Ask it in plain language,
it answers with real SQL, not a guess
Someone on the team wants a number, say how many orders came from one city last month. Getting it usually means waiting for whoever can write the query, if they are free. A SQL analyst agent answers directly in chat, with a real query against your warehouse. One SELECT, your approved mart only, forced limits and timeouts. It is built on the same pattern as the AI analyst we run on our own data.
Why the data person becomes a bottleneck
A simple business question, like how many orders came from one city, usually needs someone who knows SQL and has time to write the query right now. If that person is busy, in a meeting, or asleep in a different timezone, the question waits. The decision it was meant to inform waits with it.
A lot of these questions are genuinely simple once someone writes the query. But writing even a simple query correctly against a real schema takes real knowledge: the right joins, the right filters. Only a few people on the team actually have that knowledge.
Ad hoc requests pile up on whoever is the designated “data person.” That turns them into a human query interface. It leaves less time for the deeper analysis only they can do.
How a question becomes an answer
The agent sits in Telegram or Slack and answers plain-language business questions by writing and running a real SQL query against your warehouse. Then it explains the result in plain language too, not just a raw table. It works against an approved mart built specifically for this kind of question. A single SELECT is enforced at the database layer, with a forced row limit and a timeout. Even a badly worded question cannot run an expensive or dangerous query.
When a question is genuinely ambiguous, “last month” could mean different things depending on timezone and close date, it asks a clarifying question. It does not guess and return a confidently wrong number.
Typical scope: ad hoc business questions against an existing, modeled warehouse. One of our own builds runs exactly this pattern. An AI analyst in Telegram answers with real SQL, across a warehouse that joins seven sources for two brands in two countries.
Where your team draws the line
Interpreting an ambiguous or sensitive question, and any decision based on the number, stays with your team. Deciding which mart and which fields are safe to expose to the agent is a joint step. We work through it with you before anything goes live.
What keeps the queries safe
Read-only access is enforced at the database layer: a single SELECT against an approved mart, never the raw production database. Every query carries a forced row limit and timeout, so nothing can run long or return more than intended. Every question asked and every query run is logged, attributed to who asked it and when.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Agency runs it | from $2,000 + support plan | Agent built, tuned and supervised by us, monthly query log review | 2 to 3 weeks |
| Full control, handover-ready | from $3,000 | Same agent on your own warehouse and chat platform, your team maintains the mart | 3 to 4 weeks |
Running cost is usually $15 to $50 a month in model and warehouse query usage.
Related
See the analytics service page and the AI agents service page for the surrounding build. In the same group, the BI and dashboard agent and the data engineering and ETL agent cover the warehouse side this agent reads from. The data quality agent keeps that warehouse trustworthy. The real build behind this page is the two-brand analytics hub case study. There, an AI analyst answers business questions in Telegram with real SQL, against a warehouse joining Meta, TikTok Shop, Shopify, LINE, GA4, a CRM and a factory ERP.
Tired of being the designated “can you pull this number” person? Get in touch and we will look at what questions come up most.
FAQ
How much does a SQL analyst agent cost?
From $2,000 to wire into an existing warehouse with a defined mart, live in 2 to 3 weeks. If the warehouse itself needs building first, that is a separate data engineering and ETL step, typically adding $3,000 to $5,000.
How long before the team can actually ask it questions?
2 to 3 weeks. Most of that time goes into building and testing the guarded query layer against your real schema, so answers are accurate before anyone relies on them.
Which tools does it work with?
Telegram or Slack as the interface, and your existing warehouse, PostgreSQL in most of our builds. It does not need a new BI tool. It sits on top of what you already have.
What if it gets the question wrong or the answer is off?
It asks a clarifying question when a request is genuinely ambiguous, rather than guessing at intent. Every query it runs and every answer it gives is logged, so a wrong answer traces back to the exact query and is easy to correct.
Can it change data, or just read it?
Read-only, enforced at the database layer, not just in the prompt. It can only run a single SELECT against an approved mart, with forced row limits and a timeout. It cannot write, delete, or run an expensive query that slows down anything else.