Data that gets used, models that operate
Most data initiatives die in the pilot. Usually not because of the model, but because nobody settled where the data comes from or who maintains the result.
We build the whole pipeline: ingestion, model, quality, serving and consumption. A pretty dashboard over inconsistent data is worse than no dashboard, because it produces decisions with false confidence.
In artificial intelligence we solve concrete, measurable tasks rather than general transformations with no date. An agent that classifies tickets with verifiable accuracy is worth more than an AI strategy on a slide.
What it includes
- Ingestion from transactional systems, files and external APIs
- Analytical data model with documented business rules
- Quality tests: freshness, uniqueness, integrity and your own rules
- Dashboards by role, at the level of detail each profile needs
- Prediction and classification models put into production, not into a notebook
- Agents that execute tasks on your systems, with every action traceable
- Drift and model performance monitoring over time
How we do it
One question at a time
We start from the business decision that has to be made and walk backwards to the source data. It is slower at first and far faster afterwards than building a data lake and waiting.
Quality before volume
Every table we publish carries its tests. If a source stops complying, the dashboard says so instead of showing a wrong figure with total confidence.
AI, with evaluation
Every model or agent ships with its evaluation set and its metric. Without a way to measure whether it gets things right, there is no way to know whether the next version is better or worse.
When it makes sense
- Each area reports different figures for the same thing
- The team spends days consolidating a monthly report by hand
- There is an AI pilot that has not reached production in months
- You want to automate a repetitive task that needs judgement, not fixed rules
- There is enough data but nobody trusts it
Other practices
Consulting and transformation
Most projects that fail do not fail because of the code: they fail because nobody agreed which problem was being solved.
View →Engineering and custom development
We build systems that other people will maintain. That changes how they get written.
View →Product design and experience
An interface is judged by a tired user, in a hurry, with the sun in their eyes. Not on a presentation screen.
View →Tell us what you need to build
Initial assessment at no cost. We answer with a concrete scope, with cost and date.
