Metrics your team can trust.No upfront modeling.
Define revenue, churn, activation - once. Quaeris learns from usage patterns and agent questions, auto-building a certified semantic layer. Every answer traces back to those definitions. No LookML sprints. No manual metric catalog.
- Warehouse-native - scales to thousands of metrics
- Auto-learned from prompts - no coding required
- Certified by your team - versioned & audited
One metric definition.
Every team. Every tool.
The Quaeris Semantic Layer certifies, versions, and governs every business metric in one place so your data is trustworthy before anyone asks a question.
One definition of Revenue.
Finance, sales, and product stop arguing about numbers because there is only one certified metric to argue from. Quaeris publishes a single, approved definition for every KPI and routes all tools through it.
- Finance-approved logic is locked at the semantic layer, not buried in a dashboard filter
- Metric certification status is visible to every consumer before they build a report
- Uncertified ad-hoc calculations are flagged automatically so rogue definitions never spread
Trace any number
to the row it came from. When a CFO questions a revenue figure, any analyst can click through from the dashboard value to the source table, the transform, and the certification approval in under a second.
- Column-level lineage from dashboard to warehouse table, no manual mapping required
- Every metric change is stamped with who approved it and when
- Audit exports ready for compliance review in one click
The layer learns
which metrics belong together. Quaeris monitors query patterns across every team and auto-proposes new certified definitions when usage signals show an emerging consensus, cutting the time from ad-hoc calculation to governed metric.
- Usage frequency and co-occurrence patterns surface candidate metrics to data owners automatically
- Proposed definitions are previewed against live warehouse data before anyone approves them
- Definition quality score improves continuously as more queries validate the logic
Metrics versioned,
owned, and promoted like code. Every certified definition carries a version number, a named owner, and a promotion history so breaking changes never surprise downstream consumers.
- Semantic diffs show exactly what changed between metric versions before promotion
- Downstream consumers receive deprecation notices before a definition is retired
- Ownership assignments ensure every metric has an accountable team, not just a creator
Built, not born.
Traditional semantic layers require your team to do the modeling work upfront - before a single business user can ask their first question.
Semantic layers are built, not born.
Traditional semantic layers require upfront modeling: LookML in Looker, dbt YAML in dbt Semantic Layer, hand-written cube definitions in Cube. Your data team spends 3–6 months building before a single user asks their first question. Metric definitions live in code, buried in git history. When definitions change, documentation lags by weeks. Business teams end up with six conflicting "revenue" definitions - one in Tableau, one in Sigma, one in a spreadsheet. Trust collapses.
Learn from every question.
Quaeris watches. As business users ask questions in natural language, the platform observes which tables they're asking about, which filters they apply, which metrics they care about. Over days and weeks, it learns the semantic structure of your warehouse - not from documentation, but from actual usage. Your data team certifies what it learns, versioning it and scoping who sees it. From that moment on, every agent answer uses the same certified number.
Observe. Certify. Trust.
Three steps replace a six-month modeling sprint with a two-week deployment.
Quaeris watches your usage. You certify what you learn.
As users ask questions - "What was Q2 revenue by region?" - Quaeris parses the natural language, maps it to your warehouse tables, and notes which metrics matter. It builds a probabilistic model of your semantic structure: revenue is typically SUM of orders.order_total WHERE orders.status = 'completed'. Region comes from the geography dimension. Over 100 questions, patterns emerge. No manual YAML, no config files, no sprint meetings. Just observation.
Try for FreeYour team owns the canonical definitions.
Your data engineer reviews what Quaeris learned and certifies it: "Yes, this is revenue. No, that variant is a one-off for the finance team." Certification happens in the Quaeris UI - no code, no YAML, no friction. Once certified, a definition is locked, versioned, and owned. When it changes, every downstream agent answer updates automatically. The change is audited.
Explore the certification flowEvery answer cites the certified metric.
When a business user asks "What's our Q3 churn?", the Quaeris agent returns not just a number, but the metric definition it used - owner, version, business logic, and the exact table lineage. Users see why the answer is what it is. If a different team has a different churn definition, both coexist in the semantic layer with clear ownership. The governance dashboard shows metric usage, ownership, lineage, and change history. This is how trust scales.
See the lineage viewSix reasons to stop writing YAML.
Every semantic competitor asks your team to do the modeling work upfront. Quaeris learns from usage instead.
Weeks, not months
Traditional semantic layers need 3–6 month modeling sprints. Quaeris learns from usage in days. Deploy on week one, first certified metric by week two.
