Managed ML platform with monitoring integrations and alerting through Azure Monitor. https://azure.microsoft.com/products/machine-learning/ - Google Cloud Monitoring (Ops) — Time-series monitoring and alerting; integrates with AI services. https://cloud.google.com/monitoring - Vertex AI Model Monitoring — Drift/performance monitors with alerting via Cloud Monitoring. https://cloud.google.com/vertex-ai/docs/model-monitoring/overview ML/LLM ob [...]

December 24, 2025

[...] roducts/monitor/ - Azure Machine Learning — Managed ML platform with monitoring integrations and alerting through Azure Monitor. https://azure.microsoft.com/products/machine-learning/ - Google Cloud Monitoring (Ops) — Time-series monitoring and alerting; integrates with AI services. https://cloud.google.com/monitoring - Vertex AI Model Monitoring — Drift/performance monitors with alerting via Cloud Monitoring. https://cloud.google.com/vertex-ai/docs/model-monitoring/overview ML/LLM observability platforms (production ML/LLM performance, drift, data quality, cost, and user metrics with alerting) [...]

December 24, 2025

[...] ttps://azure.microsoft.com/products/machine-learning/ - Google Cloud Monitoring (Ops) — Time-series monitoring and alerting; integrates with AI services. https://cloud.google.com/monitoring - Vertex AI Model Monitoring — Drift/performance monitors with alerting via Cloud Monitoring. https://cloud.google.com/vertex-ai/docs/model-monitoring/overview ML/LLM observability platforms (production ML/LLM performance, drift, data quality, cost, and user metrics with alerting) - WhyLabs — ML/LLM observability (whylogs) with real-time data quality and drift monitoring + alerts. https://whylabs.ai/ - Arize A [...]

December 24, 2025