Quaeris agents turn plain-language questions into governed, audited answers. No hallucinations. No guessing. Every number traces back to a certified metric in your semantic layer.
Zero hallucinationsAnswers retrieved from certified metrics, never generated
Full audit trailWho asked, what was returned, and why - logged automatically
Role-gated accessUsers see only what their warehouse role permits - enforced at query time
Plain-language inputNo SQL required. Any business user can ask and get a governed answer
From plain-language input to a certified, source-cited, role-verified result - every step governed, every step auditable.
Type a question the way you'd say it in a meeting. The agentic query engine resolves your intent against the certified semantic layer and returns a single, unambiguous answer - not a list of dashboards to dig through.
When a question requires comparing periods, combining metrics, or identifying root causes, the engine plans and executes multiple sub-queries in sequence - then synthesises a single governed response, not a dump of disconnected numbers.
No answer leaves the engine uncited. Each result is stamped with the metric version, the certifying owner, the source table, and the audit-entry ID - so any reviewer can trace the number back to its origin without asking anyone.
Role-based access is enforced at the moment the query executes - not at export, not at dashboard publish. An analyst scoped to APAC sees APAC data; a Marketing user asking the same question is denied, logged, and returned zero rows.
Every Quaeris agent answer follows a governed, auditable execution path - from question to source in seconds.
User types a business question in plain language. The agent parses intent, maps the question to available metrics in your semantic layer, and routes it to the correct agent workflow. No free-form SQL generation.
Semantic layer mappedThe agent queries only certified, role-gated metrics from your warehouse. It verifies the query shape against your semantic layer before execution. If a question can't be answered, the agent says so rather than hallucinating.
Query validatedThe agent returns a precise answer with full source lineage: which metric definitions were used, which business rules applied, and which warehouse table was queried. Every answer is logged for compliance audit.
Audit log createdEvery other conversational BI tool relies on LLM creativity - it's fast but unreliable. When the model doesn't know an answer, it makes one up. Users don't know the difference between a retrieved fact and a fabricated one.
If a metric isn't defined, the agent says "I don't know" instead of guessing. Every number is grounded in a source. No training-data hallucinations. No free-form SQL. Just governed, auditable answers every time.
Six core capabilities, each constrained by your semantic layer. Agents don't go rogue - they're governed at every step.
Users ask questions in plain English. Agents translate to SQL constrained by your semantic layer - no model-generated code. Every query is validated against certified metrics before execution.
Agents don't stop at a single query. They plan and execute fetch-filter-join-forecast sequences without human input. Anomaly detected? Agents root-cause automatically. Forecast needed? Done in one prompt.
Agents forecast trends, flag anomalies, and diagnose root causes without being asked. A revenue dip is flagged before the executive standup. A warehouse query slowdown is surfaced before users notice.
Role-based access, data residency, audit trails - enforced at query time, not dashboard time. Users see only the data their role permits. Every answer is logged with who asked, what was returned, and why.
The semantic layer isn't static. It learns from agent interactions. Users define metrics once; agents use them everywhere. New business logic? Update the semantic layer once; all agent answers reflect the change immediately.
Revenue down 12%? Agents automatically investigate across regions, cohorts, campaigns, and product lines to find the driver - then surface the diagnosis with confidence scores. Manual troubleshooting replaced by agent rigor.
Most conversational BI tools bolt AI onto a dashboard. Quaeris builds agents on a governed semantic layer.
| Capability | Quaeris | Search-based BI | Warehouse-native AI | Cloud BI Assistant | Embedded LookML BI |
|---|---|---|---|---|---|
| Plain-language questions | Yes | Yes | Yes | Yes | Yes |
| Answers cited to metrics | Yes - every answer | Yes | Limited | Limited | Limited |
| Governed semantic layer | Auto-learns | Manual modeling | Requires YAML | Manual config | Requires LookML |
| Zero hallucinations | By design | Partial | LLM-dependent | LLM-dependent | LLM-dependent |
| BYOM (pick your LLM) | Swap anytime | Model-locked | Warehouse-locked | AWS-locked | Google-locked |
| Multi-step agent workflows | Full fetch/filter/join/forecast | Limited | Limited | Limited | Limited |
| Autonomous anomaly detection | Yes | No | No | No | No |
| Full audit trail (who/what/why) | Yes - prompt + steps | Limited | Limited | Limited | Limited |
Comparison based on publicly available product documentation. Data current as of Q2 2026. See detailed comparisons →
User question: "What was revenue last quarter by region, and which regions missed their plan?" Follow the agent's execution path below.
All entities are mapped to certified metrics in the semantic layer. The agent does not proceed to generate SQL until mapping is confirmed complete.
Mapping complete - 0 ambiguous entitiesThe agent retrieves the certified Revenue metric definition from the semantic layer. Version 3.2 is the current approved definition. The user's role grants full regional access.
Metric v3.2 - certified, access grantedSQL is generated internally and validated against the semantic layer. The user never sees or edits the SQL - governance is enforced by the agent, not trusted to the user.
Query validated - executed in 14s"Last quarter, all regions combined achieved $3.23M revenue. West overperformed by 8%; Central underperformed by 3%; East hit target; South missed by 11%."
This answer cites the Revenue metric (v3.2), uses 3 warehouse tables (transactions, regions, plans), and applies role-based access for region visibility.
Full audit trail - View trace →Every role gets governed self-serve access - without compromising the semantic layer your data team owns.
Self-serve exploratory analysis without writing SQL. Agents handle the query mechanics; analysts focus on interpretation and action.
Instant answers to business questions - no tickets, no waiting. Governed answers without needing to understand the underlying data model.
Proactive insights without waiting on reports. Agents surface anomalies before the standup and flag underperformance before it becomes a board issue.
Governance enforcement and semantic layer ownership. Agents expose metric usage patterns, flag definition inconsistencies, and help prioritize semantic layer expansion.
Dashboard metrics are illustrative. Book a Demo to see your data.
Three teams that deployed Quaeris agents and measured the outcome.
Reduced ad-hoc analytics requests by 84% while expanding self-serve access to 600+ business users - without relaxing governance. Connected agents to Snowflake, migrated 120 certified metrics, deployed with row-level security.
Cut time-to-insight from 3 days to under 20 minutes - eliminating the request backlog that had blocked product launches. Agents answer product-usage and pipeline questions with sources cited in every response.
Unified conflicting metric definitions across 6 regional teams - giving executives a single, consistent view. Audited 300+ metric variants, consolidated to 80 certified definitions, retired four legacy BI dashboards.
Book a demo. We'll walk through your warehouse setup, show you how agents work with your data, and deliver a live governed answer in 30 minutes.
One practical read on governed analytics, agents, and semantic layers - every Thursday. No generic AI hype. Just rigorous patterns from data leaders deploying agents at scale.
