AI Solution

Conversational dashboards

A chat layer on top of your data. Ask in plain English, and it writes the query, builds the chart, and answers in seconds, remembering the whole conversation.

Every question becomes a ticket

Every real question about the business turns into a ticket. "Can analytics pull revenue by region for the last six quarters?" "I need a chart for the four o'clock review." It goes into a queue, and the answer comes back a day later, often after the decision has already been made without it.

The data is right there in the warehouse. The bottleneck is the translation: from a plain question, to a query, to a chart someone can read. That translation is a skilled person's afternoon, every single time, and there are only so many of those people.

So we put a chat layer on top of your data. Anyone types a question in plain English, and it writes the query, runs it against live data, and renders the chart in the conversation, in seconds. Ask a follow-up and it remembers, so the second question builds on the first, like an analyst who never leaves the room.

The architecture, in one picture

From a plain-English question to a chart in the chat, in seconds.

Five steps and a loop. Someone asks, it understands the question in context, queries your data, builds the visual, and answers, then remembers the answer for the next question.

Ask

A question, in plain English

No SQL, no ticket
No analytics queue

Understand

Understood in context

Your domain language built in
The thread remembered

Query

Query your data

The query written for you
Run against live data

Build

Build the visual

Chart, table, or dashboard
Rendered on the fly

Answer

Answered in the chat

There in seconds
Saved to your library

Source

Your data

Warehouse or store, in sync

How it begins

Anyone asks, in plain English.

Your team types a question the way they would ask a colleague, no SQL and no ticket to the analytics queue. It works on your domain language, so "revenue", "region", and "last quarter" mean what they mean inside your business.

Where the AI earns its keep

It writes the query and builds the chart.

Claude turns the question into a query, runs it against live data, and renders whatever answers it best: a number, a table, a chart, or a full dashboard, on the fly in the conversation.

Why it feels like a conversation

It remembers, so follow-ups just work.

Ask "now split that by channel" or "what about last year" and it carries the thread, the way an analyst sitting next to you would. The useful views get saved to a library; the one-offs disappear on their own.

The blueprint

What makes it work.

The data pull and the rendering are connectors into systems you already run. The AI earns its keep in the translation: understanding the question, writing the query, and choosing the right way to show the answer.

Live data layer. Your warehouse or data store is kept in sync and queryable, so every answer is built on current numbers, not a stale export. Your warehouse
Question understanding. Claude reads the question against your domain language and the thread so far, so "revenue by region last quarter" resolves to exactly what you mean. Claude
Query, written for you. The question is turned into a query and run against live data, so nobody on your team writes SQL or waits on someone who does. Claude
Rendered on the fly. The answer is built as a number, table, chart, or full dashboard, whatever the question needs, live in the chat. Claude
Context that carries. Every answer is remembered, so follow-ups build on the last instead of starting over. Claude
A library that grows. Useful views are saved and reused, one-offs are discarded, so the dashboard collection grows around what your team actually asks.