Multi-step analysis
Ask follow-up questions and chain analyses without re-querying. Agents remember context. Start with churn, pivot to revenue, drill into a cohort - in one conversation.
Learn moreBuilt for analysts
Ask questions. Get governed answers. In seconds.
Forget SQL queues and dashboard wait-lists. Quaeris agents answer your questions in natural language - with full lineage, certified metrics, and role-based access built in. Self-serve analytics your data team can trust.
Natural language queries No SQL, no data dictionary hunting
Certified metric answers Every number sourced; zero hallucinations
Role-based access enforced At query time, not via dashboard filters
Multi-step analysis Chain questions; agents remember context
Analyst Query Brief
Churn Rate by Segment · Q2 vs Q1
Enterprise8.2% ▲ +61%
Mid-Market3.1% → flat
SMB1.8% ▼ −12%
Sources verified: dwh.prod.customers · segment lookup · churn_date field
100%Cited
14sTo answer
0 SQLRequired
Core CapabilitiesSelf-serve analytics,
Self-serve analytics,
governed at every step.
Ask any question, explore any angle - every answer is traced to a certified metric, role-checked before it leaves the engine, and logged for audit without any extra work from you.
Ask once. Get a certified answer in seconds.
Type a question in plain language - no SQL, no ticket queue. Quaeris agents retrieve the answer from your governed semantic layer, cite the metric definition, and show you exactly where the number came from before you even scroll.
- Natural-language queries resolve against certified metric definitions, never raw table scans or model hallucinations
- Median time from question to cited answer: 14 seconds, including role check and lineage trace
- Answer cards show metric owner, version, and source table inline - so you can defend the number before you share it
Find what moved the number - without a data team ticket.
Go beyond totals. Ask Quaeris to decompose a variance by dimension, GL account, region, or time window. The agent breaks down every driver against certified budget and forecast metrics, ranked by contribution, and cites each component.
- Agents decompose variances across certified dimensions without you writing a single JOIN or CASE statement
- Every driver bar is sourced from a version-locked metric - Marketing can't see Finance GL lines their role doesn't permit
- Drill from summary to row-level in the same conversation; agents hold context across follow-up questions
Extend any trend - with a warning when the line breaks.
Ask "How will churn trend if Q4 acquisition holds?" and the agent projects forward using historical certified actuals, flags the threshold breach, and surfaces the early-warning alert before you export the slide.
- Forecast queries run against the same certified base metrics as your actuals - no separate model to reconcile
- Threshold alerts surface automatically when a projection breaches a defined boundary, no manual monitoring needed
- Every forecast answer is reproducible: re-run tomorrow and the certified inputs are versioned and traced
One answer across all your warehouse sources.
Quaeris queries your warehouse in place - Snowflake, BigQuery, Databricks, Redshift - and returns a single governed answer. No data is copied out. Every source contributes to the certified semantic layer your answer is grounded in.
- Warehouse-native execution means zero data egress and zero shadow copies - your security perimeter stays intact
- The semantic layer normalises definitions across sources so "revenue" means the same thing whether the grain comes from Snowflake or BigQuery
- Access is enforced at query time per source: a Marketing analyst can ask, but Finance-scoped GL lines are blocked and logged
Why analysts choose Quaeris
Numbers that matter to analysts.
14s
Time to first answer
From question to verified result
89%
Self-serve adoption
Of analysts answer their own questions
0
False answers served
Every answer cited to certified metrics
76%
Backlog reduction
Hours freed up for data teams
Three steps to self-serve analytics
From question to governed answer.
No SQL required. No waiting on the data team. Just ask, verify, and act.
Ask in plain language
Write your question as you would ask a colleague. No SQL syntax. No dashboard hunting. Type a natural-language question: "What's the MRR trend for Enterprise customers in the West region this quarter?" Quaeris agents parse your question and reason over your semantic layer to find the answer.
Q:What's the MRR trend for Enterprise
customers in the West region this quarter?
See examplescustomers in the West region this quarter?
Get verified answers
Quaeris agents retrieve answers from your governed semantic layer - not from model hallucinations. You see exactly which metrics were queried, which business rules applied, and which data lineage was traced. No mystery numbers. Answers your analysts can trust and defend to leadership.
MRR (Enterprise, West) - certified metric v2.1
Owned by: analytics-eng@company.com
Source: dwh.prod.subscriptions · region field
Stay compliant
Role-based access enforced at query time. Your data permissions don't live in a dashboard filter - they live in the query engine. Every analyst sees exactly the data their role permits. Full audit trail: who asked what, when, and which metrics they accessed. Data teams sleep better.
jane.doe@company.com
Row-filter: segments [Enterprise, Mid-market]
Status: ✓ Cited
Analysts are stuck in the middle
Why BI isn't enough anymore.
Trapped between SQL backlogs and dashboard limitations, analysts spend more time waiting than analyzing. Quaeris closes that gap.
