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AI agents for internal reporting

An AI data analyst agent that compiles your reports and flags what changed.

A custom agent that pulls from your warehouse and tools, assembles recurring KPI, exec, and board reports on schedule, flags anomalies in plain language, and answers follow-up questions, with a human gate on anything that ships. Here is what it does, where it beats a dashboard, and the one case where a BI product is the better buy.

In one sentence

An AI data analyst agent is software that uses a large language model to query your data warehouse and connected tools, compile recurring reports and company health-checks, surface anomalies and answer questions in natural language, and route its output to a human for approval before it ships.

Human gateOn everything that ships
Model-agnostic
In your cloudYour warehouse, your auth
You own itCode and runbook
Free AI assessment

Bring one messy workflow. We will show whether an agent, automation, SaaS product, or no build is the right next move.

Find your first agent workflow
01

What a data analyst agent does that a dashboard doesn't

A dashboard shows you numbers and waits. An agent reads them. It queries the warehouse, notices that churn ticked up in one segment, writes the sentence explaining it, and asks whether to flag it to the team. It produces the narrative an analyst would, on schedule, not just the chart.

  • Dashboard: static charts you interpret yourself
  • Agent: queries, explains, and drafts the takeaway
  • Runs on a schedule, not on someone remembering
Outcome dashboard
-42% cycle time31% fewer escalations2.8x ROI signal
02

How it compiles a recurring report

The agent connects to your warehouse and the tools that hold the rest of the truth: the CRM, billing, product analytics, finance. On a schedule it pulls the metrics, compares them to prior periods and targets, writes the commentary, and assembles the report in your format. A person reviews and approves before it goes to the board or the exec list.

  • Reads from warehouse plus CRM, billing, analytics
  • Compares to prior periods, targets, and forecasts
  • Drafts the report in your template and tone
Proof of value
-42% cycle time31% fewer escalations2.8x ROI signal
03

Anomaly flags and natural-language Q&A

Between scheduled reports the agent watches the same metrics and flags movement that breaks the pattern: a spike in refunds, a region that fell off, a cohort behaving oddly. Anyone can ask it a question in plain English, "why did MRR dip in March," and get an answer grounded in the actual data, with the query it ran shown.

  • Flags anomalies against baseline, not fixed rules
  • Answers ad-hoc questions in natural language
  • Shows the query and source behind every answer
Control room
approval queue3 cases need human sign-off

Low confidence, policy exception, or protected data.

01Source checked02Risk scored03Human approved04Audit trail saved
04

When an off-the-shelf BI tool is the better buy

If your need is self-serve dashboards, ad-hoc exploration, and pixel-perfect visualizations for a broad set of users, buy a BI product. Looker, Power BI, Tableau, and similar tools are mature, cheaper than a custom build for that job, and your team already knows them. A custom agent earns its cost only when you need automated narrative, anomaly detection, and reports compiled and written for you, not just displayed.

  • Buy BI for self-serve dashboards and exploration
  • Buy BI when you need broad, governed visualization
  • Build an agent when you need written, automated reporting
Release gate
Eval suitePolicy checkHuman fallbackRelease

p95 latency 1.2s

eval pass 12/12

rollback ready

05

The human gate, and who owns it

Nothing reaches a board deck or an investor update unreviewed. The agent drafts, a person approves, and every number traces back to the query that produced it. We build it into your stack with that gate, evals on the metrics that matter, and full audit trails. You own the code and can run it without us.

  • Human approval before any report ships
  • Every figure traceable to its source query
  • Deployed in your cloud, you own the code
Handover state
handoff packageCode, runbook, evals, dashboard
owned by your team
Source repoRunbookEval suiteOwner training

Access your auth

Data your environment

Ops monitor or handoff

Where it pays off

Concrete places agents earn their keep.

01
ticket82% resolved
#4821Damaged ordernew
Agent

Policy matched. Refund ready for approval.

Lookup orderApprove refund
human-gated

Weekly KPI report

Pulls the core metrics, compares to last week and target, and writes the what-changed-and-why for the leadership channel.

02
ledger31 hrs saved
Stripe$18,240matched
Bank$18,240clear
audit-ready

Monthly board pack

Assembles the recurring board metrics and commentary into your template, ready for a human to review and finalize.

03
pipeline+18% coverage
LeadFitBrief
91

account score

CRM updated
crm synced

Exec daily digest

A short morning brief: yesterday's numbers, anything off-pattern, and the one thing worth looking at, in plain language.

04
reviewHIPAA path
Credentialing packet3 checks passed
Human review required
review queue

Company health-check

A periodic scan across revenue, retention, pipeline, and ops that surfaces where the business is drifting from plan.

05
extract14 fields
Invoice no.TotalDue date
2 exceptions routed
exceptions out

Anomaly alert

Flags a refund spike, a churned key account, or a metric breaking its baseline the moment it shows up, not at month-end.

06
answerfresh docs
Answer drafted3 cited sources
HR policyOkta SOP
sources shown

Ask-the-data Q&A

Answers "why did signups drop in the EU last week" in natural language, grounded in the warehouse, with the query shown.

FAQ

Common questions.

What is an AI data analyst agent?+
It is software that uses a large language model to query your data warehouse and connected tools, compile recurring reports, flag anomalies, and answer questions in natural language. Unlike a dashboard it writes the narrative and explains what changed, and it routes its output to a human for approval before anything ships.
How is this different from a BI tool like Tableau or Power BI?+
A BI tool shows you charts and lets people explore data themselves. An agent reads the data, writes the commentary, detects anomalies, and compiles the report for you on a schedule. If you mainly need self-serve dashboards, buy a BI product; if you need reporting that is written and automated, build the agent.
Will it just make up numbers?+
No. Every figure traces back to the actual query the agent ran against your warehouse, and that query is shown alongside the answer. We add evals on the metrics that matter and a human approval gate, so nothing reaches a board deck or exec update unreviewed.
What can it connect to?+
Your data warehouse (Snowflake, BigQuery, Redshift, Databricks and similar) plus the tools that hold the rest of the truth, such as your CRM, billing, product analytics, and finance systems, via APIs and MCP. It reads from your existing stack rather than requiring you to move data into a new platform.
Do we still need analysts?+
Yes. The agent removes the recurring grind of pulling, comparing, and writing up the same reports, so analysts spend time on the harder questions and judgment calls. It drafts; people review, decide, and act on what it surfaces.
How long until a first report is running?+
A first scoped report can be drafting in as little as a few days once we have read access to your warehouse. Production with anomaly flags, evals, and the approval gate depends on how many sources and how much governance are involved, typically weeks rather than quarters.
Production AI agents, shipped with an owner

Want agents like these in your stack?

Book a free assessment, we'll map where an AI agent creates real leverage in your workflows and scope the first one to ship.

Build, deploy, runYour cloudYou own the code