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Pricing
Essential: $29 per seat/mo (billed annually) + $0.99 per Fin resolution
Advanced: $85 per seat/mo (billed annually) + $0.99 per Fin resolution
Expert: $132 per seat/mo (billed annually) + $0.99 per Fin resolution
Fin AI Agent (on existing helpdesk): $0.99 per Fin resolution (50 resolutions per month minimum)
Copilot: $29 per agent/mo (billed annually)
Proactive Support Plus: $99 /mo
No public pricing
No public pricing
No public pricing
Core features
- ✦Fin AI Agent for automated customer support
- ✦Omnichannel support (inbox, tickets, phone, help center)
- ✦AI-enhanced inbox for agent productivity
- ✦AI Insights & Reporting for performance optimization
- ✦Workflow automation with a visual builder
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦AI-powered code autocompletion
- ✦Context-aware code referencing and chat
- ✦Natural language code editing
- ✦Customizable AI code assistants
- ✦Zero-ETL data integration
- ✦Federated Query
- ✦Streaming Ingestion
- ✦Instant Replication with CDC
- ✦API to SQL conversion
- ✦NoSQL to SQL conversion
- ✦SQL to API conversion
- ✦Self-service Integration
- ✦Generate SQL with AI
Use cases
- →Providing instant customer service with Fin AI Agent
- →Managing customer inquiries across multiple channels
- →Automating support workflows to improve efficiency
- →Empowering support agents with AI-powered tools
- →Gaining insights into customer service performance
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Accelerate development with AI-powered autocompletion.
- →Improve code understanding with context-aware chat.
- →Refactor code using natural language instructions.
- →Query data directly from its source in real-time.
- →Process data wherever it is, blending data from different sources.
- →Ingest streaming data from Kafka, Segment, etc., into Peaka BI Table.
- →Replace nightly batch ingestion with real-time data access.
- →Treat every data source like a relational database by converting APIs to tables.
- →Use SQL to query NoSQL databases.
- →Query consolidated data and expose it with APIs.
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