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Data engineering for AI projects

No AI model is better than the data that fuels it. We build modern data platforms — data lakes, warehouses, streaming pipelines — with governance, quality, and lineage, so that every AI decision rests on solid and traceable ground.

Optimized data pipelines to fuel your AI models with quality, speed, and governance.

Use cases

  • Multi-source data platforms for corporate groups
  • Custom Customer Data Platforms (CDP)
  • Real-time analytics for e-commerce
  • Feature stores for data science teams
  • Reverse ETL to CRM and marketing tools

Measurable benefits

  • Reliable and timely data
  • Reduced cloud costs with optimized architectures
  • Self-service analytics for business users
  • GDPR compliance and governance by-design

Technical details

Storage

  • Snowflake, BigQuery, Databricks
  • Data lake on S3/GCS with Iceberg/Delta
  • PostgreSQL, ClickHouse for analytics
  • Lakehouse architecture

Ingestion & transformation

  • Airbyte, Fivetran for SaaS connectors
  • dbt for versioned SQL transformations
  • Apache Spark for batch
  • Kafka + Flink for streaming

Quality & governance

  • Great Expectations for data quality
  • dbt tests + alerting
  • Catalog: DataHub, Atlan, OpenMetadata
  • Automatic end-to-end lineage

Orchestration

  • Apache Airflow, Prefect, Dagster
  • Schedule + event-driven triggers
  • Retry, backfill, SLA monitoring
  • Full observability

How we make your data usable

  1. Source inventory — We list where data lives today: ERP, CRM, spreadsheets, shared mailboxes, cloud archives, each with an owner and refresh rate.
  2. Data quality — We measure missing fields, duplicates, inconsistent formats and misaligned keys — this determines whether anything else is feasible.
  3. Pipelines — We build repeatable, idempotent extraction and transformation jobs, scheduled and with automatic checks on rejects.
  4. Governance — We define who accesses what, how new access is requested and how personal data is handled.
  5. Lineage — Every derived table declares its origin: without traceability nobody trusts a number that moves.
  6. Storage — Persistence is chosen on volume, query frequency and retention requirements.
  7. Serving — Data is exposed where it is actually needed: dashboards, internal APIs, retrieval indexes, forecasting models.

We deliver the data schema, pipeline code, lineage documentation and automated quality checks.

Working with us

The team that analyses the process is the team that builds and maintains it: product, engineering, integration with your existing systems, governance of automated decisions and post-release support.

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FAQ

Can I start without a data warehouse?

Yes, but it is the first step we recommend. We build scalable data foundations from scratch with Snowflake/BigQuery/Databricks.

What does data lineage mean?

It is the map that tracks every piece of data from the source to the final report. Critical for audits, debugging, and compliance.

How much does a data platform cost?

It range from basic setups (~15k€) to enterprise platforms with hundreds of pipelines. Cloud costs are separate and managed based on consumption.