An AI assistant for data analysts: queries from a description, export checks and a report with explanations
The model and the conclusions stay with you. The assistant takes the scaffolding: draft queries from a verbal brief, checking an export for gaps and anomalies, describing a data mart and presenting the result.
What it does
- drafts database queries from a task described in plain words
- checks an export for anomalies and gaps
- documents a data mart for colleagues
- turns the results of a calculation into a report with commentary
- documents the sources and the rules by which metrics are calculated
What it does not do
The limits are set in advance
- does not take responsibility for the model's correctness in your place
- does not draw business conclusions without your review
How much time it frees up
By our reviews, a data specialist spends roughly
15 hours a week on the routine listed above — more than a third of the working week. The assistant does not take all of it: some tasks you will still be checking. But checking something finished is faster than doing it from scratch, and the gain lies in exactly that difference.
How it works
The assistant is deployed alongside your existing workflow and speaks the language of your stack. It does not try to replace the engineering decision — it removes the scaffolding that leaves you only half a day for the decision itself.
How the assistant handles a data analyst's tasks
A draft query from a verbal brief
The requester writes: "revenue by region for the quarter without refunds, compared with last year". From the description of your data schema the assistant writes a draft SQL: joins, filters, aggregates, with comments on which assumptions it made. You fix two conditions and run it — instead of writing from scratch.
The export checked before the requester sees it
The assistant runs the export through a checklist: gaps, duplicates by key, outliers, dates outside the period, totals that do not match the control figure. It returns a list: "in March 3 % of rows have no region, 12 duplicate orders, the maximum receipt is 40 times the median". What to do with that is your call.
A data mart description for colleagues
The mart exists, the documentation does not. The assistant builds the description from the schema and your comments: what the field is, where it comes from, how it is calculated, when it refreshes, who owns it. The format is your documentation template, or a simple table if there is none.
A report with explanations, not a table without words
The calculation is done and needs handing over. The assistant presents the result: what was counted, on what data, what the limitations are, what came out — briefly and in your wording, with charts from your own data. It proposes business conclusions as a draft; you confirm them.
Metric definitions in one place
"Active customer" is counted three different ways in three reports. The assistant builds a metrics dictionary: definition, formula, source, exceptions — and highlights discrepancies between reports so you can close them once.
What the dialogue looks like
You: I need a query: average order value by acquisition channel for September, paid orders only, no test accounts. Schema — as in the orders mart description.
Assistant: Draft ready: orders ⨝ customers on customer_id, filter status = 'paid' and is_test = false, grouped by acquisition_channel. Assumption: "September" by paid_at, not created_at. Keep it?
You: paid_at is right. Add the order count and the channel share.
Assistant: Added COUNT and the share of the total. The query and a short field description are sent.
What we need from you at the start
- A description of the data schema: tables, keys, what the fields mean — existing documentation is fine.
- Your stack: the DBMS and SQL dialect, where you calculate and where you draw charts.
- Two or three of your finished reports — so the assistant repeats your presentation format.
- What the assistant must not do: write to the database, export personal data, send reports itself.
Implementation timeline
The first conversation — which queries and reports repeat week to week. Setup for the schema and stack — four to five days. A week of parallel work: the assistant writes drafts, you mark where its assumptions were wrong; from the second week one or two such corrections remain.
Frequently asked questions
Does the assistant get access to the database?
No. It works with the schema description and the exports you hand it. It writes queries as text; you run them. That rules out accidental writes and data leaks.
No. It works with the schema description and the exports you hand it. It writes queries as text; you run them. That rules out accidental writes and data leaks.
Which SQL does it write?
The dialect of your DBMS — PostgreSQL, ClickHouse, MySQL, BigQuery and others. The dialect is set at the start together with the schema.
The dialect of your DBMS — PostgreSQL, ClickHouse, MySQL, BigQuery and others. The dialect is set at the start together with the schema.
Will it replace the analyst?
No. It speeds up preparation — drafts, checks, presentation — and leaves the task definition, the choice of method and the conclusions to you. By our analyses that frees about 15 hours a week.
No. It speeds up preparation — drafts, checks, presentation — and leaves the task definition, the choice of method and the conclusions to you. By our analyses that frees about 15 hours a week.
How much does it cost?
Task analysis and setup — from 12,000 ₽, 5–7 days. Support and new scenarios — from 4,000 ₽ a month, optional.
Task analysis and setup — from 12,000 ₽, 5–7 days. Support and new scenarios — from 4,000 ₽ a month, optional.
What it costs
Task review and configuration of one assistantfrom ₽12,000 (≈ $142)
An extra task beyond the basic fivefrom ₽3,000 (≈ $36)
Training for you or a member of stafffrom ₽5,000 (≈ $59)
Support and adjustments, per monthfrom ₽4,000 (≈ $47)
Build an assistant like this
Tell us which tasks eat the most of your time, and we will say which of them are worth handing to an assistant first and which are better kept for yourself.
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