
- Category
- Search and structured data
- Model
- open, installed on your premises
- Data
- never leaves
- Subscription
- €0 per month
Modern company data is scattered across dozens of sources: CRM, e-commerce, ads, ERP, SaaS. Without a robust pipeline, this data lives in silos, arrives late and is unusable for decision-making. The Intelligent Data Pipeline orchestrates extraction, transformation and loading of all your sources into a unified data warehouse, queryable in real time.
Siloed data prevents a unified business view. Manual exports lag 24–48 hours. Ad hoc transformations introduce errors. Without CDC (Change Data Capture), missed updates create inconsistencies between systems. And without observability, pipeline failures stay invisible.
An automated pipeline ingests your data via native connectors (Fivetran, Airbyte, CDC) and loads it into BigQuery or Snowflake after transformation. Apache Airflow orchestrates dependencies and retries on error. An LLM agent (via MCP) enables natural language data warehouse queries. An observability dashboard monitors data freshness and quality.
Live in four steps.
- 01Source mapping
Audit of all your data sources: APIs, databases, files, SaaS. Prioritization by business value.
- 02Connector construction
Development or configuration of ETL/ELT connectors, CDC for critical sources (MySQL, PostgreSQL, Salesforce, Shopify).
- 03Data warehouse modeling
Star schema or Medallion (Bronze/Silver/Gold) design in BigQuery or Snowflake. dbt layer for transformations.
- 04Orchestration & observability
Airflow or Prefect deployment for orchestration, alerts on delay or error, data quality dashboard.
Results you can measure.
The CDC pipeline captures every change in your sources and propagates updates in near real time to your warehouse.
All your sources in one warehouse. CRM, sales, ads, product, everything is consistent and comparable.
The monitoring system detects and alerts on every pipeline anomaly (delay, error, missing data) before business impact.
What we get asked before signing.
Which data sources can you connect?
What is the minimum refresh frequency?
Do we need in-house data engineering expertise?
Are dbt transformations maintainable by our teams?
Let's talk about your case for thirty minutes.
A conversation to understand your context, volumes and tools. You leave with a scope and an order of magnitude, no commitment.