AI Data Analyst
Ask questions about your business data in plain language.
Request DemoMost companies already hold the data needed to answer their day-to-day questions. What they lack is a fast path between the question and the answer. The data sits in a production database, a finance spreadsheet and a warehouse table, and reaching it means writing SQL, knowing which table is authoritative, and being available to do it.
AI Data Analyst removes that bottleneck. It connects to your existing sources, translates a plain-language question into the underlying query, runs it, and returns both a chart and a written explanation of what the numbers show. The person asking does not need to know the schema, and the analyst does not need to be interrupted.
It is deliberately read-only. The tool never writes to your systems, so it can be opened to a wider group than a database client normally would be. Every query it generates is logged and can be inspected, which means an answer can always be traced back to the exact query that produced it.
The problem
Getting a straight answer from business data usually means waiting on someone who knows how to query it. The question is simple — which region slipped last quarter, why did returns spike in March — but answering it requires knowing the schema, writing the query and formatting the result. Requests queue behind whoever holds that knowledge, and by the time the answer arrives the decision has often already been made without it.
The solution
AI Data Analyst connects to your data sources and lets anyone on the team ask questions in plain language, returning charts and plain-English explanations. It generates the query, runs it against the source of truth, and shows its work: the chart, the narrative, and the query itself. Recurring questions can be scheduled as digests so the answer arrives before anyone thinks to ask.
How it works
- 1
Connect a data source — a PostgreSQL or MySQL database, a warehouse table, a Google Sheet or an uploaded CSV. Connections are read-only by design.
- 2
Describe your data model once. Table and column descriptions help the AI pick the right source when several could plausibly answer the same question.
- 3
Ask a question in natural language, the way you would ask a colleague: which products lost margin this quarter compared to last?
- 4
The AI generates the underlying query, runs it against your source, and returns the result as a chart alongside a written explanation of what it shows.
- 5
Inspect the generated query whenever you need to. Every question and its query are logged, so any figure can be traced back to how it was produced.
- 6
Schedule the questions you ask repeatedly as a recurring digest, delivered by email to the people who need them.
Features
- Natural language querying
- Automatic chart generation
- Scheduled report digests
- Anomaly and trend detection
- Generated query always visible
- Multi-source questions in one answer
- Saved questions and shared dashboards
- Follow-up questions that keep context
Benefits
- — No SQL required for common questions
- — Faster decision-making across teams
- — Consistent reporting without manual work
- — Analysts freed from repetitive ad-hoc requests
- — Answers traceable to the query that produced them
- — One definition of a metric, shared across the team
Where it fits
Typical situations this application is built for.
Weekly commercial review
A sales lead asks for revenue by region against the same week last year, drills into the region that moved most, and shares the resulting chart in the team channel — without opening a ticket with the data team.
Investigating an anomaly
Returns rise unexpectedly in one product line. Rather than waiting for a scheduled report, the operations manager asks a series of follow-up questions in sequence, each keeping the context of the previous one, until the pattern is isolated to a single supplier batch.
Monday morning digest
The five questions a finance team asks every week are saved and scheduled. The answers, with charts and commentary, arrive by email before the Monday meeting instead of being assembled by hand on Friday afternoon.
Opening data to non-technical teams
Marketing and support get read-only access to the metrics that concern them. Because the tool cannot write to any source and every query is logged, widening access does not widen risk.
Integrations
Security
- — Data stays within your connected source
- — Read-only connections — the tool never writes to your systems
- — Role-based access control
- — Query audit logging
- — Per-source credential scoping
- — Column-level exclusions for sensitive fields
Pricing
Starter
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- — 1 connected data source
- — Unlimited questions
- — Automatic chart generation
- — Generated query always visible
- — Email support
Business
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- — Unlimited data sources
- — Scheduled digests
- — Multi-source questions
- — Role-based access control and audit logs
- — Priority support
FAQ
Does it modify our underlying data?
No — it only runs read queries and never writes to your connected sources. Connections are established with read-only credentials, so the permission to write is not merely unused, it is not granted in the first place.
How do we know the answer is correct?
Every answer shows the query that produced it. Anyone who can read SQL can verify the logic, and the full history of questions and queries is retained so a figure can be re-checked later.
What happens when the AI does not understand a question?
It says so rather than guessing. Ambiguous questions — where two tables could plausibly answer — prompt a clarification instead of returning a confident but wrong number.
Do we need to restructure our database first?
No. It reads your schema as it stands. Adding short descriptions to tables and columns improves accuracy, but that is configuration rather than migration, and it can be done progressively.
Can we restrict which data is reachable?
Yes. Access is scoped per source and per role, and individual columns can be excluded so that fields such as salaries or personal identifiers are never queryable.
Can it answer a question that spans several sources?
Yes, on the Business plan. A question that needs figures from a database and a spreadsheet is resolved against both, and the answer states which source each figure came from.
How long does setup take?
Connecting a first source and asking a first question is typically a same-day exercise. The work that takes longer is describing your data model well, which is what raises accuracy on ambiguous questions.
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