Ainswer vs ModelRefs
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Ainswer compared with ModelRefs
| Detail |
Ainswer | M
ModelRefs |
| What it does | Does AI recommend your brand?
| The AI Reference Layer for Implementation Decisions
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| Stage | Not given
| Not given
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| Founded | Not given
| Not given
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| Based in | TW
| US
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| Pricing model | Paid
| Freemium
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| Pricing | Diagnostic: $499 USD one-time, delivered in 7 days. Monitor: $149 USD per month, billed monthly. Done-for-you: custom pricing.
| Free tier: 1,000 API calls per month. Starter: $19/month. Pro: $49/month. Business: $199/month. Builder: $19/month with 1,000 task executions. Scale: $49/month with 5,000 task executions.
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| Key features | - Track buyer prompts across 5 AI engines
- Measure AI Recommendation Share and competitor performance
- Weekly multi-run consistency scans with full logs
- Prioritized action plan with named targets
- AI-readability audit including schema and crawler access
- Competitor benchmark on the same buyer questions
- Source placements, comparison pages, and FAQ pages written for you
- Answer-shaped articles on your domain and original category research
- Email alerts when AI answers change
| - Evidence API with citations and governed benchmark runs
- Knowledge Graph connecting models, providers, benchmarks and workflows
- Rate limiting and monthly API allowances
- MCP server for agent integration
- Bearer token authentication
- Reference profiles and implementation context
- Status and methodology visibility for evidence gaps
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| What makes it different | - Measures real AI Recommendation Share across all major AI engines simultaneously
- Done-for-you service includes source placements and content creation
- Weekly dashboard tracks trends and consistency across repeated scans
- Free audit to test AI visibility before purchasing
| - Every benchmark score includes methodology limitations and claim scope so evidence cannot be presented without context
- Connected reference system organizing AI knowledge fragmented across announcements, docs and research
- All tiers access the same evidence base; differences are only in call volume and support
- Governance-aware methodology for compliance and risk review
- Free read access to all tutorial, reference and comparison content
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| Community votes | 0 | 0 |
| DR (Domain Rating by Ahrefs) | Not checked yet ● no change since last check | 1 ● no change since last check |
| Trust Flow (Majestic) | 18 | 2 |
| Citation Flow (Majestic) | 19 | 17 |
| Referring domains (Majestic) | 8 | 11 |
6 details filled in for both companies.
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