No code required
Your data team certifies metrics in a UI, not YAML or MDX. Analytics engineers stay focused on BI work, not semantic layer maintenance.
Versioned & audited
Every metric definition change is logged with owner, timestamp, and rationale. Rollback or compare versions in one click. Full audit trail, always.
Multiple authoritative definitions
Finance's "revenue" and Product's "activation" coexist in one semantic layer. No forced reconciliation. Ownership and lineage make the difference clear.
Scales to thousands
Warehouse-native design handles thousands of metrics across hundreds of tables. No performance cliff as your semantic layer grows with your business.
Governance by design
Access controls, role-based metric visibility, and lineage enforcement are built in. Not added later. Security and governance scale with adoption.
How Quaeris compares to other semantic layers
Direct, factual comparison against dbt Semantic Layer, Cube, AtScale, and Looker's LookML. Current as of June 2026.
| Feature / Capability | Quaeris Smart Semantic Layer | dbt Semantic Layer | Cube | AtScale | Looker LookML |
|---|---|---|---|---|---|
| Auto-learn from usage | Yes - learns from natural-language prompts | No - requires YAML config | Partial - requires code | No - manual cube design | No - requires LookML |
| UI-based certification | Yes - zero code | No | No | No | No |
| Warehouse-native | Yes - queries live data | Yes - via dbt Cloud | Partial - proxies queries | Limited - requires data sync | No - proprietary model |
| Versioning & audit trail | Full history, owner tracking | Via git only | Via API only | Limited | Limited |
| Role-based metric access | Yes - agent-level enforcement | No | Partial | Partial | Via Looker roles |
| Time to first metric | Days - learning starts immediately | Weeks - upfront YAML sprints required | Weeks - developer setup & architecture work | Months - cube modeling required before first query | Months - LookML expertise & modeling sprint |
| Warehouse platform support | Snowflake, BigQuery, Databricks, Redshift, Synapse | Yes - via adapters | Yes - via drivers | Yes | Looker-only |
| Learning curve for data teams | Low- watch & certify | Medium - YAML + dbt modeling | High - code + architecture | High - cube design | Very high - LookML + Looker concepts |
Feature comparison current as of June 2026. See our full comparison library for deeper analysis.
What a certified metric looks like
Every metric definition in Quaeris contains the information your team needs to trust it. Click any field to inspect it.
Where the Smart Semantic Layer delivers
From multi-team metric alignment to warehouse consolidation - four situations where auto-learned semantics changes the outcome.
Finance + Product + Sales speak the same language
Each team has their own "revenue" definition. Quaeris holds all of them in one semantic layer with clear ownership. No more reconciliation calls before the board meeting. Every team queries the definition they own.
See how teams unify their metricsStop the ad-hoc request queue
Business teams no longer ask "What's our revenue?" - they know the certified definition and ask confident follow-ups. Your data team shrinks the backlog because every agent answer is already governed and traceable.
See how data teams cut their backlogAudit every metric and every query
Regulated industries - finance, insurance, healthcare - need proof that metrics are certified and queries are governed. Quaeris's semantic layer IS the proof. Every agent answer is logged with metric version, user, and table lineage.
Explore compliance & audit featuresMigrate off multiple BI tools
Teams running Tableau, Looker, and Sigma in parallel - each with different metric definitions. Quaeris's semantic layer becomes the single source of truth. Once certified, it's the foundation for retiring the legacy BI tool and its metric fragmentation.
See consolidation ROI analysisDeploy in two weeks. Govern on day one.
Four phases from warehouse connection to full semantic governance.
Week 1Connect your warehouse. Quaeris begins learning.
- Day 1–2: Connect to your Snowflake, BigQuery, Databricks, or Redshift instance (30 minutes). Quaeris scans your schema and begins observing user questions.
- Day 3–5: Your data team invites business users to ask pilot questions (10–20 exploratory queries). Quaeris infers metric patterns.
- Day 7: Review what Quaeris learned. Begin certification: "Yes, that's our revenue. No, that's a one-off."
Week 2Your team certifies metrics. Governance locks in.
- Day 8–10: Data engineer certifies the top 20–30 metrics. Assigns owners, writes business rules, versions each one.
- Day 11–12: Configure role-based access. Decide which teams see which metrics. Test with a cohort of power users.
- Day 14: Go live. All agent answers cite certified metrics. Lineage is live. Audit logs are flowing.
