Data Engineering
Pipelines, integrations and well-organized data foundations, so reports, decisions and AI projects start from numbers you can trust, not from diverging spreadsheets.
Before the dashboard, the data has to be true
The scene repeats itself: every department with its own spreadsheet, three reports giving three different numbers for the same question, and the meeting that was supposed to decide something turns into a debate about which number counts. The obvious fix, buying a BI tool, does not solve it: a dashboard on top of messy data just makes the mess prettier and more convincing.
The foundational work is connecting the sources: sales system, ERP, spreadsheets, third-party APIs. We build pipelines that collect, clean and join that data in a central repository, with business rules applied once and holding for everyone. When someone asks for the month's revenue, there is one answer, traceable all the way back to its source.
We size the architecture to your real volume, not to the fashion catalog. Most companies do not need big data: they need a well-modeled database, monitored pipelines and transformations versioned as code, something one person can operate. Heavy tooling comes in when volume justifies it, and we do that math together with you, in writing.
A pipeline without monitoring breaks in silence, and wrong data in circulation is worse than no data. We instrument validations that check the data on every load and alert when something drifts from the pattern, before a wrong number reaches a decision. That same foundation feeds AI projects: a RAG assistant only answers well if the data behind it deserves trust.
- Data ingestion and transformation pipelines
- Central data repository modeled for analysis
- Source integration: systems, APIs and spreadsheets
- Dashboards and reports with trustworthy indicators
- Data quality validations and alerts
- Catalog and documentation of the data foundations
- Data preparation for AI and RAG projects
- Access governance and privacy controls
Discovery
We understand the problem, the context and the constraints before writing the first line.
Build
We start from the most valuable business question and build its complete path, from source to trustworthy indicator, validating each step with the people who use the number daily.
Evolution
With the foundation live, we monitor the pipelines, expand to new sources and questions, and keep documentation current, so the data stays reliable as the operation grows.
My reports show different numbers for the same thing. Can you fix that?
It is the classic problem, and the cause is almost never bad arithmetic: it is business rules applied differently in each spreadsheet. The fix is centralizing the definition, such as what counts as a sale and from which moment, applying it once in the pipeline and having every report drink from the same source.
Do I need big data or something like it?
Probably not, and the honest answer saves a lot of money. Most companies solve everything with a well-modeled relational database and simple, monitored pipelines. Distributed processing tools come in when real volume justifies the complexity of operating them. During discovery we measure your volume and show you the math.
Which data sources can you integrate?
Practically any source that exposes data in some way: databases, APIs, exported files, spreadsheets and legacy systems. When a source has no ready-made integration, we build the connector. What varies between them is the effort involved, which is exactly why discovery maps the sources before any estimate is made.
How do you handle privacy regulations like LGPD?
Privacy enters at the design stage, not as a final adjustment. We classify personal data while mapping the sources, apply anonymization or pseudonymization where the analysis does not require identified data and restrict access by role. Disposal is designed too. We build the engineering that supports compliance; the policy itself you define with your legal counsel.
I want to use AI in my company. Is this work related?
Completely: it is the prerequisite that is usually missing. Language models answering about your business, through RAG or automated analysis, depend on data that is organized, clean and traceable to its origin. Building that foundation first is what separates an AI pilot that convinces from one that hallucinates.
My team lives in Excel. Do they have to give it up?
No, and they should not: spreadsheets are a great tool for ad hoc analysis. What changes is their role. Instead of being the source of truth, fed by typing and prone to outdated copies, they start consuming data from the central repository, updated and consistent. Your team stays in the environment it masters, with better numbers.
Let's get your idea off the ground
Investment is handled later, in the proposal, after the discovery call.