Ad-hoc requests are drowning the team
Every Tuesday it's the same: revenue questions, cohort analyses, ad-hoc forecasts. Your data team is a ticket-processing machine. Strategy? Infrastructure? Hiring? There's no time.
For Data TeamsKill the ad-hoc
Kill the ad-hoc
request queue.
Business users ask their own questions. Your data team writes the rules, once. Quaeris handles the governance - so you can finally build instead of firefighting.
Governed self-serveBusiness users explore; your rules hold
Warehouse-nativeSnowflake, BigQuery, Databricks, Redshift
Instant answers14 min median time-to-insight from any question
Access at query timeRow-level enforcement - automatic, audited
Governed Query Panel
Ad-Hoc Request Volume - This Week
Finance & FP&A48 tickets
Product & Growth31 tickets
Sales Operations12 tickets
Marketing Analytics9 tickets
Access enforced: Role-scoped · Semantic layer v2.1 · Audit logged
87%Queue reduction
14 minTo insight
ZeroSQL required
Core CapabilitiesBuilt for the team that
Built for the team that
certifies the truth.
One governed semantic layer. Certify a metric once, give the business governed self-serve, eliminate the repeat-request queue, and trace downstream impact before any definition changes.
Certify once. Trusted everywhere.
Define the canonical logic for any metric, assign an owner, and version-lock it in the semantic layer. Every downstream question - from any surface, any role - draws from the same certified definition, preventing shadow metrics and conflicting numbers.
- SQL definition, owner, and version recorded at certification; no undocumented variants in circulation.
- Any change triggers a new version - previous answers remain auditable against the definition that produced them.
- Certified status surfaced inline on every answer card, so business users know without asking.
Self-serve that stays within bounds.
Business users get plain-language access to the metrics they are scoped to see - and only those metrics. Role-based access is enforced at query time, not at the dashboard-sharing step, so self-serve cannot leak data or bypass governance.
- Row- and field-level access evaluated at query time; no governance workaround via exported reports.
- Business users ask natural-language questions; the agent answers only with definitions the team has approved.
- Access-denied events are logged with the same immutability as granted ones - full picture for auditors.
Stop answering the same question twice.
The repeat-request queue exists because the business cannot trust ad-hoc answers. When every metric is certified and self-serve is governed, the queue dissolves - your team shifts from running one-off reports to maintaining the definitions that power all of them.
- Certified answers to recurring questions are reusable; the same governed result is returned without re-running ad-hoc SQL.
- Time-from-question to certified answer benchmarks at 14 minutes median across deployed orgs - not 3-day ticket turnaround.
- Data teams track which definitions are queried most, letting them prioritise certification work by actual business demand.
See who breaks before you change a thing.
Before editing a metric definition, run downstream impact analysis to see every dashboard, agent, and report that references it. Governance is not just a record of the past - it is a blast-radius map for the future.
- Full upstream-to-downstream graph surfaced on any certified metric: source tables on the left, consuming dashboards and agents on the right.
- Impact count and owner list generated before any definition change is committed - no surprise breakage in board-level decks.
- Audit log records the before-and-after definition for every version, giving regulators a complete change history.
The Data Team's ChallengeYou built the pipeline.
You built the pipeline.
Now you're the bottleneck.
Three problems - and the governed self-serve answer to each.
Self-serve analytics shrinks the queue
Business users ask directly. The semantic layer gates the answers - no dangerous slicing, no mismatched metrics. Your team writes the rules once and scales from there.
Every team calculates revenue differently
Finance says one number, Product says another. Your data team has spent six months reconciling definitions across Tableau, Looker, and three homegrown dashboards. It's never consistent.
One semantic layer, one source of truth
Define revenue once in the semantic layer. Every self-serve query uses the same definition. When the metric changes, it changes everywhere - audited and visible.
Self-serve means data leaks and wrong answers
You can't give business users direct access to your warehouse. There's no row-level security, no audit trail, and when someone exports sensitive data, you find out after the breach.
