Ask your warehouse a question. Get an answer you can check.
SystemIT agents work on top of BigQuery, Snowflake and PostgreSQL. They write the SQL, check its cost before running it, query your data read-only, and show their work — so your data team stops being a ticket queue.
Why did revenue drop last week?
- Matched 3 tables and 2 metric definitions0.6s
- Wrote SQL with a partition filter2.1s
- Dry run: 1.2 GB, under the 5 GB budget0.4s
- Ran on a read-only connection3.8s
SELECT region, SUM(IF(order_date >= '2026-09-28', revenue, 0)) AS this_wk, SUM(IF(order_date < '2026-09-28', revenue, 0)) AS last_wk FROM sales.orders WHERE order_date BETWEEN '2026-09-21' AND '2026-10-04' GROUP BY region
Revenue fell 8.4% week over week. 71% of the drop is in APAC, where a payment-provider outage on Oct 2 cut completed orders.
Data teams spend their week on work that repeats
The same questions, again
Business teams wait days for numbers that take an analyst ten minutes — once the analyst gets to the ticket.
Pipelines fail quietly
A late load or a null spike surfaces only when someone notices a dashboard looks wrong.
Warehouse bills creep up
Full-table scans and forgotten tables add cost that nobody owns until the invoice arrives.
Three agents, one set of guardrails
Each agent takes one kind of repetitive work off your data team. All of them share the same read-only access, cost checks and approval rules.
Analytics agent
Answers questions in plain language with a chart, the exact SQL it ran, and a short explanation.
- SQL grounded in your metric definitions
- Root-cause and trend analysis
- Answers in Slack, email or the web app
Data management agent
Keeps your warehouse documented and cost-efficient. Every change waits for a person to approve it.
- Table and column docs, kept current
- Expensive query review with rewrites
- Schema change and lineage tracking
Data quality agent
Watches your pipelines, spots anomalies and failed loads, and drafts a fix before stakeholders notice.
- Freshness, volume and null-rate checks
- Failure triage for Airflow and dbt
- Incident summaries with a proposed fix
From question to checked answer
Every step has a guardrail your data team controls. Nothing runs that hasn't been checked first.
Connect
Connect your warehouse, dbt and Airflow with a read-only service account. Deploy in your own cloud if you need to.
Least-privilege access
Understand
The agent maps your tables, metrics and lineage, and learns the definitions your team already uses.
Your definitions, not guesses
Reason and check
It plans the analysis, writes SQL, and dry-runs it for cost and correctness before anything executes.
Cost budget per query
Answer
You get the result with a chart, the SQL and the assumptions — in the web app, Slack, or through the API.
Every action logged
Built on Claude
Claude is the reasoning engine behind every SystemIT agent. We chose it for reliable tool use, strong SQL, and the long context needed to hold a whole warehouse schema at once. Our own layer makes sure nothing it produces runs unchecked.
What Claude does
- Plans the analysisBreaks a business question into the queries needed to answer it.
- Writes SQL and calls toolsUses tool calls to search the schema, dry-run queries and fetch results.
- Explains the resultTurns numbers into a short answer with the reasoning and assumptions spelled out.
What SystemIT adds
- A semantic layerYour metric definitions and table lineage, given to Claude as context.
- A validation layerDry runs, cost budgets and result checks on every query Claude writes.
- Access control and auditRead-only by default, human approval for any write, and a log of every action.
Where teams use it first
Self-serve answers for business teams
Marketing, sales and operations get numbers in minutes instead of waiting in the data team's queue.
Warehouse cost control
Find expensive queries, missing partition filters and unused tables, with a concrete rewrite for each.
Reliable pipelines
Catch late or broken loads early, with a plain-language incident summary and a proposed fix.
Documentation and onboarding
A living catalog of tables and metrics, so new analysts are productive in days, not weeks.
Data engineers building the agents we wanted
We spent years running large ETL/ELT pipelines on BigQuery. We know the ad-hoc queries, the silent failures and the surprise bills first-hand — so we are building agents that handle them safely, with the data team in control.
- CompanySystemIT
- Based inSeoul, Republic of Korea
- FocusAI agents for analytics and data operations
- Built onClaude
- Contact[email protected]
Tell us about your data stack
Share your warehouse and the problem you want solved. We reply within two business days.
Prefer email? [email protected]