Data + AI: transforming data into business actionable

Before we measure, we understand. We dive into the business model, operational flows and strategic objectives to design a tailored data roadmap. We identify what information is critical, what gaps exist today and how to capitalize on the data you’re already generating to drive growth.

Ungoverned data is a risk. We implement classification, access and quality policies that ensure data integrity and confidentiality. We design permission structures, data lineage and validation processes that ensure regulatory compliance (GDPR, CCPA, local regulations) without compromising the analytical operation.

We design and implement ETL/ELT pipelines that extract, cleanse, transform and consolidate data from all your sources: ecommerce platforms, ERP, CRM, marketing tools, payment systems and offline data. The result is a reliable, up-to-date and analytics-ready data layer.

We design the right storage architecture for your scale and needs: Data Warehouses in the cloud (BigQuery, Snowflake, Redshift), Data Lakes for unstructured data, or hybrid architectures. We automate ingest flows so that data is available when the business needs it.

Most companies make decisions on dirty data. We audit your current measurement ecosystem, detect discrepancies, double counting and attribution errors, and rebuild the implementation on solid foundations: GA4, Server-Side Tracking, CAPI and structured Data Layer.

We transform data into visual narratives that teams actually use. We develop executive dashboards, operational reports and interactive exploration tools adapted to each user profile: management, operations, marketing and finance. With KPI tracking, alerts and context for decision making.

When descriptive analysis is not enough, we implement customized predictive models: demand forecasting, customer segmentation, propensity scoring, anomaly detection and churn models. Each model is designed based on your actual business data and integrated into existing operational flows.

Beyond the last click. We implement multi-touch attribution models and Marketing Mix Modeling (MMM) to understand the real contribution of each channel, campaign and touchpoint to revenue. We integrate advertising, organic traffic, CRM and offline sales data to build a unified view of marketing ROI and optimize budget allocation with analytical support.

A data ecosystem is not a one-time project: it evolves with the business. We provide ongoing support to adapt the architecture to new sources, update models with recent data, ensure the health of pipelines and train teams in the use of tools and advanced analytics.

What is the difference between a dashboard and a real BI system?

A dashboard is a static or semi-static visualization of predefined metrics. A BI system is a complete architecture: integrated data sources, a storage and transformation layer that ensures data consistency, documented business semantics (what exactly "conversion" or "net revenue" means in your company), and exploration tools that allow you to answer unanticipated questions without relying on someone to build a new report. The practical difference: with a dashboard, the business sees what someone decided to show; with BI, the business can explore.

When does it make sense to implement Machine Learning models?

When the descriptive (what happened) and diagnostic (why it happened) analysis are already well covered and the business needs to anticipate: predict what will happen, which customer will cancel, which product will be out of stock, or which segment is more likely to convert. Models add more value when there is a sufficient volume of historical data (usually 12 months or more), when there is a clear business decision that the model can improve, and when there is the operational capability to act on the predictions. It makes no sense to implement ML on poor quality data or without a concrete use case.

Do we need to involve our IT or development team?

For implementations that require changes to the Data Layer or integrations with internal systems, collaboration is necessary. But we act as a technical bridge: we deliver detailed documentation, implementation specifications and accurate test cases to minimize development hours and eliminate back-and-forth. In most projects, our team assumes full technical responsibility and coordinates directly with the client's IT team, without the business area having to mediate between parties.