AI Agents for Data Analysis

Your data team is backlogged. Every department wants dashboards, ad-hoc queries, and weekly reports. AI agents do the repetitive analysis work so your analysts focus on the questions that actually move the business.

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The Problem

Data teams are bottlenecked. Product wants a funnel report. Finance wants revenue breakdowns. Marketing wants attribution numbers. Each request takes days because analysts are buried in queue. By the time a report lands, the decision window has closed.

How AI Agents Solve It

An AI agent connects to your data warehouse, understands your schema, and generates reports on request or on schedule. It flags anomalies the moment they appear instead of waiting for someone to notice. Business teams ask questions in plain English and get answers with the SQL and charts to back them up.

How It Works

Connect and Map

The agent connects to your data warehouse (Snowflake, BigQuery, Redshift, or Postgres). It maps your schema, understands table relationships, and learns your business metrics definitions.

Query and Analyze

Users ask questions in natural language. The agent writes SQL, runs it, and returns results as tables or charts. It also runs scheduled reports and anomaly detection on your key metrics.

Alert and Explain

When a metric moves outside normal range, the agent sends an alert with context. It shows what changed, when it started, and which segments are affected.

What You Get

Self-serve analytics

Business teams get answers in minutes instead of filing a ticket and waiting three days for an analyst.

Anomaly detection that works

The agent monitors your key metrics 24/7 and alerts you when something changes. No more discovering a drop two weeks after it started.

Analysts do real analysis

Your data team spends time on strategic projects instead of pulling the same revenue report every Monday.

Consistent metric definitions

The agent uses your defined metric logic every time. No more conflicting numbers from different teams running different queries.

90%

reduction in ad-hoc report requests

5 min

from question to answer

4 wks

to production deployment

Related Use Cases

Report GenerationView →

Research AutomationView →

Frequently Asked Questions

Does the agent write SQL directly?+

Yes. It generates SQL based on your schema and metric definitions, runs it against your warehouse, and returns the results. Your data team can review the queries anytime.

What if the agent writes a bad query?+

All queries run with read-only permissions and resource limits. If a query would scan too much data, the agent flags it and suggests a more efficient approach.

Can it handle complex multi-table joins?+

Yes. The agent understands your schema relationships and can join across multiple tables. It handles window functions, CTEs, and subqueries.

How does anomaly detection work?+

The agent learns normal patterns for each metric over time. When a value falls outside the expected range, it sends an alert with the deviation size, when it started, and which dimensions are affected.

Ready to put AI agents to work?

We build production-grade AI agents for your specific workflows. Most projects go live in 4-6 weeks.