An analyst who answers in Telegram,
and checks the SQL before it checks the box

What it is and who needs it

An AI business data analyst answers real questions about your numbers, which campaign is actually profitable, which product line is slipping, what changed last week, by querying your own data directly and writing the answer in plain language. It is for a team that has data but not the time or the staff to query it properly every time a question comes up. It is not a replacement for a dashboard you check daily; it is for the questions nobody built a dashboard for, because nobody knew to ask them until now.

What is inside

Connectors pull from whatever you actually run on, ad platforms, a CRM, order data, an ERP, into a warehouse with the joins and cost logic worked out once rather than recalculated by hand every time. A SQL guard sits between the model and the database: every generated query passes through it before execution, blocking writes, unscoped scans and anything outside a user’s permitted role. Questions arrive as plain chat messages in Telegram, Slack, or a web interface, and answers come back in plain language with the underlying number traceable to its source. A log of every question and every query means a number can always be checked, not just trusted.

How we build it

We start by mapping where your real data already lives and what the correct joins and cost logic actually are, since a warehouse with wrong joins produces confidently wrong answers faster than no warehouse at all. The SQL guard gets built and tested against intentionally bad queries before a single real question reaches it, not added as an afterthought once something goes wrong. The first version connects one data source and proves out the chat interface and the guard; additional sources get layered in once that foundation is solid. We run a tuning period reading the question log weekly, catching cases where the model misunderstood intent before they become a trusted wrong answer repeated across your team.

What to watch

Wrong joins are the real danger here, a warehouse that silently double-counts revenue or misses a cost component produces an answer that sounds authoritative and is simply incorrect. This is why the SQL guard and the join logic get validated against numbers your team already knows are correct before the chat layer goes anywhere near a real question. Role-based access also needs real testing, not just configuration, since an analyst answering from data a person should not see is a bigger problem than a wrong number. Expect to revisit the guard rules as new data sources get added later.

Timeline and price

Running cost on top of the build is usually $25 to $90 a month in warehouse hosting and model calls, depending on data volume and query frequency.

What you own at the end

You own the warehouse, the connectors, the guard rules and the full source code, running on infrastructure in your name. Documentation covers the schema and every join’s logic, so a future hire, or our team on a maintenance basis, can extend coverage without reverse-engineering what is already there.

Related

Pairs with data warehouse and BI product for the dashboard layer most teams still want alongside a chat interface, and the AI monitoring and alerting product for the watch-and-flag side of the same data. See the analytics service page for audits and warehouse builds beyond the AI layer. Real builds: the analytics hub with AI analyst case study and the factory ERP recovery case study, where the recovered data became the warehouse this kind of analyst runs on. Tired of being the only person who can answer a data question? Get in touch and tell us what you query most often by hand.