Dashboards that answer questions instead of showing charts.

We build the analytics layer over the data you already hold, on Claude, Google Gemini or OCI — from ERP to dashboard, with the query behind every number in plain view.

Most companies already hold the data they need to decide. What is missing is the layer that joins it, explains it and answers a question in seconds instead of handing it to an analyst whose calendar opens up next week.

Why nobody opens the dashboards you already have

A traditional dashboard answers the questions someone anticipated when they built it. The question that matters today — why did margin fall on this account this quarter — was not anticipated, and answering it means opening Excel again.

That is why most dashboards are opened in week one and abandoned by week three. It is not a shortage of data. It is that the distance between the question and the answer is still a person.

What we build

• The collection layer. Connections to the ERP, the CRM, the databases and the spreadsheets that still run the process. Oracle, SQL Server, PostgreSQL, internal APIs — and files, when files are what exists.
• The data model. The layer where a sale, an active customer or a margin is defined — once, in writing, the same for everyone. This is where the meetings die in which two departments bring different numbers.
• The dashboards. Indicators, time series and comparisons, designed around the decision they have to support rather than around filling the screen.
• The AI layer. Ask in plain language, get the number and the query that produced it. Whoever reads it can verify rather than believe.

What AI adds to a dashboard

• Asking in writing. “Which five accounts dropped most against last quarter?” returns the table and the SQL behind it — without going through anyone.
• Explaining the movement. Not just that the indicator fell, but which dimensions contributed most to the fall.
• Spotting what breaks pattern. Alerts on deviations nobody was watching, because nobody knew a pattern was there.
• Summarising for whoever decides. The monthly report written from the real numbers, exceptions flagged — reviewed by a person before it goes out.
• Projecting. Demand, cash or load forecasts, with the confidence interval in plain view instead of a lone number.

Built on platforms you already know

We develop on Claude, Google Gemini and OCI Generative AI, with the data layer on Oracle, PostgreSQL or whatever warehouse you already run. The model sits behind a layer of ours: switching provider is configuration, not a rewrite.

Where Power BI, Looker or Qlik is already in the building, we build on top rather than replace — the natural-language layer and the alerts arrive without forcing anyone to change tools.

What you get

• The dashboards in production, wired to real data and refreshing on their own.
• The data model documented, every metric defined in writing and signed off by the people who use it.
• The question assistant embedded where the team already works — intranet, Teams or the application itself.
• A log of the questions asked and the cost per period, so you know what is being used and what it costs.
• Training for the people who will use it and documentation for the people who will maintain it.

Frequently asked questions

We already have Power BI. Does this replace it?

It does not replace it, it adds to it. We keep the dashboards that work and build the missing layer on top: asking in plain language, explaining movements, alerting on deviations. When the real problem is the data model underneath, we say so — and that is usually where it is.

Does our data leave our infrastructure?

We settle that up front. The model receives the table structure and the question, not the whole dataset — and where the data category demands it, the alternative is a self-hosted model. It is your decision, informed.

How long until the first useful dashboard?

Four to eight weeks for the first set of indicators in production, depending on the state of the data and how many systems have to be connected. The natural-language layer follows, on top of a model that is already validated.

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