Isonapse vs Kanlet
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Isonapse compared with Kanlet
| Detail | 
Isonapse | 
Kanlet |
|---|
| What it does | The governance layer for AI agents
| Semantic context layer that helps AI agents understand your business data accurately
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| Stage | MVP
| MVP
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| Founded | 2026
| Not given
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| Based in | CH
| Not given
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| Pricing model | Not given
| Not given
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| Pricing | Agent Hook for Claude Code (public beta, available now), Community Edition (public beta, early September 2026, free), Enterprise Edition (Q4 2026)
| Not given
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| Key features | - Policy-based rule definition for agent actions
- Checkpoint enforcement before every action
- Tamper-evident signed audit ledger
- Vendor-neutral across models and harnesses
- Deterministic decision-making without LLMs
- Offline enforcement without network connectivity
- Budget caps and cost control
- Data tokenization and secret placeholder support
- One-click installation for Claude Code
| - Connects to 15+ data systems including Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI and Postgres
- Extracts structural metadata including databases, schemas, tables, views, columns and relationship lineage
- Provides Web-UI, MCP server and REST APIs for accessing semantic context
- Automatic incremental updates with configurable sync schedules from on-demand to daily or weekly
- Role-level access controls and data classifications including PII, PHI and SPII
- Only reads metadata without accessing or moving actual data from systems
- Integrates with quality monitoring tools including Monte Carlo, Anomalo and Bigeye
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| What makes it different | - Neutral layer enforcing policies across any agent, model, or harness
- Self-hosted on your hardware with no telemetry or cloud dependency
- Cryptographically verified audit trail you can check offline
| - Eliminates confident AI mistakes by grounding agents in semantic context instead of having them guess from table names
- Reduces AI token costs and speeds up answers by resolving schema and joins once upstream instead of in each agent query
- Ensures consistent answers across all agents and teams using the same definitions and business logic
- Provides complete traceability and auditability of AI decisions with documented context and ownership
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| Free plan | Not given
| Not given
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| Open source | Not given
| Not given
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| Platforms | Not given
| Not given
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| Public API | Not given
| Not given
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 5 ▲ +5 (up) since last check | 8 ● no change since last check |
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| Trust Flow (Majestic) | 9 | 1 |
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| Citation Flow (Majestic) | 24 | 24 |
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| Referring domains (Majestic) | 64 | 29 |
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4 details filled in for both companies.
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