Quaeris is built on a governed semantic layer, trusted AI agents, and enterprise-grade security controls - so every answer your team gets is accurate, auditable, and role-scoped. Architecture is not a marketing claim. It is what prevents hallucinations.
Warehouse-native Your data never leaves your environment
Prompt-level audit trail Every question, every answer, logged
BYOM Connect OpenAI, Anthropic, Google, or Meta
From a plain-language question to a board-ready answer - every step certified, every metric traceable, every result grounded in your warehouse.
Quaeris Trusted Agents translate any business question into a query that runs against your certified semantic layer - returning an answer with a full trace to its source, never a hallucination.
The Governed Semantic Layer is the single source of truth that every agent, every dashboard, and every export reads from - eliminating the spreadsheet wars that undermine board-level decisions.
Governance is not a feature you bolt on - it is the architecture. Quaeris logs every question asked, every metric resolved, every role check applied, and every result returned, with immutable audit trails ready for compliance review.
Quaeris runs compute inside your existing warehouse - Snowflake, BigQuery, Redshift, or Databricks - so there is no data copy, no egress charge, and no shadow data store to secure.
Connects to your existing stack
Most AI analytics tools apply governance as a permission layer on top of an existing BI stack. Quaeris encodes it at three levels simultaneously - in the semantic layer that defines your metrics, in the agents that answer questions, and in the security controls that scope every query. Each layer reinforces the others.
Your data team defines metrics once - revenue, activation rate, churn - inside Quaeris's Smart Semantic Layer. The layer auto-learns business definitions from user interaction; no upfront LookML or MDX modeling sprint is required. Every agent answer traces back to these certified definitions. Conflicting numbers across teams stop being a problem.
Deep diveBusiness users ask questions in plain language. Quaeris agents plan and execute multi-step analyses - fetch, filter, join, forecast, anomaly-detect, root-cause - without a human in every step. Critically, agents query the governed semantic layer, not raw tables or a language model's memory. Every number is traceable to a certified metric.
Deep diveRole-based access controls are enforced at query time, not as a dashboard filter. Quaeris runs warehouse-native - your data never leaves your environment. Every query, every agent step, and every answer is logged in a full prompt-level audit trail. Bring your own model: connect OpenAI, Anthropic, Google, or Meta and switch as your compliance posture evolves.
Deep diveA transparent pipeline. No black boxes, no unexplained numbers, no hallucinations.
Quaeris connects directly to your existing Snowflake, BigQuery, Databricks, Azure Synapse, or Amazon Redshift instance. No data copying, no pipelines to rebuild. SharePoint and Google Drive connect for document ingestion alongside structured warehouse data.
See supported warehouses →Your data team defines certified metric definitions, business rules, ownership, and lineage. The Smart Semantic Layer also auto-learns business context from user interaction - reducing the upfront authoring burden that every other semantic-layer product requires.
Explore the semantic layer →Business users type questions as they would to a colleague. Quaeris agents interpret the question, traverse the semantic layer, plan multi-step analysis where needed, and return a precise, source-cited answer. Data and Document Agents can join structured and unstructured data in a single query.
Book a Demo →Every answer shows the metric definitions it used and the agent steps it took. Role-based access controls enforce what each user can see - at query time, not dashboard level. The full prompt-level audit trail logs who asked what and when, satisfying internal governance and emerging obligations such as the EU AI Act.
Quaeris is not a single feature wrapped in a product shell. Six integrated capabilities work together - and each one is governed by the semantic layer beneath it.
Plain-English questions translate into precise, governed SQL - checked against the semantic layer, not generated freeform. No query goes to the warehouse without passing through certified metric definitions.
Agents plan and execute analyses that span multiple steps - fetch, filter, join, forecast, anomaly-detect, root-cause - without requiring a human to hand off each stage.
Forecasts, anomaly flags, root-cause diagnosis, and proactive alerts surface issues before they reach the board. The agent tells you what changed and why, not just what the current number is.
Auto-learns business definitions and data relationships from user interaction. No upfront LookML or MDX modeling sprint. The layer improves as usage grows and is controlled by your data team.
Connect OpenAI, Anthropic, Google, or Meta models and switch as the landscape evolves. Quaeris is not the model gatekeeper. Your compliance team chooses the model; the governed layer stays constant.
Extract structured fields from contracts, invoices, and resumes - then join with warehouse fact tables in a single natural-language query. Structured and unstructured data as co-equal citizens in one governed query.
Quaeris organizes its capabilities into three named engines. Each engine is a cluster of sub-capabilities with its own URL, so your team can navigate directly to the feature that serves their workflow.
The Context Engine is where questions enter the system. Natural-language queries are parsed, mapped to the semantic layer, and enriched with the business context the Smart Semantic Layer has learned. This is where NL-to-SQL translation happens - checked, not generated. Sub-capabilities: Ask · Integrate · Activate.
The difference is not the language model. It is whether governance is encoded at the architecture level or applied as a filter on top.
Power BI Copilot is Azure-OpenAI bound. Tableau leans Einstein. Snowflake Cortex Analyst locks you to one warehouse and one model. When the compliance landscape changes, you cannot switch.
Quaeris answerQuaeris is not the model gatekeeper. Connect OpenAI, Anthropic, Google, or Meta - and switch as your compliance posture or cost math evolves. The governed layer underneath stays constant regardless of which model sits above it.
Cube, dbt Semantic Layer, AtScale, and Honeydew all require your team to author semantic definitions upfront - LookML, SML, or MetricFlow YAML. That is weeks of modeling before users can ask a single governed question.
Quaeris answerQuaeris's Smart Semantic Layer learns business definitions and data relationships from user interaction. It reduces the upfront authoring sprint every other semantic-layer product demands. Your team defines the rules; the layer handles the learning.
Tableau, Power BI, Looker, Qlik, and Sigma are structured-data-only. Glean and Hebbia are document-only. Every workflow that spans a contract and a warehouse fact table requires manual extraction and a second query tool.
Quaeris answerQuaeris's Document Agents extract structured fields from contracts, invoices, and reports, then join them with warehouse data in a single natural-language query. Structured and unstructured data are co-equal citizens in the same governed system.
Enterprise teams across financial services, SaaS, and insurance are getting governed, auditable answers from Quaeris - without moving their data.
Head of Analytics, Regional Bank
Replaced a manual reporting process that required three analyst-hours per request. Business users now ask questions directly and receive governed, source-cited answers within seconds - with a full audit trail for compliance review.
VP of Operations, Property Management Platform
Unified structured lease data with unstructured maintenance reports in a single governed query - eliminating the manual extraction step that previously required a separate tool and a half-day delay.
Chief Data Officer, Mid-Market Insurer
Met internal audit requirements for prompt-level logging of every AI-assisted query - a regulatory ask that legacy BI tools could not satisfy. The full audit trail now feeds directly into quarterly compliance reviews.
Every pillar has a dedicated subpage with technical detail, integration diagrams, and relevant FAQs. Pick the layer that matches your team's current priority.
How Trusted AI Agents plan and execute multi-step analyses - governed by the semantic layer at every step.
The Smart Semantic Layer: how certified metric definitions are built, auto-learned, and enforced across every query.
Full trace from plain-language question to source table - versioned, auditable, and ownership-stamped.
How query-time access controls scope data to exactly what each user's role permits - no dashboard-filter workarounds.
Why your data never leaves your environment, and how Quaeris connects without pipelines or data copies.
The full prompt-level audit trail: who asked what, which agent steps ran, which metric definitions answered.
Book a demo. We will connect to your warehouse, walk through the semantic layer setup, and return a governed answer against your own data - in 30 minutes.
