Data Infrastructure That Makes AIand Decisions Possible.
AI systems are only as reliable as the data behind them. We build the infrastructure layer that most companies skip — ETL pipelines that handle dirty data, vector databases that make enterprise knowledge searchable, and RAG systems that ground AI outputs in your real information.
ETL Pipeline for Fragmented ERP Data
A multi-site industrial group runs three different ERPs. We build a unified ETL pipeline that extracts, normalizes, deduplicates, and loads data into a central analytical database — giving leadership a single version of truth.
RAG System for Technical Documentation
An engineering company maintains 40,000 pages of technical manuals. We build a Retrieval-Augmented Generation system with vector indexing and AI-powered search where field engineers get source-cited answers in seconds.
Real-Time Data Platform for E-Commerce
A D2C brand sells across its own shop, Amazon, and wholesale partners. We build a data platform with real-time ingestion from all channels and automated reporting that reconciles revenue and inventory daily.
Knowledge Base Infrastructure for AI Agents
A professional services firm has knowledge trapped in 15 years of reports and emails. We build ingestion pipelines, embedding models, and vector storage that turns institutional memory into a queryable knowledge base.
Data Audit
Map your data landscape, sources, and quality gaps
Pipeline Architecture
Design ETL flows, schemas, and transformation logic
Production
Build and deploy pipelines with automated testing
Monitoring
Data quality alerts, pipeline health, and continuous optimization
Data Audit
Map your data landscape, sources, and quality gaps
Pipeline Architecture
Design ETL flows, schemas, and transformation logic
Production
Build and deploy pipelines with automated testing
Monitoring
Data quality alerts, pipeline health, and continuous optimization
Automated data management
Pages indexable
To working prototype
Why Most AI Projects Fail Because of Data
The most powerful AI model delivers poor results when the underlying data is unstructured, outdated, or scattered across dozens of systems. Studies show that roughly 80% of effort in a typical AI project goes into data preparation — not model training. Without clean, structured, and accessible data, every AI initiative remains an expensive experiment.
That's why every successful AI project starts with the right data infrastructure. In our AI consulting engagements, we define the strategic direction together — here, we build the technical foundation everything else depends on.
Our Approach: From Data Audit to Production-Ready Pipeline
We start with a comprehensive data audit: Where does your data live? What quality is it in? What formats and systems are involved? Based on this analysis, we design an architecture that consolidates fragmented sources into a unified pipeline — your single source of truth.
For AI-powered use cases, we extend the pipeline with vector databases and RAG architectures, making your internal documents and knowledge bases accessible to large language models. Our guide on data infrastructure for AI covers the technical foundations in detail.
On top of the finished data infrastructure, we layer AI automation — agents, workflows, and integrations that measurably accelerate your business processes.
Data Infrastructure From Hamburg — GDPR-Compliant and Scalable
Our team is based in Hamburg and understands the data sovereignty requirements of the German market. We rely on EU-compliant hosting providers, encrypted data transmission, and granular access controls. For particularly sensitive data, on-premise options are available — learn more in our article on GDPR-compliant on-premise LLMs.
Whether Hamburg, Munich, or Berlin: projects start with an on-site data audit and continue through close remote collaboration. This way, we combine personal exchange with efficient, location-independent delivery.
ETL, RAG, Vector Databases: What Does Your Company Need?
ETL pipelines consolidate data from ERP systems, CRM databases, Excel files, and APIs into a central data warehouse. They create the foundation for reliable analytics and reporting.
RAG systems connect your internal knowledge — manuals, contracts, knowledge bases — with large language models. Your employees receive precise, context-aware answers instead of generic AI output. Learn more in our article on RAG systems for enterprises.
Vector databases enable semantic search across large document collections. Instead of exact keyword matching, they understand meaning and context — ideal for support portals, internal knowledge systems, and product catalogs.
Which combination makes sense for your company depends on your data sources, use cases, and scaling goals. On top of the finished infrastructure, we develop custom software solutions tailored precisely to your processes.
Frequently Asked Questions About Data Infrastructure
What does building a data infrastructure cost?+
Costs vary depending on data volume and complexity. A first ETL pipeline prototype is often feasible as a fixed-price project. We start with a free data audit and provide a transparent quote.
How long until the infrastructure is ready?+
A first working prototype — such as an ETL pipeline or RAG system — typically stands after three weeks. Full production deployment including monitoring takes six to twelve weeks.
What is a RAG system and when do I need one?+
RAG (Retrieval-Augmented Generation) connects your internal data with AI models. Instead of relying on general knowledge, the AI accesses your documents, manuals, and databases — for precise, context-aware answers.
Is our data secure with you?+
Yes. We use EU-compliant hosting providers, encrypted transmission, and access controls. On-premise options are also available. Data protection and GDPR compliance are integral to every architecture.
Can existing data sources be integrated?+
Yes. We integrate ERP systems, CRM databases, Excel files, APIs, and cloud storage into a unified pipeline. The goal is a single source of truth for your company.
Do you only offer data infrastructure in Hamburg?+
Our office is in Hamburg, but we build data infrastructures for companies across Germany. Projects start with an on-site data audit and continue remotely.
Bereit für Ihr nächstes Projekt?
Lassen Sie uns gemeinsam Ihre Vision verwirklichen. Kontaktieren Sie uns für eine unverbindliche Beratung.