Conversational analytics: talk to your data with Nexatron

Ask about a real business decision

Conversational analytics starts with a configured source and a clear definition of what you want to measure. These example questions are a starting point for a pilot, not published customer results.

• Revenue: “Show approved revenue by region for last quarter.” Confirm the revenue definition, fiscal calendar and currency with the report owner.
• Sales pipeline: “Which open opportunities are expected to close this quarter?” Agree on stages, the cutoff date and the records your role can access.
• Follow-up: “Now break that down by region.” Check that the original period and filters still match the question you intend to answer.

You can ask in plain language, typed or by voice, without writing the SQL yourself. A source owner still needs to configure access and business definitions. Compare the answer with an approved reference before using it for a decision.

Claude is a trademark of Anthropic, PBC. Nexatron and dotSolved Inc are not affiliated with, sponsored by or endorsed by Anthropic.

What it does

• The query is part of the answerFor supported SQL sources, natural-language questions use a text-to-SQL workflow. Responses carry the SQL, SOQL or source request that produced them. It sits next to the number rather than in a debug panel, where the person deciding something can read it or hand it to someone who can.
• A confidence signal, and what moved itAnswers carry a score with its breakdown: whether a certified metric was used, how well the question matched the schema, whether validation passed. A low score is shown as a low score rather than smoothed away.
• Governed and ungoverned are visibly differentAn answer built from a certified metric is labelled differently from one built from raw tables. Both can be useful; presenting them with identical authority is how an ad-hoc query becomes a board number.
• Voice in, spoken summaries outAsk by voice and hear the answer read back, with the same governance and the same evidence as a typed question.
• Follow-ups keep the thread"Now break that down by region" resolves against the previous answer rather than starting over, so a line of inquiry behaves like a conversation.

What it deliberately won’t do

Each of these is a case where returning something would have been easy and wrong, so the product is built not to.

• It will refuse rather than approximateAsk for a dimension the connected user cannot see and it says which field it could not read. It does not substitute a similar column and it does not quietly drop the dimension from the breakdown.
• A zero is reported as a zero, not as nothingAn empty result says the filters matched no rows, which is a different statement from the data not existing, and the difference has cost people money.

Common questions

What is conversational analytics?

Conversational analytics, also called conversational BI, lets a person ask a question of business data in everyday language, typed or spoken, and get the answer back as a chart, a table or a sentence, then ask a follow-up that keeps the earlier context. In Nexatron each question is answered under the asker's own permissions, and the answer arrives with the query that produced it and a confidence score.

Is Nexatron a text-to-SQL tool?

For supported SQL sources, Nexatron uses a text-to-SQL workflow, but the model's output is treated as untrusted: it is validated against the semantic layer before anything runs, and the generated SQL is returned with the answer so the translation can be checked. A question it cannot answer within your definitions and permissions is declined or sent back for clarification rather than guessed.

Can an AI data analyst replace the analytics team?

No. Ask answers questions against definitions people have agreed, and it shows the query and evidence so someone can check the result. Deciding what a metric means, certifying it and judging whether an answer is fit for a decision stay with the people who own the numbers.