We connect the sources, structure the flows and deliver analytics that leaders actually use, not just what the data team produces.
Book a diagnostic →Food manufacturing
7,680h/year
of manual work removed
Fashion retail
100%
of franchises with real-time data
Food retail
10×
faster decision making
Grupo Orangefox
150+
active clients
Most companies have data. Few have decisions built on it.
The problem is not a missing tool. It is data with no governance, reports with no owner, and insight that arrives late or never arrives at all. The result is decisions made on instinct where they should be made on evidence.
We fix that. With a method.
We find where the data sits, how it flows and where the chain breaks. No guesswork, a real map.
We build the data environment with the right tools for the size and the sector. Data with an owner, a process and measured quality.
Dashboards, automated reports and predictive models tied to the decisions the business actually makes, not to vanity metrics.
Your team learns to run it and does not depend on us to reach its own data. Independence is part of the delivery.
The director asks for yesterday figure and gets a spreadsheet from three weeks ago. Sales and finance spend two days arguing which revenue number is right, because each area has its own. The report that decides the month depends on one specific person being available, and when they go on holiday the decision waits.
None of these is solved by buying another tool. They are problems of foundation: data that enters wrong at the source, business rules that live in someone head, metrics with no owner, and no layer between the transactional system and whoever decides.
That is what we attack first. We do not start with the pretty dashboard, we start by finding where the chain breaks, because a dashboard built on bad data only speeds up the wrong decision.
A good share of data projects fail by inverting that order. They start with the tool, discover the data will not hold it up, and by then it is late: the budget is gone and so is the team trust.
In the analytics layer we follow our own model, in three steps. The dashboard answers what is happening and is where the board looks every day. The analysis layer lets a person go down into the detail and understand why it happened. And self-service is when the team builds its own view, with no ticket and no dependence on us.
That last step is the one most projects never reach, and it is exactly what separates a BI vendor from an operation that walks on its own. Independence is part of the delivery, not an extra.
Four lines of work, which can come together or separately, depending on where your operation stands.
Executive and operational dashboards, automated reports and metrics with a written formula and a named owner. In Qlik Sense, Qlik Cloud and Power BI.
Ingestion and transformation pipelines, integration across ERP, CRM and legacy systems, and orchestration that runs on its own, with an alert when it fails rather than silence.
Catalogue, lineage, monitored quality, access policy and the agreement on who answers for each number. It is what stops data from being argued about in meetings.
Architecture for volume and variety, on Databricks, Microsoft Fabric, Snowflake or Delta Lake, sized for the real operation rather than for the slide.
In three formats, depending on the need: assessment, when the first step is understanding maturity and drawing the path; fixed-scope project, when the destination is already clear; and allocated squad, when demand is continuous and the team needs senior capacity in house.
In all three, the first delivery in production usually lands in weeks rather than quarters. We work with early wins that pay for part of the path before the project ends.
Food
Food manufacturing
7,680h/year
Hours of manual work removed by automating analytics reporting. The team moved on to analysis instead of spreadsheets.
Retail / Franchises
Fashion retail
100%
Of the franchise network reporting in real time, with no manual step. Full visibility of the network, from anywhere.
Almost never. In most cases the tool is not the problem, what is missing is the data layer underneath it. When a switch does make sense we say why, with the cost and the gain on the table; when it does not, we work with what you already have.
Yes, and that is how nearly every project starts. Bad data is not a blocker, it is the starting point. What does not work is building metrics on top of it without treating the source first, because then the number comes out pretty and wrong.
The first delivery in production usually ships in weeks. The whole project depends on the size of the operation, but we work in partial deliveries precisely so you do not wait until the end to see value.
The opposite is the goal. Training and independence are the fourth stage of the method, and the measure of success is your team building its own analysis without calling us.
A 30-minute conversation with our team is usually enough to show where the biggest bottlenecks are.