Data

Your warehouse stays exactly where it is.

Quaeris queries your data where it already lives. Nothing is copied out, no migration is required to get started, and the model behind the answers can change without disturbing the governance built underneath it.

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Data Execution

Queries in Quaeris push down into your existing warehouse rather than pulling data out of it. A question against Snowflake, BigQuery, Databricks, Redshift, or Synapse runs where the data already sits, so no copy of your data has to exist anywhere else.

The relationships between tables are learned from how people actually query, rather than requiring a data team to hand-map every join in advance. Because there is no warehouse rebuild involved, teams can typically start using Quaeris within days of connecting a source, not months.

Warehouse-native execution

Push-down queries across Snowflake, BigQuery, Databricks, Redshift, and Synapse, with zero rows extracted.

Self-learning relationships

Join paths and business context are learned automatically from actual query patterns.

Deployment in days

No warehouse rebuild and no migration required before adoption can begin.

Integrations

Most enterprise data does not sit in one warehouse alone. It is spread across ERP systems, CRMs, and cloud platforms that each hold a piece of the picture. Quaeris connects to these systems directly, so a question can draw on SAP, Salesforce, or Workday data without a separate export process in between.

For teams building their own tools on top of Quaeris, REST APIs and connectors are available to integrate the platform into existing enterprise systems.

ERP connectors

SAP, Salesforce, Oracle, Workday, and Microsoft Dynamics 365, without copying data out.

Cloud warehouses

Snowflake, BigQuery, Databricks, Redshift, and Synapse, all queried in place.

API access

REST APIs and connectors for custom integrations into existing enterprise systems.

Model flexibility

Quaeris is not built around a single model provider. An enterprise can connect OpenAI, Anthropic, Google, or Meta models, and switch between them, without rebuilding the governance layer underneath. The certified definitions and access controls persist across a model change, because they are enforced by the platform rather than embedded in the model itself.

Data run through the platform is not used to retrain any model, and session data is deleted after use, so connecting a new data source does not add it to a training set anywhere.

Bring your own model

Connect OpenAI, Anthropic, Google, or Meta models and switch without rebuilding governance.

Model-agnostic architecture

Governance definitions persist across model changes with no retraining required.

No training on your data

Data is never used to retrain a model, and session data is deleted after use.

Connects to the systems already in place

Quaeris is built to sit alongside the infrastructure an enterprise already has, rather than asking a data team to rebuild around it.

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Details reflect Quaeris customer deployments and published materials. Specific integration availability should be confirmed with your account team.

Explore the rest of the platform

Intelligence

Agents that plan, query, and cite

Conversational queries, document agents, and proactive monitoring grounded in the semantic layer.

Governance

Access control, audit, and compliance

Role-based access, prompt-level audit trails, and certified metrics built for regulated environments.