Automated Reporting: How to Replace the Monday Spreadsheet
Someone spends every Monday morning copying numbers between tabs. That job can be done by a machine, and done more accurately.
Data and reporting
AI Data Analyst
Managers type questions like "which products sold best in the North last quarter?" and get an answer, a chart and the query behind it. The assistant only reads your data, never changes it, and shows its working so numbers can be checked.
Build time4 to 6 weeks
Fixed priceFrom $3,000
The codeYours, 100%
The problem
Why businesses ask us for this
Your data sits in a database or warehouse, but getting answers out of it depends on one or two people who know SQL. Every question becomes a ticket, and simple ones wait days behind bigger work. Dashboards answer last month's questions, not today's. So decisions get made on gut feel or out-of-date exports. Off-the-shelf chat tools are risky to point at live data, because a wrong query or a too-broad permission can expose or change records.
An e-commerce brand with one busy analyst
Example: A growing e-commerce brand keeps orders, customers and ad spend in a Postgres database, and one analyst fields every question from the founders and marketing team. With the AI data analyst in Slack, the marketing lead asks which campaigns brought in repeat buyers last quarter and gets a table, a chart and the SQL. The analyst reviews the metric definitions once a week and marks trusted queries. Customer emails and addresses are excluded from the tool entirely.
How it works
From the first message to the finished job, step by step
Connect read-only
We connect to your database or warehouse through a dedicated read-only user, limited to the tables and columns you approve. Sensitive fields, like personal contact details, can be excluded or masked entirely.
Teach it your business
We document what your tables mean and define key metrics once, such as revenue, active customer or margin, so the AI uses your definitions instead of guessing from column names.
Question to query to answer
A question is turned into SQL, checked by guardrails for read-only access, row limits and allowed tables, then run. The result comes back as a short answer, a table and a chart.
People check what matters
Every answer shows the SQL and the assumptions behind it. Your data person reviews saved questions, corrects a metric definition once when needed, and marks trusted queries that others can reuse with confidence.
What you get
Features built in from day one
Read-only by design
The database user cannot insert, update or delete, so no question, however phrased, can change your data. Guardrails also cap query time and row counts.
Your metric definitions
Revenue, churn or margin mean what your business says they mean, defined once and reused in every answer, so two people asking get the same number.
Charts on request
Ask for a trend, comparison or breakdown and get a clean chart you can download or pin to a shared dashboard.
Shows its working
The SQL, the tables used and any assumptions appear with each answer, so anyone technical can verify a number before it goes into a board pack.
Ask in Slack or Teams
Questions can be asked in a web app or straight from Slack or Microsoft Teams, with access tied to each person's role.
Scheduled questions
Save a question and get the answer every Monday by email or Slack, a simple report without building a dashboard.
Models and data
Which AI model it runs on
Writing correct SQL against a real schema is reasoning-heavy, so the query step uses a strong model, such as a larger GPT, Claude or Gemini model. We pick it by testing on a list of your real questions with known answers. Cheaper models handle the easy parts: rephrasing the question, labeling charts and writing the short summary. Data safety does not depend on the model behaving, because the database user is read-only and limited to approved tables either way. The model is a swappable setting, and the test list shows quickly whether a new one is better.
Price and timeline
From $3,000fixed quote
Typical build: 4 to 6 weeks. Each question costs a small amount in AI usage, and with a small server the monthly total is usually modest; we estimate it from your team size.
- Written scope and one price before any code
- Working version on a staging link in week one
- Code in your repository from the first commit
- Optional 24-month support plan
Frequently asked questions
How much does a custom AI data analyst cost?
Builds start from $3,000 for one database, a documented set of core tables and metrics, a web or Slack interface and the read-only guardrails. More data sources, role-based permissions or a full dashboard layer add scope. You get a fixed quote after we look at your schema, so you know the cost before any work starts.
Can it change or delete anything in our database?
No. It connects with a database user that only has read permission on the tables you approve, so even a badly formed query cannot insert, update or delete records. On top of that, guardrails reject anything that is not a plain read query and limit run time and row counts. For extra safety, it can query a read replica instead of production.
How long does it take to set up?
Usually 4 to 6 weeks. Most of the time goes into understanding your data: what each table means, which numbers people trust and how metrics are defined. We build a test list of real questions with known answers, tune until results match, then open it to a small group before rolling it out.
Can it give a wrong number?
Yes. It can misread an ambiguous question or join tables the wrong way, and the answer can look confident either way. That is why every answer shows the SQL and assumptions, and key metrics use fixed definitions. Important figures should still be checked by someone who knows the data before they go into a report. Saved, reviewed queries are marked as trusted.
Does our data get sent to the AI provider?
The model sees your table descriptions and the question, and usually a small sample of the query result so it can write the summary. Raw tables are never bulk-uploaded. Calls go through paid API terms that exclude training, and query logs stay on your own cloud account. You can exclude personal data columns, keep results out of the AI step, or use an open-weight model.
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Want this built for your business?
Send us a few real examples of the task. You get a written scope, a fixed price and a timeline within two working days.