Raindrop

Building AI products like chatbots and agents presents unique challenges. Unlike traditional software that throws predictable errors, AI often fails silently. Your users might experience frustration, receive inaccurate information, or see unexpected behavior, but you might not know until it's too late – or unless you manually sift through mountains of logs. Standard testing and evaluations confirm specific cases work, but they don't capture the unpredictability of real-world user interactions.

Raindrop provides the crucial layer of monitoring specifically designed for the nuances of AI. Think of it like Sentry, but purpose-built for AI behavior. We help you automatically detect when your AI misbehaves in production, sending you alerts with direct links to the problematic interactions. This allows you to understand the root cause quickly and confidently ship improvements.

Key Capabilities: See What's Really Happening

• 🚨 Detect AI-Specific Issues Automatically: Raindrop identifies common AI failure modes like the assistant forgetting context, exhibiting laziness, failing tasks, or causing user frustration. Know when and why your AI isn't meeting expectations.

• 📊 Understand User Feedback Patterns (Signals): Log explicit user signals like thumbs up/down or regenerations. Raindrop surfaces patterns in this feedback, showing you what resonates positively and what needs attention, helping you prioritize fixes and double down on wins.

• 💬 Receive Daily Insight Summaries: Get concise Slack notifications summarizing key events from the previous day, including detected issues (with trends) and positive highlights ("Wins"). Stay informed without getting overwhelmed.

• 🔍 Pinpoint Behaviors with Natural Language Search (Pro): Describe the behavior you're looking for in plain English (e.g., "users complaining about code quality," "assistant using filler words") and find relevant conversations or traces instantly.

• 📈 Discover Trends with Topic Clustering (Pro): Automatically group interactions into topics to see your AI's most popular use cases and identify which areas generate the most issues, guiding your product roadmap.

• 🕵️ Analyze Root Causes with Tracing (Pro): Track the step-by-step execution of your AI calls to understand precisely where things went wrong in complex interactions.

• 🏷️ Track What Matters Most with Custom Issues (Pro): Define and monitor any specific issue or topic unique to your application or business needs.

• 🔒 Protect User Privacy with Edge PII Redaction (Pro): Automatically identify and redact personally identifiable information from user messages and model responses before logging.

• 📚 Build Better Datasets from Real Interactions (Pro): Select any set of events within Raindrop to easily create curated datasets for fine-tuning models or building more robust evaluations.

How Teams Use Raindrop

1. Debugging Elusive Chatbot Errors: A user reports your support bot gave incorrect policy information. Instead of searching logs, Raindrop alerts you to "Task Failure" or "User Frustration" issues. You click the alert, review the conversation trace, pinpoint the incorrect reasoning step or knowledge retrieval, and deploy a targeted fix. You then monitor that specific issue type in Raindrop to confirm the fix reduced its incidence.

2. Improving AI Agent Reliability: Your AI agent successfully completes tasks in testing, but users report inconsistencies in production. Raindrop's Topic Clustering reveals a specific, less-common task type has a high failure rate. Using Deep Research, you query for interactions related to that task, identify patterns in the failures (e.g., misunderstanding specific user phrasing), create a dataset from these examples, and use it to fine-tune the agent's understanding.

3. Validating Product Improvements: You've rolled out changes to reduce AI "laziness" (e.g., overly short or generic responses). You track the "Laziness" issue in Raindrop. The daily summaries and issue dashboards show a clear downward trend in these events post-deployment, giving you confidence the changes were effective in the real world.

Stop Guessing, Start Improving

AI doesn't have to be a black box in production. Raindrop provides the essential visibility to move beyond basic evals and understand how your AI actually performs with real users. Catch problems early, diagnose them quickly, validate your fixes, and build user trust by consistently improving your AI product based on concrete data, not just guesswork.