Data engineering
Pipelines that integrate and order your sources
Data engineering: pipelines that stay up
Data rarely arrives clean or from a single place. We build the pipelines that integrate your sources, normalize the formats and leave the information ready to use, with no manual loading in between.
Source integration
We connect ERP, CRM, online store and spreadsheets into one flow, with loading automated and traceable.
Normalization and quality
We unify formats, deduplicate records and validate every field, so nobody argues about whether the data is right.
Orchestrated pipelines
Scheduled processes that run on their own, alert when something fails and can be reprocessed without breaking anything.
Data consulting: what you have and what you lack
Before building you need to know what you are working with. We map the sources, the state of the information and the processes that generate it, and tell you what works today, what needs fixing and what is still not being captured.
Source diagnosis
We map where each piece of data comes from, how often it updates and who uses it to decide.
The real state of your data
We measure completeness, duplicates and consistency. The report says how far you are from trusting your data.
Prioritized roadmap
A staged plan, with what returns first and what can wait. No two-year projects.
Data analytics: from spreadsheet to decision
A dashboard full of charts is useless if nobody changes a decision after seeing it. We define the metrics that matter, keep them visible for the team and use AI where it adds more than a simple query.
Dashboards with your metrics
We define your business indicators and keep them current, with no exporting spreadsheets by hand.
Predictive models
Demand forecasting, stockouts or customer churn, trained on your own history.
AI on your own data
Plain-language queries and automatic summaries, grounded in your information and not in generic sources.
Cloud data: infrastructure that scales with you
Data grows and so does the cloud bill. We design storage and processing to hold the volume that is coming, measuring the cost before it turns into a surprise.
Storage in order
Data warehouse or data lake depending on the case, with separate layers for raw, processed and consumption.
Scaling on demand
The infrastructure grows at peaks and scales back when it is not needed, with no manual intervention.
Cost under control
We monitor spend per process and tune what consumes too much. A cheap cloud is designed, not discovered.