Inteldo vs Taurus
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Inteldo compared with Taurus
| Detail | 
Inteldo | 
Taurus |
|---|
| What it does | Eight specialized AI agents research your question in parallel and deliver one clear answer you can act on
| Lead armies of AI agents sanely
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|---|
| Stage | Startup
| Not given
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| Founded | 2024
| Not given
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| Based in | SI
| Not given
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| Pricing model | Freemium
| Freemium
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| Pricing | Not given
| Free: $0 with 10 agents, 2 parallel runs, 2 GB storage. Start: $20/month with 100 agents, 5 parallel runs, 10 GB storage. Plus: $50/month with 300 agents, 12 parallel runs, 50 GB storage. Pro: $200/month with 1000 agents, 30 parallel runs, 250 GB storage. On paid plans, model usage billed at true pr
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| Key features | - Eight parallel specialist agents analyzing different business data domains
- Stripe and Google Ads revenue tracking with MRR and CAC analytics
- Google Analytics 4 and Search Console traffic analysis
- PostHog product analytics and funnel monitoring
- Competitor website analysis and pricing extraction at scale
- Real-time chat workspace with cited sources for all findings
- OAuth secure, read-only data connections
- Signal boards for ongoing metric monitoring
| - Hierarchical agent trees with parent-child delegation and coordination
- Container-isolated agent environments with Linux shell and persistent filesystem
- Persistent agent memory and context between runs
- Cron-based scheduled execution with overlap handling
- Shared /shared folder for cross-agent file collaboration
- Support for 30+ AI models from multiple providers
- Web IDE with terminal access and five editor themes
- Static site dashboards built and maintained by agents
- Voice input and file attachment support
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| What makes it different | - Eight specialized agents work in parallel instead of a single chatbot
- Every claim cited to live sources, not generated from training data
- Bootstrapped with roadmap driven by customer needs, not investor targets
- Data analyzed and discarded, never trained on models
| - Agentic tree structure where agents collaborate and share context unlike stateless tools
- Fully isolated Linux containers for each agent with persistent filesystems
- Persistent memory and context continuity across scheduled runs
- Mix and match 30+ AI models freely within the same team
- Pricing per fleet rather than per seat or per token overage
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 35 ▼ −1 (down) since last check | 31 ● no change since last check |
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| Trust Flow (Majestic) | 19 | 0 |
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| Citation Flow (Majestic) | 32 | 30 |
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| Referring domains (Majestic) | 25 | 115 |
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4 details filled in for both companies.
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