Querio
AI data analyst that turns plain English into SQL and investigates KPI changes
Last reviewed 2026-06-19
Querio is an AI-native analytics workspace built around a data copilot. It connects directly to a live data warehouse with encrypted, read-only credentials and translates plain-English questions into SQL (and Python), with every AI-generated answer exposing the underlying code for inspection. On top of ad-hoc querying it offers data notebooks, dashboards, and embedded analytics, and a semantic YAML layer to standardize metric definitions. Where Querio leans more agentic is its analysis features: an autonomous root-cause investigation that uses variance decomposition to rank the drivers behind a KPI change, and proactive 24/7 KPI monitoring that detects anomalies and writes narrative explanations. These run analysis automatically, but a human reads the findings and decides what to do, so it operates as a supervised analytical agent. Querio was founded in 2024 by Rami Abi Habib and Javier Bonilla, who met at Y Combinator Startup School, and has raised a reported ~$4.3M.
What it can do
Answer questions in natural language with visible code
AssistantTranslates plain-English questions into SQL and Python against a live warehouse, exposing the generated code for every answer so it can be inspected.
sourceRun autonomous root-cause investigation
SupervisedUses machine-learning variance decomposition to analyze a KPI change across a dataset and rank the drivers (region, product, channel) by impact.
sourceMonitor KPIs and explain anomalies 24/7
SupervisedRuns continuously to detect anomalies and generates narrative explanations within seconds, surfacing them for a human to review.
sourceStandardize metrics with a semantic layer
AssistantA semantic YAML layer defines consistent metric definitions reused across notebooks, dashboards, and embedded analytics.
source
Strengths
- +Read-only warehouse access with visible SQL/Python keeps answers auditable
- +Goes beyond querying with automated root-cause analysis and 24/7 anomaly monitoring
- +Semantic YAML layer enforces consistent metric definitions; SOC 2 Type II
Limitations
- −Young, small company (founded 2024) with modest funding
- −Autonomous analysis surfaces findings but a human still decides and acts
- −Variance-decomposition results depend on clean, well-modeled warehouse data
Overview
Querio is an AI-native analytics workspace centered on a data copilot. It connects to a live warehouse with read-only credentials and turns plain-English questions into SQL and Python, exposing the code behind every answer. It adds notebooks, dashboards, embedded analytics, and a semantic layer.
What it does
Beyond conversational querying, Querio runs autonomous root-cause investigations: variance decomposition analyzes a KPI change and ranks the drivers by impact. Proactive KPI monitoring runs 24/7, detecting anomalies and generating narrative explanations within seconds. A human reviews these outputs and decides on action, which is why it sits at the supervised-agent level.
Integrations & setup
Connects directly to Snowflake, BigQuery, Databricks, Amazon Redshift, ClickHouse, and PostgreSQL using encrypted, read-only credentials. SOC 2 Type II with row-level access controls, SSO, and audit trails. Available as SaaS or self-hosted.
Pricing
Subscription, reported around $25-$50 per user/month, with an enterprise annual option (reported ~$14,000/year, no seat limit). Confirm current pricing with Querio.
Best for / not for
Best for data and business teams that want conversational analytics plus automated root-cause and anomaly explanations on top of a live warehouse. Less suited to teams needing a mature, large-vendor BI stack or an agent that takes downstream actions.
Traction
Founded in 2024 by Rami Abi Habib (CEO) and Javier Bonilla (CTO), who met at YC Startup School. It has raised a reported ~$4.3M across seed and pre-seed rounds from investors including Forum Ventures, Haatch, and the LAUNCH Fund; figures are aggregator-reported.
Alternatives
Seek AI and Basedash target natural-language warehouse analytics; Hex and Julius focus on notebook-style AI analysis.
What people are saying
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FAQ
Is Querio's root-cause analysis fully autonomous?+
It runs the investigation automatically (variance decomposition that ranks KPI drivers) and writes explanations, but a human reviews the findings and decides what to do. It is a supervised analytical agent, not an autonomous actor.
Does Querio write to my database?+
No. It connects with encrypted, read-only credentials and surfaces generated SQL and Python for transparency.
Sources
- Querio (official site) · accessed 2026-06-19
- Top AI data analysis platforms 2026 (Querio) · accessed 2026-06-19
- Querio company profile (Crunchbase) · accessed 2026-06-19
Last reviewed 2026-06-19