# Brizz

> Product analytics platform for AI agents. Turn every conversation into actionable insights that accelerate product decisions, improve agent performance, and drive measurable impact.

Brizz is the analytics layer for AI agents. It provides visibility into agent behavior, user intent, and quality issues across every conversation. Unlike traditional analytics (clicks and pageviews) or observability tools (logs and latency), Brizz is built for AI product managers and AI teams.

## Key Capabilities

- Conversation analytics across all AI agent interactions
- Agent behavior tracking and performance insights
- User intent analysis and quality issue detection
- Product decision acceleration through data-driven insights
- OpenTelemetry-compatible SDK for easy integration

## Integration

Add a few lines of code to integrate. Brizz uses an OpenTelemetry-compatible SDK that fits into existing agent stacks. Supports Slack integration for real-time alerts and insights.

## Security & Compliance

SOC 2 Type II certified, ISO 27001 certified, HIPAA compliant, GDPR and CCPA compliant.

## Pages

- [Home](https://www.brizz.ai/): Main landing page with product overview
- [Pricing](https://www.brizz.ai/pricing): Plans and pricing information
- [Contact](https://www.brizz.ai/contact): Get in touch for early access or inquiries
- [Slack Integration](https://www.brizz.ai/slack): Slack app integration details
- [Privacy](https://www.brizz.ai/privacy): Privacy policy

## Blog

- [Blog](https://www.brizz.ai/blog): Insights on AI agent analytics, product management for AI, and building better AI products
- [Conversational AI vs Search Bar: Building the Future of Retail](https://www.brizz.ai/blog/conversational-ai-vs-search-bar): Amazon is replacing search bars with conversational AI. Learn what this format shift means for AI builders and how to measure conversational agent success.
- [AI Agent Error Handling: Why Strict Tool Standards Matter](https://www.brizz.ai/blog/ai-agent-error-handling): Fix silent failures with a strict AI agent error handling contract. Learn how structured tool errors improve debugging and observability for LLM agents.
- [RAG Retrieval Optimization: The #1 Mistake AI Builders Make](https://www.brizz.ai/blog/rag-retrieval-optimization-mistakes): Upgrading your LLM to fix hallucinations is a costly trap. Learn how RAG retrieval optimization and key evaluation metrics solve the real bottleneck.
- [AI Product Management: Fixing Silent Model Degradation](https://www.brizz.ai/blog/ai-product-management-fixing-silent-model-degradation): Stop AI model drift. Learn why AI product management requires continuous evaluation of probabilistic systems to prevent silent failure and user churn.
- [AI Agent Analytics: Why Traditional SaaS Metrics Fail](https://www.brizz.ai/blog/ai-agent-analytics-traditional-saas-metrics-fail): Stop slicing AI agent data by industry or ARR. Learn how intent-based AI agent analytics surface real product gaps and improve agentic workflow performance.
- [LLM-as-a-Judge Cost: Surviving the Production Compute Tax](https://www.brizz.ai/blog/llm-as-a-judge-cost): Scaling AI agents? LLM-as-a-judge costs can destroy your unit economics. Learn 4 strategies to optimize token usage and reduce LLM evaluation cloud spend.
- [Continuous Evaluation for AI: Why Traditional QA Fails](https://www.brizz.ai/blog/continuous-evaluation-for-ai-vs-traditional-qa): Stop silent AI degradation. Learn why continuous evaluation for AI is replacing traditional QA to monitor semantic drift and maintain LLM product quality.
- [LLM as a Judge: Why Eval Scores Fail Your AI Roadmap](https://www.brizz.ai/blog/llm-as-a-judge-trap): LLM as a judge is great for regression testing but terrible for discovery. Learn why high eval scores hide flat retention and how to surface real user intent.
- [Introducing the Brizz MCP Server: Your AI Analytics, One Question Away](https://www.brizz.ai/blog/introducing-brizz-mcp-server): Ask Brizz anything about your AI product — directly from Claude, Cursor, or any MCP client. No dashboards, no digging, just answers.
- [Mission Control for Claude Code: How to Manage 10 Agents Without Losing Your Mind](https://www.brizz.ai/blog/mission-control-for-claude-code): Stop tab-switching and start orchestrating. fleet is a terminal mission control for managing parallel Claude Code sessions.
- [Introducing Brizz: Product Analytics for AI Agents](https://www.brizz.ai/blog/introducing-brizz): Traditional analytics stop at clicks and pageviews. Learn how Brizz helps teams understand AI agent behavior, improve quality, and make data-driven product decisions.

## Platform

- [Brizz Platform](https://platform.brizz.dev/): Access the Brizz analytics dashboard

## Documentation

- [Docs index for LLMs](https://docs.brizz.ai/llms.txt): Curated index of every Brizz doc page — SDKs, instrumentation, platform, integrations, CLI, API reference
- [Full docs corpus](https://docs.brizz.ai/llms-full.txt): Every doc page inlined as one markdown document
- [Machine-readable docs index](https://docs.brizz.ai/docs-index.json): Doc slugs, titles, descriptions and sections as JSON
- [Install the SDK](https://docs.brizz.ai/docs/get-started/install.md): Install the Brizz SDK in a Python or Node.js project
- [Send your first session](https://docs.brizz.ai/docs/get-started/first-session.md): Initialize the SDK and capture your first session
- [API overview](https://docs.brizz.ai/docs/api/overview.md): Brizz HTTP API — telemetry ingestion, reading data, webhooks