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Raindrop monitors AI applications for silent failures by analyzing user interactions and surfacing issues like forgetfulness, vague responses, and task failures. It allows engineers to track problems using natural language and provides direct links to affected conversations for rapid debugging. Advanced features support topic clustering, signal analysis, and dataset creation for deeper insights into AI behavior.
Why Traditional Monitoring Falls Short in AI
Conventional software tools detect failures through clear, structured exceptions. Errors are flagged, logged, and surfaced in tools like Sentry. AI products behave differently. Instead of throwing exceptions, they fail silently. These failures often pass undetected by traditional observability systems.
Raindrop highlights this gap by pointing to public failures: Figma had to roll back its AI design feature after it generated outputs resembling Apple’s designs. Air Canada lost a lawsuit when its chatbot incorrectly promised a refund. Virgin Money’s bot scolded users for mentioning the word “Virgin.” These are not code crashes, but behavioral failures—unstructured, context-sensitive, and difficult to trace.
AI engineers are left sifting through millions of logs or chasing flaky evaluation tests that don’t reflect production behavior. Raindrop’s core premise is that evals are not enough. Like unit tests, they may validate edge cases, but they miss the unpredictable and context-driven failures users encounter daily.
What Raindrop Actually Does—and Why It Matters
Co-founded by Alexis Gauba, Ben Hylak, Zubin Singh and KM Koushik, Raindrop detects and reports issues when AI misbehaves in production. It integrates into AI-powered applications and automatically identifies problems across interactions, surfacing patterns in how users respond and where AI performance degrades.
The platform monitors:
- Forgetfulness
- Vague or lazy responses
- Task completion failures
- User frustration signals
Raindrop doesn’t just capture failures. It also logs wins—positive behaviors that lead to helpful, efficient, or well-received user outcomes. This dual tracking allows teams to reinforce what works and investigate what doesn’t.
Alerts are sent through Slack and contain direct links to event traces, which allow engineers to quickly investigate the root cause. Instead of relying on logs, teams are routed straight into conversations and traces that led to the issue.
How Raindrop Tracks “Invisible” AI Failures in Production
Raindrop collects real-time metrics on interaction volume, user activity, and issue detection. For example, a daily summary may include message counts, user growth, and the number of issues found across interactions. The system flags top problems and provides excerpts from affected conversations.
Examples of detected issues include:
- Context retention failures
- Increased rates of assistant forgetting
- Unclear or incomplete answers
- Unwanted response patterns like filler words
Raindrop attaches real user quotes to these detections, offering qualitative insights. Comments like “It forgets what we talked about just 30 min ago” or “It stopped mid-sentence without warning” provide immediate clarity into user experience problems.
Each issue links to a corresponding event trace, letting engineers review exactly what happened in the context of the full conversation.
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AI Debugging Becomes Easier with Natural Language Queries
Raindrop enables engineers to track issues using plain English. Instead of writing logic or filters, they describe problems directly:
- “Users complaining about the AI’s generated code”
- “The assistant using filler words like ‘tapestry’”
- “Users saying the bot forgot something”
Raindrop processes these descriptions and automatically tracks behavior patterns that match. This natural language interface reduces manual investigation and allows teams to rapidly iterate on issue discovery.
Pro Features That Take Monitoring to the Next Level
The Pro tier expands the monitoring capabilities to meet enterprise and research needs. These include:
- Custom Issues/Topics: Define specific issues that matter to your product and track them continuously
- Topic Clustering: Group conversations and interactions based on topic similarity to understand key use cases and issue hotspots
- Signals: Surface trends from thumbs up/down, regenerations, or similar user actions
- Deep Research: Use natural language search over production data to find behaviors, test edge cases, or explore emerging patterns
- Traces: View the full stack of every AI interaction for root cause analysis
- Edge PII Redaction: Automatically remove personally identifiable information from user inputs and model outputs
- Dataset Creation: Convert events into structured datasets for analysis or model fine-tuning
These features aim to transition AI observability from reactive issue fixing to ongoing product intelligence.
What Top AI Teams Say After Using Raindrop
Clay.com uses Raindrop to identify patterns in user behavior and align product development with real-world usage. According to their team, insights from Raindrop directly inform their improvement roadmap.
At New Computer, Raindrop has been described as “an invaluable partner” in identifying bugs and verifying whether recent changes are effective. The platform supports bulk-level analysis, helping teams respect user privacy while maintaining visibility.
Atlas credits Raindrop with helping prioritize product decisions, especially across international markets. Features like translation tracking have been critical to understanding a global user base.
Tolans emphasizes Raindrop’s role in keeping issue rates low during fast growth. Its CTO compares the tool to iOS crash reports—but applied to AI behavior.
Unstuck uses Raindrop to find hard-to-detect bugs and better understand how users engage with their agents. Daily issue summaries influence engineering priorities.
The AI Observability Tool Every Engineer Starts to Rely On
AI systems are inherently unpredictable. Their behavior shifts based on input context, training data, and user feedback loops. Without observability, issues fester unnoticed or surface only after they become public failures.
Raindrop offers a structured way to monitor, trace, and understand how AI products behave in real-world conditions. By combining natural language tracking with live data inspection, the platform helps teams ship faster, safer, and with greater confidence in their AI performance.
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