Kore.ai vs Referee.chat
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Kore.ai compared with Referee.chat
| Detail | 
Kore.ai | 
Referee.chat |
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| What it does | Enterprise agentic AI platform helping businesses accelerate outcomes across customer service and employee productivity
| An AI panel debates your goal; a referee rules on re-checkable evidence
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| Stage | Not given
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| Founded | Not given
| Not given
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| Based in | US
| Not given
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| Pricing model | Not given
| Freemium
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| Pricing | Not given
| Free: $1.00 starting credit once. Credit plans from $10 top-up with no expiration. Your own key: 5% infrastructure fee after the first $5.00 of usage.
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| Key features | - Voice and digital AI agents
- Agent Blueprint Language (ABL™) for defining agent behavior and guardrails
- Autoloop™ optimization engine for continuous agent improvement
- ARCH™ AI solution architect
- StateTrace™ execution path tracking
- AI-native contact center capabilities
- Enterprise Search functionality
- Long-running process agents
- Human-in-the-loop workflows
- Multi-system process automation with audit trails
| - Panel of up to six AI models from different vendors
- Referee rules on re-checkable evidence only
- Searches academic databases: arXiv, OpenAlex, Crossref, Europe PMC
- Executes Python code to verify claims
- Customizable acceptance standards
- Matches pause and resume without losing work
- Shared ledger prevents re-derivation
- Spending limit per match
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| What makes it different | - Autoloop™ continuously optimizes agents against goals from build through production
- Purpose-built Agent Blueprint Language for formal agent definition and governance
- StateTrace™ and root cause analysis for identifying performance gaps
- Forward Deployed Engineers enabling production deployment in weeks
- Recognized leader by Gartner, Forrester, and Everest Group
| - Referee decides on evidence, not argument quality
- Panel from different vendors fails in different directions
- Stuck is a targeted question, not model thrashing
- Cost grows with ledger, not round count
- Published matches become research record
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 75 ● no change since last check | 2 ● no change since last check |
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| Trust Flow (Majestic) | 20 | 0 |
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| Citation Flow (Majestic) | 30 | 13 |
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| Referring domains (Majestic) | 3072 | 31 |
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3 details filled in for both companies.
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