The context layer
your data agents
need.

Traditional semantic layers like Cube or dbt MetricFlow require data teams to manually define every metric, entity, and relationship. Kaelio goes further: it auto-generates a context layer that includes a built-in semantic layer plus schema, lineage, dashboard logic, and domain knowledge. If you already have a semantic layer, Kaelio ingests it and fills gaps. If you don't, Kaelio builds one. Either way, your data team reviews and governs the result, not builds it from scratch.

Data catalogs like Atlan and Alation help data teams organize and find metadata. They don't build a context layer or power data agents. Kaelio auto-generates a governed context layer from your warehouse, BI tools, and domain knowledge, combining semantic definitions with schema, lineage, and dashboard logic. Agents query this context layer to deliver trusted, sourced answers to business teams via Slack, Teams, or email.

No. Kaelio consumes dbt, not replaces it. If you use dbt, Kaelio ingests your models, metric definitions, and documentation into its auto-generated context layer. This makes your dbt investment more valuable by exposing governed metrics to data agents that business teams can query directly. If you don't use dbt, Kaelio builds the context layer directly from your warehouse schemas and BI tools.

The Deep Context Engine is how Kaelio auto-builds your context layer. It reads your warehouse schemas, BI dashboard logic, metric definitions, lineage, and domain knowledge, then generates structured semantic models: metrics, entities, and relationships. It continuously refines itself by learning from how teams query the data, suggesting new metrics and knowledge to fill gaps. Your data team reviews and governs the output.

Kaelio auto-generates metric definitions from your warehouse, dbt models, and BI tools, then surfaces them for your data team to review, approve, and enrich. As teams query the data, Kaelio identifies gaps and suggests new metrics and definitions to add. Each metric gets one governed definition with a clear source, so business teams always see consistent numbers.

Kaelio connects to Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, and more. For BI tools, it supports Tableau, Looker, Metabase, Power BI, Mode, Sigma, and more. It also connects to dbt Cloud, dbt Core, Fivetran, and 900+ other data sources and SaaS tools. No existing semantic layer required.

Agents hallucinate when they lack business context. Kaelio solves this by auto-generating a governed context layer from your warehouse, BI tools, and domain knowledge. Every agent answer is grounded in governed metric definitions, schema, and lineage. Every answer cites its source: which model, table, or dashboard the data came from. Your team can verify anything in seconds.

Yes. Kaelio supports role-based access controls with table, row, and column-level permissions that mirror your existing data governance. The data team controls exactly which metrics and data are available to which teams. Sensitive data can be excluded at any granularity, and all access is auditable.

Most data teams are up and running in under an hour. Connect your warehouse, point Kaelio at your dbt project or BI dashboards, and it starts auto-generating your context layer immediately. Your team reviews and governs the output. Business teams can start querying agents the same day.

Yes. Kaelio is SOC 2 and HIPAA compliant and uses 256-bit encryption at rest and in transit. Your data is never shared across customers or used to train models. We offer SSO, role-based access, and audit logs for enterprise deployments.

BI tools are powerful but require training and SQL knowledge to use effectively. Business teams still rely on the data team to build and maintain dashboards. Kaelio ingests your BI dashboard logic into its auto-generated context layer, then lets business teams ask questions in natural language and get sourced answers via data agents. It doesn't replace your BI tool. It makes the insights inside it accessible to everyone.

A context layer is what sits between your raw data stack and your data agents or analytics agents. It combines a governed semantic layer (metrics, entities, relationships) with schema, lineage, dashboard logic, and domain knowledge into one layer that agents can query. Without a context layer, agents hallucinate or return inconsistent answers. Kaelio auto-generates this context layer from your existing data stack, so your data team governs it and any agent can consume it.