Governance enforced at query time
Role-based access controls are baked into the agent layer - users only see what they're permitted to see. Every query is logged. You maintain control while enabling access.
Real outcomes across deployed orgsNumbers that hold up
Numbers that hold up
to audit scrutiny.
87%Reduction in ad-hoc requestsMedian in first 90 days · across deployed orgs
14 minTime to first insightFrom question to answer · vs. 2–3 day ticket queue
100%Metric definitions alignedIn the semantic layer · single source of truth
600+Self-serve users enabledAcross deployed orgs · median first 90 days
How it worksGoverned self-serve
Governed self-serve
in four steps.
Your data team sets the boundaries. Business users explore within them. No requests, no bottlenecks, no chaos.
Write governance rules once
Your data team defines the semantic layer: certified metrics, business logic, access policies. Role-based controls are baked in. When a metric changes, the whole organization sees the update.
Give business users direct access
No more "send me a query." Business users open Quaeris and ask their questions in plain language. Agents reason over your governed semantic layer and return instant answers.
Every answer is auditable
Users see the metric definitions and data lineage behind every answer. If the revenue number is wrong, you trace it back to the source in one click. Role-based access enforces permissions at query time.
Your team scales without hiring
Self-serve answers 80% of ad-hoc requests. Your data team shifts from ticket-processing to strategy: building forecasts, refining models, mentoring analysts. Finally, focus on the work that moves the business.
Core capabilitiesThree things that make
Three things that make
governed self-serve real.
Core Capability 01
The semantic layer learns as you use it.
Quaeris watches how your team uses data. Business definitions, metric relationships, data lineage - the semantic layer auto-learns and surfaces suggestions. You don't have to pre-model everything in YAML. Your team approves, it learns.
Explore the semantic layerSemantic Layer
Metric Registry4 certified
revenueData Eng · v3.1Certified
arrFinance · v2.4Certified
churn_rateAnalytics · v1.9Certified
cacGrowth · v1.2Certified
gross_marginSuggested
nrrSuggested
Auto-discovered from your warehouse · approve to certify
Core Capability 02
Agents that cite their sources.
Business users ask natural-language questions. Agents translate to SQL, query the semantic layer, and return certified answers - not hallucinations. Every number shows its sources: which metric definition, which table, which business rule. Your team sleeps better.
Book a DemoAgent Conversation
"What was our NRR last quarter?"
Querying semantic layer…
Net Revenue Retention last quarter was 108%. This is based on the certified nrr metric (v2.1), sourced from your CRM renewal data joined with billing records.
Metric: nrr v2.1 · Source: billing.renewals · Certified by: Analytics team
"Break it down by product line."
Core Platform114%
Add-ons103%
Professional Services97%
Role-scoped · same metric definition · zero hallucinations
Core Capability 03
Security enforced at the agent layer.
Role-based access policies are applied when the agent runs - not as a dashboard filter. A sales rep asking about customer lifetime value sees only their region's data. A controller asking about expenses sees only company-owned spend. Enforcement is automatic, audited, and consistent.
Read the governance blueprintRole-Scoped Results
Viewing as:
Total Revenue$1.1M
Company Gross Margin••••
Closed Deals (Your Region)63
Headcount Costs••••••
Enforcement is automatic · every query logged
Real data teams. Real outcomes.What deploying Quaeris
What deploying Quaeris
looks like.
Three teams, three verticals, three ways self-serve transformed how they work.
Reduced ad-hoc requests by 84% while expanding self-serve to 600+ users
84% fewer ad-hoc requests600+ self-serve users enabled90 days to full rollout
The data team was processing 200+ requests per week from finance, product, and FP&A. They connected Quaeris to their Snowflake warehouse, migrated 120 certified revenue and bookings metrics into the semantic layer, and opened the agent interface to the business. Within 90 days, the ad-hoc queue dropped by 84%. The team shifted from firefighting to building predictive models.