SQL queues are a bottleneck
Your data team is buried in ad-hoc requests. A simple revenue question means a Slack message, a Jira ticket, waiting two days. For an analyst, that's lost productivity and missed insights.
Get answers in 14 seconds
Ask Quaeris agents directly. No queue. No Jira tickets. Your question is answered and cited in seconds. Data teams go from firefighting to strategy.
See the workflowBI tools are rigid
Dashboards are pre-built and static. Want to pivot the analysis? Explore a new dimension? You're stuck asking data engineers. Self-serve in theory, locked in practice.
Ask any question, any angle
Agents reason over your semantic layer on the fly. Pivot, slice, dice, forecast - all without rebuilding dashboards. True exploratory analytics at the speed of thought.
See what's possibleConflicting answers across tools
Revenue in the CRM is $50M. Revenue in the data warehouse is $48M. Analysts spend hours debugging definitions. Which number do you present to the board?
One certified metric, every answer
Your data team defines revenue once in the semantic layer. Every agent answer - across every analyst's query - uses that same certified definition. Consistency guaranteed.
Learn about semantic governanceCompliance audits are painful
Regulators ask: "Who accessed that customer cohort?" "Why was this metric different in Q2?" Your BI tool has no answers. You dig through logs for days.
Full audit trail, always
Every question, every answer, every metric access is logged with timestamps and ownership. Regulators ask, you answer in minutes. No dig, no doubt.
See audit in actionWhat a real question looks like
Churn, MRR, anomalies - answered in seconds.
An analyst at a B2B SaaS company wants to understand churn drivers. Here's how Quaeris answers it - with full lineage and access controls visible.
The Question
Which customer segments had the highest churn rate in Q2? Show me monthly trend and tell me if it's statistically significant vs. Q1.
Natural language. No SQL. No data dictionary lookup needed.
Quaeris Response
Answer
Enterprise segments had 8.2% churn in Q2, up from 5.1% in Q1. This is a 61% increase and is statistically significant (p < 0.05). Mid-market churn was flat at 3.1%.
Metrics Used
Churn Rate (Enterprise)Definition: customers_churned / customers_start_of_period
Owned by: analytics-eng@company.com · Updated 2026-06-10
Owned by: analytics-eng@company.com · Updated 2026-06-10
Churn Rate (Mid-market)Definition: customers_churned / customers_start_of_period
Owned by: analytics-eng@company.com · Updated 2026-06-10
Owned by: analytics-eng@company.com · Updated 2026-06-10
Data Lineage
dwh.prod.customers → churn_date · segment column → aggregated by month_date → chi2 significance testACCESS CONTROL: Your role permits Enterprise and Mid-market.
Every number is sourced. Every metric is certified. Every access is logged.
Data Team Audit Log
Analystjane.doe@company.com
Time2026-06-12 09:14 UTC
Query"Which segments had highest churn in Q2…"
MetricsChurn Rate (Enterprise, Mid-market)
Row filter[Enterprise, Mid-market] applied
Status Cited
Data teams stay in control. Every analyst action is visible and auditable.
View query provenanceThe Question
How will MRR trend if Enterprise churn stays elevated through Q3?
Forecast query. Agents reason over historical patterns.
Quaeris Response
Answer
If Enterprise churn stays at 8.2% through Q3, MRR projects at $4.1M by Q3 end - a 6.8% decline vs current $4.4M. Intervention before August averts the risk with >70% confidence.
MRR (Enterprise) forecastMetric: mrr_enterprise_forecast_v1.2 · owned by FP&A · certified
Model Lineage
Historical MRR (24 months) → ARIMA forecast → segment filter applied. Access: [Enterprise] only.Data Team Audit Log
Analystmike.patel@company.com
MetricsMRR Forecast (Enterprise)
Status Cited
The Question
What drove Q2 revenue growth vs. plan? Break it down by product and region.
Root-cause query. Agents trace anomalies to their source.
Quaeris Response
Answer
Q2 revenue was $12.4M vs $11.8M plan (+5.1%). Top driver: Enterprise West +$480K (product: Analytics Suite). Offset by Mid-market East −$120K (product: Core). Growth is statistically attributable to Q1 expansion cohort.
Revenue (by product, region)Metric: revenue_net_v3.4 · owned by Finance · certified
Data Lineage
GL 4000–4099 → product SKU join → region dimension → variance vs plan_revenue_v3.4. Access: all regions.Data Team Audit Log
Analystpriya.s@company.com
MetricsRevenue Net, Plan Revenue
Status Cited
What analysts can do
Six things analysts do in seconds, not days.
From multi-step analysis to proactive anomaly detection - governed analytics at the speed of thought.
Forecasting
"How will churn trend if we don't intervene?" Agents forecast using historical patterns from your certified semantic layer - not from guesswork or spreadsheet models.
Learn moreRoot-cause diagnosis
"Why did conversion drop last week?" Agents trace anomalies to their source - surfacing the metric, the dimension, and the time-period at fault. No digging required.
