Data · ETL

AI-powered ETL and data integration

We unify your company’s data —ERP, sales, operations, e-commerce, spreadsheets— into a reliable model that updates itself. It is the foundation for reports everyone trusts and for AI agents that actually work.

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Without organised data, no AI works

Most companies have their data spread across systems that do not talk to each other. Every month someone exports, copies and cross-references files by hand to build a report, and the sales, finance and operations numbers still do not match.

An ETL (extract, transform, load) process automates that work: it takes data from every source, cleans it and integrates it in one place, with clear rules and traceability. What used to take days is ready every morning.

An AI agent is only as good as the data it can access. That is why, in many projects, organising the data is the first step before automating with agents.

What is included

Connection to your sources

ERP, CRM, point of sale, e-commerce, databases, APIs, Excel and CSV files.

Automated pipelines

Scheduled or near-real-time loads, with monitoring and alerts when a source fails.

Data quality

Rules for duplicates, out-of-range values, dates, currencies and codes, with quality reports.

AI to clean and match

Language models that unify catalogues, classify records and fix inconsistent descriptions, with human validation.

Unified data model

A cloud data warehouse or lakehouse with a documented model designed for your business.

Reports and a base for agents

Power BI dashboards and data ready for advanced analytics and AI agents that answer business questions.

How we work

01

Data map

We identify sources, owners, current quality and the reports or decisions that depend on that data.

02

Model design

We define the target data model, transformation and quality rules, and the refresh frequency.

03

Build in stages

We build the pipelines starting with the highest-impact sources, with visible results from the first weeks.

04

Operation and growth

We monitor the loads, document the process and train your team to maintain and extend it.

Technology

We work mainly on Microsoft Azure and adapt to the infrastructure you already have.

  • Microsoft Azure (orchestration, storage and databases)
  • Python and SQL for transformations
  • Large language models (LLMs) for cleaning and classification
  • Power BI for reporting
  • Connectors to ERP, CRM, e-commerce and APIs

Common use cases

  • Sales and inventoryConsolidate stores, channels and warehouses into one daily report.
  • FinanceReconciliations, closings and management reports without manual cross-checks.
  • OperationsProduction, logistics and service KPIs in a single dashboard.
  • AI readinessClean, governed data for agents and predictive models.

Timeline and engagement

The first integrated sources are usually in production within a few weeks. The rest are added in stages, prioritising what has the most impact on your operation.

We can build and run the solution for you or hand it over to your team with documentation and training.

Case studies

Related case study

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Mexican retail company · Omnichannel retail

Sales and inventory ETL for a Mexican retail company

Sales and inventory from ERP, stores, e-commerce and Excel consolidated daily into a single data model, with AI to standardise the product catalogue.

Frequently asked questions

What is an ETL process?

ETL stands for extract, transform and load: taking data from different systems, cleaning and unifying it, and loading it into a central repository for reporting and analysis.

Where does AI fit into ETL?

In the tasks that used to need human judgement: matching products or customers written in different ways, classifying records, filling missing fields and detecting anomalies.

Do I have to change my current systems?

No. The ETL connects to what you already use and does not replace your ERP or tools.

Is my data secure?

We work on the Microsoft Azure cloud with access control, encryption and separate environments, and we follow your company’s security policies.

Blog

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Is your data spread across many systems?

Tell us which sources you have and which reports you need. We will present a similar case and a proposal in a video call.

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