Week 3+Grow metrics. Refine governance.
- Weeks 3–4: Monitor usage. Add new metrics as they emerge. Retire obsolete definitions.
- Month 2: Roll out to all business teams. Measure reduction in data-team ad-hoc requests.
- Ongoing: Quaeris learns new patterns. Your team certifies quarterly. Semantic layer stays current.
OngoingQuaeris runs alongside your existing BI stack.
- Your data team gets: setup support, bi-weekly office hours, Slack channel with Quaeris experts.
- Business users get: 30-minute training on how to ask questions and read metric definitions.
- Documentation: API reference, SQL generator, lineage how-tos, and governance playbooks.
Real outcomes, in their words
"We had six different 'revenue' numbers being cited in the same board meeting. Quaeris gave us one certified definition everyone agreed on - in under two weeks."Head of Analytics, mid-market financial services firm (illustrative)Talk to our team
"We were on month four of a LookML modeling sprint when we evaluated Quaeris. We had certified metrics live by day fourteen. I've never seen an analytics tool close that gap."VP of Data, B2B SaaS company (illustrative)
The semantic layer bottleneck - and why Quaeris solves it
Every semantic layer product asks your team to do the work upfront. Looker demands LookML expertise. dbt Semantic Layer requires weeks of YAML config writing. Cube and AtScale need architects to design cubes before a single question gets answered. The ROI math is brutal: you spend 3–6 months building before you get any benefit. Meanwhile, business teams have already found their own analytics solutions - spreadsheets, Sigma, Mode - and defined their own "revenue." Once that fragmentation sets in, unifying metrics later is a political problem, not a technical one.
Quaeris flips the model. Metrics emerge from usage. Your team certifies what Quaeris learns, not the other way around. Governance happens at the point of observation, not at the end of a long modeling sprint. The ROI is immediate: in week two, every agent answer cites a certified metric. By month three, your data team's ad-hoc request queue has measurably shrunk - and your team is focused on decisions, not definitions.
This is the only semantic layer that pays for itself before your team finishes configuring it.
Common questions about the Smart Semantic Layer
Does Quaeris replace our existing BI tool's semantic layer (LookML, YAML, etc.)?
Quaeris complements, not replaces, your BI tool. If you're using Looker's LookML, Quaeris can coexist and serve natural-language queries while LookML continues to power structured dashboards. If you're looking to migrate off a legacy BI tool altogether, Quaeris's semantic layer becomes the source of truth - and you can retire LookML, YAML, or proprietary models.
Can Quaeris learn metrics from a warehouse that already has a semantic layer?
Yes. If you have Looker, dbt Semantic Layer, or Cube already deployed, Quaeris can ingest those definitions and extend them. It learns from new questions, suggests new metrics, and lets your team version everything in one place. You're not forced to rebuild from scratch.
What if Quaeris learns a metric definition wrong?
Your data team reviews it during certification. If it's wrong, you reject it or correct it - mark the actual business rule. Quaeris won't use the rejected definition. It's not training on your corrections in a black-box way; your team's certification decision is explicit and versioned.
Who owns the metrics - Quaeris or our team?
100% your team. Metrics live in your warehouse. Definitions are certified by your data engineers. Quaeris is the platform that learns patterns and surfaces them for your approval. You control versioning, access, change history, everything.
Does the auto-learning happen in real time or in batch?
Quaeris observes questions in real time and incrementally updates its confidence in metric patterns. Certification happens on your schedule - daily, weekly, or monthly. No batch delays. No waiting.
What if we have conflicting metric definitions (Finance's revenue vs. Product's revenue)?
Both live in the semantic layer. Quaeris tracks which team owns which definition, tags them separately, and ensures agents use the correct one based on context or user role. No forced reconciliation. Transparency replaces conflict.
Can we audit which queries used which metric definition?
Yes. Every answer Quaeris returns is logged with: the metric definition version used, the date, the user who asked, the warehouse tables queried, and the exact lineage. This audit trail supports compliance workflows in regulated industries - ask your Quaeris representative about specific certification coverage.
How does Quaeris handle new tables or schema changes?
Quaeris rescans your warehouse regularly. When a new table appears, it learns potential metrics from that table. When a schema changes, it updates its confidence estimates. Your team re-certifies as needed. Zero downtime.
See the Smart Semantic Layer in action.
Book a 30-minute demo. We'll connect to your warehouse, walk through a live metric certification, and show you an agent answer with full lineage - using your actual schema.