Intelligence

Answers that act, not just answers that inform.

Quaeris's agents plan, query, and respond to questions across your warehouse and your documents, with every step grounded in certified definitions and every result traceable back to its source.

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Agents

An agent in Quaeris is a process that plans a path to an answer, runs the queries that path requires, and returns a result with its reasoning attached. There is no analyst waiting in the loop between the question and the response, and no query written by hand.

Questions can be asked the way a person would ask a colleague, in plain sentences rather than structured syntax. Quaeris translates that question into governed SQL, runs it against your warehouse in read-only mode, and returns a result tied to a certified metric definition rather than an improvised calculation.

Agentic engine

Plans a multi-step path to an answer and executes it without a person at each step.

Conversational queries

Questions asked in ordinary language are converted into governed SQL and run read-only.

Proactive monitoring

Agents watch certified metrics on a schedule and flag what changed before someone has to ask.

Search and discovery

Most enterprise questions do not live in one place. An answer about claims exposure might need a number from the warehouse and a clause from a filed contract. Quaeris searches both at once, so the person asking does not need to know in advance whether the answer sits in a table or a document.

The semantic layer underneath this search is not static. It learns business definitions from how people actually query, and every definition it adopts is certified and versioned before it is used in an answer, so the same question returns the same answer no matter who asks it.

Converged search

One question searches structured tables and unstructured documents together.

Document agents

Read and cross-reference PDFs, contracts, and filings, with access controls applied to every result.

Smart semantic layer

Learns business definitions from query patterns, then certifies and versions them before use.

Actions and alerts

Intelligence that only responds when asked misses the questions nobody thought to ask yet. Quaeris's agents also watch certified metrics on their own, flag values that fall outside expected thresholds, and deliver that notice to the inbox or Slack channel where the relevant team already works.

A pinboard lets a team keep the metrics that matter to their role visible at all times, with the same role-based access rules applied to a pinboard that apply to any other query.

Inbox and Slack alerts

Proactive notifications arrive where the team already communicates.

Anomaly detection

Agents flag outliers against certified thresholds without a person setting the alert manually.

Governed pinboards

Metrics that matter stay pinned and live, with role-based access enforced throughout.

Grounded in the semantic layer, not the model

Every agent in Quaeris draws from the same governed semantic layer that powers search, alerts, and reporting elsewhere in the platform. An agent does not have license to improvise a metric definition or invent a join path. If a certified definition does not exist for a term in the question, the agent says so rather than guessing.

This is what separates an intelligence layer built for regulated industries from a general-purpose chat interface bolted onto a data warehouse. The model can change. The definitions it is required to use do not, unless someone with the authority to certify a metric changes them deliberately.

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

Explore the rest of the platform

Data

Warehouse-native execution

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

Governance

Access control, audit, and compliance

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