Data & AI
Pipelines, data warehouses and dashboards that bring scattered company data into one reliable place.
Most companies already have the data they need. The problem is that it lives in five systems and two spreadsheets, and every team has a different number for the same question. We build the layer that integrates those sources, organizes history and delivers metrics the team can actually decide with.
Benefits
- One source for the metrics leadership tracks
- Pipelines that refresh on their own, no spreadsheet exports
- Dashboards with your operation's KPIs, not a template's
- Governance: who can see what, and where each number came from
- AWS or Google Cloud infrastructure sized for real volume
- A foundation ready for machine learning models later on
Process
Architecture & integration
Connecting sources, APIs and legacy systems into secure pipelines.
Modeling & storage
An organized data warehouse or lake with defined access rules.
Analysis & visualization
Dashboards with KPIs defined together with the people who'll use them.
Pillars
Data engineering
Ingesting and cleaning data from many sources, in batch or real time.
Dashboards
Panels built from the questions the team asks every week.
Data warehouse
Storage that grows with volume and keeps history organized.
Data visualization
Charts that answer one question at a time, without decoration.
Frequently asked questions
What does Cortex IQ build in a data project?
- We build the layer that integrates ERP, CRM and spreadsheet data into pipelines, organizes history in a data warehouse or data lake and delivers dashboards with the KPIs your operation uses. The infrastructure runs on AWS or Google Cloud, sized for real volume, and is ready for machine learning models later on.
How do you make dashboard numbers match across departments?
- Numbers match when there is one source for the metrics and governance rules that define who can see what and where each number came from. Pipelines refresh on their own, with no spreadsheet exports, and KPIs are defined together with the people who will use them, not copied from a template.
What are the stages of a data and AI project?
- There are three stages: architecture and integration, connecting sources, APIs and legacy systems into secure pipelines; modeling and storage, with a data warehouse or lake and defined access rules; and analysis and visualization, with dashboards defined together with their users. Each stage is checked with your team before the next.
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- Data lake, data warehouse or lakehouse: how to decide in 2026With open table formats like Apache Iceberg, lake and warehouse are no longer an either-or choice. The decision now comes down to which answers the business needs every week, who will query the data, and how you'd rather pay for it.
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Industries already using this
Why Cortex IQ
2009
the founder's first IT project
10
published cases, with the client's name
4
partner clouds: AWS, Google Cloud, Microsoft and Oracle
3
working languages: Portuguese, English and Spanish
Partner clouds