Learn moreAnomaly detection
Agents flag suspicious metrics proactively. "Revenue is 12% above forecast. Here's why." Stop discovering anomalies in the board meeting. Know before the meeting starts.
Learn moreAd-hoc cohorts
"Show me customers who signed up in Q1 and churned by Q2." Agents build cohorts on the fly from the semantic layer - no SQL, no data request ticket, no waiting.
Learn moreExport to anywhere
Results to Slack, email, dashboards, or notebooks. Every answer is reproducible - colleagues can re-run your analysis with the same certified metric definitions.
Learn moreFrom backlog to autonomy
What happens when analysts stop waiting.
B2B SaaS · Mid-marketRead full story
"Analytics team cut request wait time from 3 days to 90 seconds - and the data team loved it"
A B2B SaaS analytics team of six was fielding over 40 ad-hoc data requests per week. Simple questions about campaign performance or customer cohorts took 3+ days to answer. Complex revenue analyses took a week. Analysts spent 40% of their time waiting on data engineers, and 60% actually analyzing.
After deploying Quaeris, the same team answered questions in 90 seconds. The request backlog dropped to near-zero within the first month. The data engineering team shifted from firefighting requests to building new semantic models and improving data quality. Within 60 days, analysts were running cohort and churn analyses independently.
3 days
→ 90sAverage time to answer
→ 90sAverage time to answer
87%Data team time freed up
300+Questions answered independently
"We went from a 40-ticket backlog to near-zero in a month. Our analysts can answer their own questions now, and the data team finally has time to do the work they were hired for."
- Head of Analytics, B2B SaaS company (illustrative)
Zero SQL required
Certified metric governance
Full audit trail from day 1
Analysts trust Quaeris
From the people doing the work.
What analysts say after switching to governed self-serve analytics.
"I used to spend half my week in Slack explaining why numbers didn't match. Now every agent answer cites its metric, so the conversation shifts from 'Which number is right?' to 'What does this mean for our strategy?'"
"Before Quaeris, every analysis started with a two-hour search through our BI tool to find the right dashboard. Now I just ask. The metric definition is right there - no more 'which revenue is the right revenue?'"
"The audit trail is the killer feature for me. When my manager asks where a number came from, I can answer in one click. That's never been true of any tool I've used."
"Our data team went from fielding 30 requests a week to 5. Analysts are answering their own questions. The team is now doing the modeling work they were hired to do."
"Setup took less time than I expected. By week two, our analysts were running their own churn and cohort analyses without any tickets. That's 70+ hours of data team time back per month."
"The governance aspect is what made our legal and compliance teams comfortable. Every query is logged, every metric is certified. For a regulated industry, that's not a nice-to-have - it's essential."
Answers everywhere analysts work
Quaeris meets you where the work happens.
Sourced integrations: Snowflake, BigQuery, Databricks, Azure Synapse, Amazon Redshift, SharePoint, Google Drive.
Slack
Ask Quaeris directly in Slack. Get cited answers without tool-switching. Every response includes the metric source and lineage - right inside the channel where decisions happen.
AvailableNotebooks
Embed Quaeris answers in Python or SQL notebooks. One certified source of truth for every metric. Analyses are reproducible and auditable across your team.
AvailableBI Dashboard Embedding
Surface Quaeris answers as a governed data source inside your existing dashboards. Certified metric definitions stay consistent across every reporting surface.
RoadmapScheduled Reports
Schedule recurring analyses and deliver answers to inboxes on a cadence. Always fresh, always cited. Governance is enforced on every scheduled run - not just interactive queries.
AvailablePowered by your governed metrics
Every answer grounded in certified metrics your data team owns.
Quaeris agents don't generate numbers - they retrieve them from your semantic layer. Revenue means the same thing in every query. Churn is defined once. Margin is owned by Finance. When an analyst asks a question, agents reason over these certified definitions and return answers your organization can act on without debate.
This is where the trust lives. Not in the agent, but in the layer beneath it.
One metric definition across all analyst queries
Ownership and audit trail for every metric
Automatic consistency across analysts, teams, and tools
Semantic Layer · Certified Metrics
Revenue
Finance
certified
Churn Rate
Analytics
certified
MRR
Finance
certified
Margin
Finance
certified
CAC
Marketing
certified
LTV
Analytics
certified
Quaeris agents query this layer
Every agent answer uses certified definitions
No hallucinations. No inconsistencies. Full lineage.
Questions analysts ask
Straight answers. No hype.
Yes, agents remember context across messages. Ask your first question, then refine, drill down, or pivot. Agents chain analyses without losing state - so you can start with churn, ask a follow-up about a specific segment, and then forecast forward, all in one session.
Ready to unlock self-serve analytics?
See Quaeris in action - on your data.
Book a 30-minute demo with a Quaeris solutions engineer. We'll connect to your warehouse, walk through analyst use cases on your metrics, and show you what self-serve looks like with governance built in.