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Kanlet

Semantic context layer that helps AI agents understand your business data accurately

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Kanlet is a semantic context layer that unifies data and institutional knowledge so AI agents can understand and operate within your business with accuracy and

About Kanlet

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Kanlet is a semantic context layer for intelligent systems that sits between your business systems and AI stack. It unifies context from warehouses, BI tools, business systems, data platforms, unstructured content and tribal knowledge into a single graph that agents query in real time. By defining business logic, metric definitions, and organizational knowledge once in a unified semantic model, Kanlet ensures consistent, trustworthy answers across all AI workflows.

The platform brings together three core components: a metadata graph mapping data sources into a queryable layer, decision memory capturing tribal knowledge through decision traces, and semantic knowledge encoding ontology and glossary terms. This enables AI agents to understand data (what is happening), reason about business meaning (institutional knowledge and policies), route actions (available tools and workflows), and act through enterprise systems (CRM, ERP, ticketing, analytics).

  • enterprise ai
  • data governance
  • knowledge graph
  • metadata graph
  • context layer
  • semantic layer
  • ai agents

Features worth knowing

  • 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

Kanlet metrics

Scan results for kanlet.ai from Ahrefs (Domain Rating) and Majestic (links); arrows mark the change since the last scan.
MetricValue
Domain Rating8 no change since last check
Trust Flow1 no change since last check
Citation Flow24 no change since last check
Referring domains29 no change since last check
External backlinks45 no change since last check

Ahrefs Domain Rating scanned 26 Oct 2026, updated 26 Oct 2026. Main topic: Business (1). Majestic scanned 26 Oct 2026, updated 26 Oct 2026. Link stats via Majestic. Trust Flow, Citation Flow and Topical Trust Flow are trademarks of Majestic-12 Ltd.

Kanlet domain facts

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From the registry's public RDAP record, checked 26 Oct 2026, 18:50 UTC.
Domain age3 years
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RegistrarGoDaddy.com, LLC
DNSSECNot signed

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FAQ

What is Kanlet?
Kanlet is the semantic context layer for intelligent systems that sits between your business systems and AI stack. It unifies context from warehouses, BI tools, business systems, data platforms, unstructured content and tribal knowledge into a single graph that agents and analysts query in real time. Without one, every new agent deployment starts with months of manual context building; with one, every new agent inherits the organization's full institutional memory on day one.
Which systems does Kanlet connect to?
Kanlet connects to 15+ systems including Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI, and Postgres. Once connected, lineage, query history, BI semantics, tags, and quality signals flow in automatically through scheduled and event-based workflows with no manual mapping required. More database and application connectors are in the pipeline.
Does Kanlet access the actual data in my systems?
No. Kanlet only reads metadata such as schema names, table structures, column definitions, lineage relationships, ownership, and timestamps. Your actual data never leaves your systems, as Kanlet never queries the rows in your tables, never stores data payloads, and never moves data between systems.
How does Kanlet work alongside my existing data tools?
Kanlet layers on top of your existing data stack and can run alongside tools like Microsoft Purview, Snowflake Horizon or Databricks Unity Catalog, pulling metadata from all into a unified context layer rather than rebuilding from scratch. Built on open APIs and Iceberg-native formats, context stored in Kanlet stays portable and is not locked to any vendor's proprietary schema.
How does Kanlet help AI agents to be more reliable and useful?
Once the Semantic Context Layer is defined, AI agents can understand what is happening in the enterprise (data, metadata, entities, relationships, business definitions), reason about business meaning (institutional knowledge, prior decisions, policies, business rules, exceptions), route how actions are performed (available tools, APIs, workflows, permissions, dependencies, procedures), and act to execute decisions through enterprise systems (CRM, ERP, ticketing, communication, analytics).

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