Data Analyst Agent
Natural-language interface to your warehouse — ask for charts, segments, and anomalies in plain English.
Features
5
Tech
14
Steps
5
Data Analyst Agent
Capabilities
What this agent does
Every capability is shipped end-to-end — not a feature flag. You get a working slice on day one and we expand from there.
Feature 01
NL to SQL
Type 'top 10 customers by churn risk last quarter' — get the chart.
Feature 02
Schema discovery
Maps your tables, foreign keys, business semantics.
Feature 03
Scheduled reports
Daily/weekly summaries to Slack or email.
Feature 04
Anomaly alerts
Spots unusual spikes/drops, pings ops on Slack.
Feature 05
Chart generation
Picks the right chart type per question automatically.
Stack
14 technologies we use
Production-tested tools chosen for reliability, observability, and developer velocity. Every piece is swappable.
System at a glance
How the agent flows
A simplified view of what comes in, what the agent does with it, and what it produces. Each arrow is observable end-to-end.
Inputs
Agent
Data Analyst Agent
Plan → tool-call → reflect, with approval gates.
Tools the agent calls
Outputs
Engagement
How we roll it out
A 5-step process from kickoff to handover. Every step has a deliverable you can sign off on.
- 01
Warehouse connect
Read-only credentials to your DW with row-level security.
- 02
Semantic layer
Annotate tables/columns with business meaning.
- 03
Train on past queries
Replay your team's last 6 months of SQL.
- 04
Chat UI rollout
Internal app + Slack bot with charts inline.
- 05
Alert setup
Define KPIs to monitor; agent emails when they move.
Outcomes
What you can expect
Real numbers we see when Data Analyst Agent ships on top of an actual workflow. Specifics vary with starting baseline, data quality, and scope — but these are the ranges.
Outcome 01
Minutes
Time-to-insight
Down from days waiting on the data team.
Outcome 02
+200%
Self-service rate
Non-analysts get answers themselves, data team focuses on hard work.
Outcome 03
Hours
Anomaly detection
Catches issues your dashboards would surface days later.
Sample use cases
What this looks like in practice
Three concrete scenarios where teams deploy Data Analyst Agent today. Your engagement starts with one of these (or your own variant) and expands from there.
PM asks 'why is churn up?'
Plain-English question → agent picks the right tables, runs cohort analysis, surfaces the 2 segments driving the lift, plots them, posts to Slack with context.
Daily standup briefing
Every morning, leadership gets a 1-pager: yesterday's KPIs, week-over-week shifts, anomalies, suggested questions to ask the team — fully auto-generated.
Weekly cohort + funnel review
Marketing + product run weekly reviews against fresh data instead of waiting for the analyst — agent prepares cuts, the team interprets and decides.
Typical engagement
From kickoff to live in 4-8 weeks
Most Data Analyst Agent engagements follow this shape. We give you a firm quote after a 30-minute scoping call, and the price is fixed up-front so you can budget.
2-3 weeks
Pilot
- Discovery + playbook write-up
- Tool design + guardrails
- Eval suite + sandbox deploy
2-4 weeks
Production
- Hardened deploy on your stack
- Observability dashboard + alerts
- Two-week supervised mode
Ongoing
Handover & support
- Runbook + team training
- Monthly tuning + eval review
- Pause or expand any time
Indicative range
$2,500 — $7,000
Full project, scoped + fixed up-front. Includes pilot, production, and 30 days of post-launch support.
Or hire us by the week
from $900/wk
Embedded retainer — best for evolving scopes.
Ready to ship Data Analyst Agent?
Tell us about your data, your tools, and what success looks like. We come back within one business day with a clear plan, price, and timeline.