Fluid dashboards from a prompt
Ask in words for the report you need and get the view on your data, with queries that stay within the boundaries your team has set
What it solves
Decision-makers need numbers when they need them: margin per job over the last six months, where a process is overrunning, how a department is trending. Almost always that number goes through a request to the data team or whoever runs BI, and days pass between the question and the answer.
A fixed dependency forms. Decision-makers wait, the people who build reports end up with a queue of near-identical requests, and every small variant (the same data filtered a different way) sends the whole round trip back to the start.
A dashboard generated from a prompt shifts that balance: you ask in words for the view you need, the interface composes itself on the data you already have and updates the moment you change the question. It applies to anyone who makes decisions on financial, process or productivity data and today depends on someone else for every new view.
How it works
Describe in words the view you need
Write in natural language what you want to see, for instance margin per job over the last six months with overruns highlighted. You don't have to learn an interface and you don't have to hand the request to someone else.
The system queries the data you already have
The AI translates the request into queries against your company sources, within boundaries set upstream. It has no open access to the database: it goes through agreed views and data contracts and respects the permissions of your profile.
The view composes itself and updates
Tables, charts and thresholds appear on your real data. When you change the question the dashboard regenerates without a new round trip through the data team, and every view stays savable and shareable.
Why it stays under control
- Every view is traceable: what question produced what query, against which sources and when, stays on record, and the same view can be reproduced identically
- The AI works within views and data contracts agreed with your team, not on the open database. A badly worded question can't read what it shouldn't
- Permissions stay the ones from your own systems: governance doesn't change just because the request arrives as a prompt instead of a menu click
What changes
- For most recurring questions, decision-makers get to the number when they need it, without joining a queue
- The data team sheds the repeat requests and goes back to the analysis that is worth their time
- Comparing data gets faster: you change the question and see the effect straight away, without opening a new round of requests
What we don't promise
- It doesn't replace the data team or the BI you already have: it makes access to recurring questions conversational, complex analysis stays human work
- If the underlying data is messy or inconsistent the dashboard shows those problems, it doesn't fix them: that has to be tackled at the source
- We don't put it into production without boundaries, permissions and traceability defined upstream: we don't open the database to anyone who can type a prompt
Frequently asked questions
Does this mean giving an AI open access to our database
No. The AI works within views and data contracts defined with your team and respects the permissions you already have. It doesn't query the database freely, and a badly worded question can't read what the person asking has no right to.
How do I trust the number it shows me
Every view is traceable: you can see what question produced what query, against which sources and when, and the same view reproduces identically. The number isn't a black box, you can check it and redo it.
Does it replace our data team or BI tools
No. It takes the recurring requests off their queue, so they can focus on more complex analysis. Conversational BI sits alongside what you already have, it doesn't replace it.
Does it work if our data is scattered and not perfectly clean
You start from where you are. If the data is inconsistent the dashboard makes that visible instead of hiding it, and in the Inception package we first work out which sources and which questions hold up, so you start from solid ground.
At a glance
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Does this scenario sound familiar?
We start with an Inception package to see, on your actual case, whether and where it makes sense.
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