Unit cost
€0.80 – €3.50 per query or report handled
Methodology v1.0. Counted once per query or report handled regardless of which capability handled it.
Run the analytics service desk end-to-end — natural-language-to-SQL with validated queries, recurring dashboard distribution to stakeholders, pipeline and schema health monitoring, and metric anomaly detection — with analyst review on novel metrics and sensitive breakdowns.
Scoped like a data analyst hire, priced per query or report handled, anchored to a fully-loaded EUR 60-85k benchmark.
Response time
sub-minute on routine questions
Accuracy target
95-98% on validated queries
Escalation cap
under 2 hours on analyst review
Priced per business action
Range reflects artifact depth. Low end is validated NL-to-SQL answers on single-table cuts; high end is multi-source recurring reports or pipeline-health investigations with lineage.
Unit cost
€0.80 – €3.50 per query or report handled
Methodology v1.0. Counted once per query or report handled regardless of which capability handled it.
Human-equivalent reference
Data Analyst
EU mid-market
Benchmarked against EU mid-market data analyst roles. Fully loaded includes salary, benefits, warehouse + BI tooling, management overhead, and first-year ramp.
Live calculator
Demo projection · Methodology v1.0
Capabilities
Activate the capabilities that match your largest repetitive categories. Start with the default set; expand as you prove each one. Metered unit is the role's — adding capabilities never changes the per-action price.
Translates business questions into validated SQL against the semantic model.
Read the capability
Ships recurring dashboards and digests to stakeholders on cadence.
Read the capability
Watches pipeline and schema health and flags breaks for analyst review.
Read the capability
Flags metric deltas against baselines and routes for analyst review.
Read the capability
Scenarios
Three business shapes we see most often. Costs are computed from €0.80 – €3.50 per query or report handled and a fully-loaded Data Analyst benchmark.
Scenario 1 · SaaS · 300-800
500 queries or reports handled / month
Starting capabilities
Situation
A 500-person B2B SaaS company fields 500 questions and recurring reports a month. The analytics queue runs days long. Dashboards refresh late. Stakeholders ping analysts on the same metrics weekly.
Agent fit
Data Analyst activates NL-to-SQL and report distribution. Validated answers return in minutes; recurring dashboards ship on cadence; analysts shift to modelling and insight.
Outcome
Expected outcomes at this volume: query turnaround sub-minute on routine, report distribution coverage above 98%, analyst hours recovered weekly.
Scenario 2 · Services · 800-2000
1,200 queries or reports handled / month
Starting capabilities
Situation
A 1500-person services firm handles 1200 queries and reports a month across engagement-mix, utilization, and margin metrics. Pipeline breaks surface days late. Anomalies land in leadership decks before the data team sees them.
Agent fit
Data Analyst activates all four capabilities. Questions answer in minutes; recurring reports ship on cadence; pipeline breaks surface in hours; anomalies flag with contributing-factor context.
Outcome
Expected outcomes: cycle-time reduction 50-70% on ad-hoc queue, pipeline-health detection lead time in hours, metric anomaly time-to-flag in minutes.
Scenario 3 · Subscriptions · 40-100
200 queries or reports handled / month
Starting capabilities
Situation
A 70-person subscriptions business fields 200 questions and recurring reports a month with a two-analyst team. Stakeholders ping on the same cohort questions weekly. Board decks get assembled the night before.
Agent fit
Data Analyst activates NL-to-SQL and report distribution. Validated answers return in minutes against the governed warehouse; board and operating reports ship on cadence; analysts shift to modelling.
Outcome
Expected outcomes at this volume: query turnaround sub-minute on routine, report distribution coverage above 98%, analyst hours recovered weekly.
Scenario 4 · eCommerce · 250-800
2,200 queries or reports handled / month
Starting capabilities
Situation
A 450-person eCommerce brand handles 2200 queries and reports a month across merchandising, marketing-mix, and margin metrics. Pipeline breaks surface in dashboards before the data team sees them. Anomaly patterns reach leadership without context.