"We went from a 48-hour average ticket turnaround to answers in under 20 minutes. The data team can actually focus on strategy now."
- Head of Data Engineering, Financial Services firm
Cut time-to-insight from 3 days to 20 minutes
20 min average time-to-insight3× faster than previous ticket workflow0 hallucinated numbers
Product and growth teams were waiting 2–3 days for cohort and retention analyses. Every question meant a ticket, a data analyst context switch, and a SQL query. The data team deployed Quaeris across their product and revenue metrics. Now, product managers ask directly and get instant, source-cited answers. The data team went from reactive to strategic.
"Product managers used to open a ticket for every cohort cut. Now they ask Quaeris directly - and the number they get matches what we'd produce."
- Senior Analytics Engineer, B2B SaaS company
Unified conflicting metrics across 6 regional teams
6 regional teams aligned1 metric definition per KPI11× ROI on analytics time
The organization had six regional BI tools and three legacy data warehouses. Headquarters and regional teams calculated "revenue" and "margin" differently. The data team unified everything into a single Quaeris semantic layer with 80 certified definitions. Executives and regional leaders ask the same question and get the same answer. One team retired four conflicting dashboards.
"For the first time, regional GMs and HQ finance saw the same revenue number. We retired four dashboards and stopped the weekly 'whose number is right?' call."
- Chief Data Officer, Retail / CPG organization
Questions from data leadersAnswers that
Answers that
actually help.
If it's not here, book a demo - we'll walk through your specific setup.
The semantic layer is your guard rail. Users ask questions in plain language; agents translate those questions to SQL against the semantic layer - not raw tables. If a question can't be answered from certified metrics, the agent says so. The semantic layer enforces consistency; your team writes the definitions once.
Governed by designHow governance stays intact
How governance stays intact
as you scale self-serve.
Self-serve doesn't mean lawless. Here's how Quaeris keeps governance front and center.
Certified metrics, not wild estimates
Business users query your semantic layer - the definitions your data team has certified. No hallucinations, no model drift. Every agent answer is locked to a metric definition your team approved.
Lineage visible in every answer
Every answer shows the metric definition, the source table, the business rule that applied, and the user's access level. Your analysts can audit any result in one click. Compliance auditors get full traces.
Access enforced at runtime
Role-based policies are applied when the agent runs. A user with finance permissions can't see product costs. A regional user can't see other regions' data. Enforcement is automatic and audited - no manual row-level filters required.
Why data teams choose QuaerisQuaeris vs.
Quaeris vs.
the alternatives.
Not all self-serve solutions are created equal. Here's how Quaeris is different.
Self-serve BI dashboardsTableau, Looker, Power BI
Beautiful dashboards don't answer ad-hoc questions. Every question still needs a ticket. You're back where you started.
Quaeris
Agents answer ad-hoc questions in seconds. Dashboards stay for scheduled reporting. Both coexist - no migration required.
Unmanaged SQL editorsDBeaver, notebooks, Jupyter
Users run arbitrary SQL. Metric chaos. Row-level security is manual. Ad-hoc queries break compliance and create inconsistency.
Quaeris
Users ask in plain language; agents translate to governed SQL. Semantic layer enforces consistency. Access is automatic and audited.
LLM chatbots on raw dataChatGPT + your DB, basic prompt-engineering
Language models hallucinate numbers. No audit trail. No access control. Results are unreliable and unverifiable.
Quaeris
Agents query governed metrics, not raw tables. Every answer is certified. Full audit trail. Zero hallucinations.
Hyperscaler-native AI toolsCortex Analyst, Genie, AWS Q
Locked to one warehouse vendor, one model. Governance is bolt-on. Migrating is painful and expensive.
Quaeris
Warehouse-portable. Model-portable (BYOM). Governance is baked in. Switch vendors without retraining your semantic layer.
Ready to shrink the ad-hoc queue?
Let your data team breathe.
Book a 30-minute demo. We'll walk through your warehouse setup, show you the semantic layer in action, and show you governed answers - no slides, no fluff.