Dipole ML Inc. vs Continuous Intelligence
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Dipole ML Inc. compared with Continuous Intelligence
| Detail | 
Dipole ML Inc. | 
Continuous Intelligence |
|---|
| What it does | Canada's sovereign frontier AI lab building the Raven models and DCode
| Stream-native AI for real-world time series data
|
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| Stage | Startup
| Startup
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| Founded | 2026
| 2026
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| Based in | CA
| GB
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| Pricing model | Freemium
| Not given
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| Pricing | Raven Free (free forever with automatically replenishing limits), Pro ($20/month or $200/year, Founder $160/year), Ultra ($100/month or $1,000/year, Founder $800/year). DCode Coding Plan $29/month (Founder $230/year). API: Raven Flash $0.50 per 1M input tokens, $2.50 per 1M output tokens; Raven Max
| Not given
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| Key features | - Native multimodal vision for diagrams, schematics, and error logs
- Autonomous tool execution with structured receipts
- Inspectable reasoning streams in real time
- 1M token context length
- OpenAI and Anthropic SDK compatibility
- Automatic prompt caching with up to 90% input cost savings
- Model Context Protocol (MCP) support
- Canadian sovereign compute and PIPEDA compliance
- Real-time streaming and sub-second latency
- Prepaid credits that never expire
| - Anomaly detection and correlation discovery across multiple data streams
- Real-time data stream monitoring to flag potential problems
- Forecasting by understanding interactions between data streams
- Natural language querying with context and assumptions
- ci-proto frontier model with improved forecasting accuracy
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| What makes it different | - Independent frontier AI built and served from Canada with statutory PIPEDA data privacy
- Inspectable reasoning streams for transparent, auditable AI decisions
- #1 ranked on Terminal Bench, Agent's Last Exam, and AutomationBench benchmarks
- Bootstrapped from first principles without venture capital
- Unified continuous architecture across multiple frontier models
| - Stream-native architecture designed specifically for time series data
- Foundation models that eliminate context window limitations
- Continuous-time machine learning approach
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | Not checked yet ● no change since last check | 2 ● no change since last check |
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| Trust Flow (Majestic) | 0 | 0 |
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| Citation Flow (Majestic) | 15 | 12 |
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| Referring domains (Majestic) | 1 | 1 |
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6 details filled in for both companies.
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