Agent fit
Data Analyst activates NL-to-SQL, data-quality monitoring and anomaly detection. Questions answer in minutes; pipeline breaks surface within hours of occurrence; anomalies flag with contributing-factor context before leadership sees them.
Outcome
Expected outcomes: query turnaround sub-minute on routine, pipeline-health detection lead time in hours, metric anomaly time-to-flag in minutes.
Query turnaround
Sub-minute on routine; under an hour on multi-source
Report distribution coverage
Above 98% on cadence
Pipeline-health detection lead time
Hours, not days
Metric anomaly time-to-flag
Minutes to hours
Weekly maintenance
2-4 hours
Query lineage
every answer logged with semantic-model reference and validation pass
How it works
Workflow summary
The agent picks up analytics work from triggers — question posted, report schedule due, pipeline signal observed, anomaly detected — and produces the artifact with analyst review on novel or sensitive items.
Exceptions
Novel-metric definitions, sensitive breakdowns, PII-adjacent cuts, and material anomalies route to the analyst with annotated context and query lineage.
When humans step in
Humans step in on novel metrics, sensitive breakdowns, PII-adjacent cuts, and material-impact anomalies.
Connected systems
Agent operates inside the warehouse, semantic layer, BI tool, and messaging. Translates business questions into validated SQL, distributes recurring reports, watches pipeline and schema health, and flags metric anomalies — logs every query and artifact with lineage.
Data inputs
Query logs, metric definitions, dashboard specs, pipeline metadata, anomaly thresholds. Writes query results, report deliveries, pipeline-health findings, and anomaly alerts back to the BI tool and messaging with audit trails.
Decision logic
Uses semantic-model lookups, metric-dictionary matching, pipeline-signal thresholds, and anomaly-detection rules to decide auto-answer, draft-for-review, or escalate-to-analyst.
Readiness
Warehouse connection current, semantic model documented, BI tool wired, anomaly thresholds calibrated.
Integrations
No new systems to learn. The role connects to the platforms your team already uses.
What "working" looks like
Query turnaround target range
Sub-minute on routine
Median time from business question to validated SQL answer with lineage.
Source · Agent execution log
Report distribution coverage above target
Above 98%
Share of recurring dashboards and digests delivered on cadence.
Source · BI tool delivery log
Pipeline-health detection lead time under target
Hours, not days
Median time from pipeline break or schema drift to analyst-routed finding.
Source · Agent execution log
Metric anomaly time-to-flag under target
Minutes to hours
Median time from metric deviation crossing threshold to routed alert with contributing-factor context.
Source · Agent execution log
Governance & compliance
AI Act posture
Subject to transparency obligations: clear AI disclosure to end users where the agent interacts directly.
GDPR legal basis
Legitimate interest
DPIA
Not required for this role's scope.
Questions we get
An AI role priced per query or report handled. It translates business questions into validated SQL, ships recurring dashboards to stakeholders, watches pipeline and schema health, and flags metric anomalies with contributing-factor context. Same scope as a data analyst hire, priced per artifact.
Pure usage: EUR 0.80-3.50 per query or report handled. Launch fee covers warehouse-connection setup, semantic-model capture, BI-tool wiring, and anomaly-threshold calibration.
No. The agent runs read-path queries against the governed warehouse and writes artifacts — answers, reports, findings, alerts — back to the BI tool and messaging. Pipelines and models stay owned by the data team.
Snowflake and BigQuery on the warehouse side. Looker, Metabase, and Hex on the BI and analytics workspace side. dbt handles the transformation layer. Segment is supported for event data.
On novel metrics, sensitive breakdowns, PII-adjacent cuts, cross-domain ambiguity, and material-impact anomalies. Analysts keep the final word on new metric definitions and business-impact calls.
Typical 21-35 days. Faster with a documented semantic model, an approved metric dictionary, and a governed warehouse already wired.
Chat opens with your role context already loaded. Scope a launch set of capabilities, review integrations, and get a timeline in one conversation.