Product

One engine, five doors

Someone typing a question, an analyst approving a definition, an AI assistant calling out to your data, a question that spans three systems, and your own product embedding all of it. Every one runs through the same checks. There is no second, looser path that exists because it was quicker to build.

One semantic layerOne set of row-level rulesOne audit trail

Asktalk to your data with NexatronConversational analytics for your business data: ask in chat or by voice, review the query evidence behind each answer, and ask follow-up questions.
Semantic layerAI semantic layer for consistent business metricsA semantic layer for business metrics: define metrics, dimensions and joins once in MetricQL, and keep certifying a definition a human decision.
Agents & MCPMCP server for governed enterprise analyticsNexatron exposes an MCP server so agents like Claude query enterprise data under the same row-level security, certified metrics and audit as a person.
FederationCross-source analytics across CRM, ERP and warehousesAnswer questions across a CRM, an ERP and a warehouse without a migration first, and require a certified join key between sources when you choose to.
EmbedEmbedded AI analytics for your applicationPut governed natural-language analytics into your own application with the @nexatron/chat SDK, with tenant isolation enforced at the query layer.

Every page here states what it won’t do

A platform that answers everything has no way to tell you when it is guessing, and the answer you cannot check is the one that costs you.

How we measure answer quality