Tidepool by Aquarium
Tidepool by Aquarium: Analytics for AI Text Interfaces
Tidepool is a product analytics platform built for AI text interfaces, using embeddings to uncover patterns in unstructured conversation data without writing a single line of code.
Overview
Tidepool by Aquarium tackles a problem that traditional analytics tools weren't built for: understanding what users actually say inside AI-powered text applications. Instead of relying on predefined events or manual log digging, Tidepool automatically categorizes conversations, surfaces recurring themes, and connects text-based attributes to core product metrics. This gives teams a clear window into user intent, popular topics, language preferences, and behaviors that might otherwise stay buried in raw text.
Built for teams shipping conversational AI products, Tidepool plugs into your existing data stack—whether that's an SDK, CDP, or reverse-ETL pipeline—and turns messy, unstructured interactions into structured, explorable insights. Its visual interface lets product managers, researchers, and developers drill into conversations and trends without needing SQL or data science expertise, making it easier to spot which user actions could be automated into shortcuts or new features.
Ultimately, Tidepool acts as a discovery engine for AI-first products, helping teams move beyond guesswork and make data-informed decisions about what users need next.
Capabilities & Features
- Product analytics
- AI text interfaces
- Natural Language Processing (NLP)
- Text analytics
- User behavior analysis
- Data visualization
- Embeddings
- Machine Learning
Core Features
- Automated insight generation powered by embeddings
- No-code visual interface for exploring text data
- Automatic categorization of user interactions and topics
- Trend tracking across user activity over time
- Correlation between text attributes and key product metrics
- Flexible integration via SDK, CDP, or reverse-ETL
Use Cases
- Understanding how users genuinely engage with an AI text product beyond surface-level metrics
- Identifying the most common topics, questions, or languages appearing in user conversations
- Pinpointing repetitive user actions that could be turned into automated shortcuts
- Informing roadmap decisions with real behavioral patterns instead of assumptions
- Supporting UX research by surfacing qualitative themes at scale
Best For
- Product managers
- Data scientists
- UX researchers
- AI application developers
- Conversational AI teams
Pros
- •No coding or SQL required to explore complex text data
- •Automated categorization saves significant manual analysis time
- •Flexible integrations fit into most existing data stacks
- •Helps translate unstructured conversations into actionable product decisions
- •Purpose-built for the unique challenges of AI text interfaces
Cons
- •May require adjustment for teams used to traditional event-based analytics tools
- •Effectiveness depends on quality and volume of text interaction data
- •Lacks publicly available pricing, requiring direct inquiry for cost details
- •Best suited for text-heavy AI products, less relevant for non-conversational apps
How to Use
1. Connect Tidepool to your existing data infrastructure using an SDK, CDP, or reverse-ETL integration.
2. Let Tidepool automatically process and categorize incoming text interactions using embeddings.
3. Explore the visual dashboard to review trends, clusters, and categorized conversation topics.
4. Drill down into specific conversations to understand context and user intent.
5. Correlate text attributes with product metrics to identify opportunities for new features or shortcuts—no coding required.
Frequently Asked Questions
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Pricing
Pricing details for Tidepool are not publicly listed, so interested teams will need to contact the company directly for a custom quote.
Pricing data is provided as a summary. Visit the vendor website for full tier details.