We design, build, and operate data platforms that turn your raw data into your most valuable asset, powering real-time analytics, AI/ML, and business intelligence at scale.
68% of enterprise data goes unused. Most organizations lack the architecture, pipelines, and governance to extract value. We bridge that gap by designing data systems that scale with your business and fuel your competitive advantage.
68%
Enterprise data unused
80%
AI fails on bad data
5x
Faster data-driven decisions
Your Data Is Your Moat. But Only If You Can Use It.
Organizations are drowning in data but starving for insights. 97% invest in data initiatives, but only 24% consider themselves data-driven. The problem isn't volume. It's architecture, quality, and accessibility. We build the engineering foundation that turns raw data into competitive advantage.
Annual cost of poor data quality per organization
Gartner
AI projects that fail due to data quality issues
Gartner
Enterprise data that goes unused
Forrester
Faster decisions for data-driven organizations
McKinsey
The Challenge
Why Your Data Isn't Working for You
What teams expect
Single source of truth on day 1
Clean data flowing seamlessly to every team
AI ready from the start
Clean data flowing seamlessly to every team
Decisions backed by data
Clean data flowing seamlessly to every team
What actually happens
Data Scattered Everywhere
Dozens of disconnected systems, no single source of truth. Teams spend more time finding and cleaning data than analyzing it.
AI Ambitions Without Foundation
You want AI-driven personalization and automation, but the data infrastructure to power it simply isn't there yet.
Decisions Made on Gut Feel
Every day without unified data means missed revenue opportunities, blind spots in customer understanding, and evidence-free decision making.
Data Engineering for the AI Era
Every AI initiative lives or dies on its data foundation. Here's how we build yours.
Powering AI Features with Real-Time Data
Build the streaming pipelines and feature stores that power customer-facing AI. Real-time recommendations, intelligent search, and predictive features all start with the right data architecture.
Key Stat
85% of enterprises adopting lakehouse architectures for AI/ML workloads
Databricks Survey
- Real-time feature stores for ML models
- Streaming data pipelines (Kafka, Flink)
- Event-driven architectures for AI inference
Building Your AI Training Ground
Fine-tuning LLMs, training custom models, and building RAG systems all require a purpose-built data platform. We design and implement the lakehouses, vector databases, and data pipelines that make your proprietary AI possible.
Key Stat
24% of organizations consider themselves truly data-driven
NewVantage
- Lakehouse architecture (Snowflake, Databricks, BigQuery)
- Vector database integration for RAG
- Data quality pipelines for ML training data
AI-Driven Data Operations
Apply AI to the data engineering process itself: automated data quality monitoring, intelligent schema evolution, anomaly detection in pipelines. DataOps meets AIOps.
Key Stat
70-80% of analyst time goes to data prep. AI-assisted engineering cuts this dramatically
Industry Standard
- Automated data quality monitoring with ML
- Intelligent ETL optimization
- AI-powered data cataloging & discovery
The Approach
Data Infrastructure That Drives Business Value
A hands-on approach that connects data engineering directly to business outcomes - from architecture through activation.
Data Platform Architecture
Modern data infrastructure designed for reliability, scale, and AI-readiness.
- ✓Data platform architecture & design
- ✓Data warehouse & lakehouse implementation
- ✓Real-time streaming & event-driven architectures
- ✓Data governance & quality frameworks
Key Insight
85% of enterprises adopting lakehouses for AI/ML workloads
Pipeline Engineering
Robust data pipelines that deliver the right data at the right time.
- ✓ETL/ELT pipeline development & optimization
- ✓Customer data platform (CDP) integration
- ✓Business intelligence & analytics setup
- ✓Data foundation for AI/ML workloads
Key Insight
$12.9M annual cost of poor data quality per organization
Activation & Analytics
Connecting data infrastructure to business outcomes.
- ✓AI/ML feature store design
- ✓Legacy data system migration
- ✓Data team structure & operating model
- ✓Self-service analytics enablement
Key Insight
70-80% of analyst time spent on data preparation, not analysis
The Hidden Tax of Poor Data
Poor data quality costs more the longer it goes unaddressed
We build data quality into the foundation: validation at ingestion, schema enforcement at integration, and continuous monitoring throughout. Fixing data problems at the source is 10x cheaper than fixing them downstream.
The True Cost of Poor Data
$3.1 Trillion
Estimated annual cost of poor data in the US economy
IBM
Architecture
The Modern Data Architecture Blueprint
A layered platform that takes raw data from any source to actionable insight - with AI/ML readiness built in from the foundation.
Sources
Ingestion
Lake / Storage
Warehouse / Lakehouse
Serving
Hover any layer to explore details and technology options
Engagement Model
From Data Chaos to Data Products
A structured engagement that turns scattered data into unified, actionable business intelligence.
Data Landscape Discovery
Week 1-2
Map all data sources, pipelines, storage systems, and consumption patterns. Interview stakeholders to understand pain points and priorities.
- ✓Data landscape inventory
- ✓Quality assessment
- ✓Opportunity matrix
Architecture & Use Case Design
Week 3-5
Design the target data architecture anchored to highest-priority business use cases.
- ✓Data architecture blueprint
- ✓Technology selection rationale
- ✓Implementation plan
Foundation Build
Week 6-14
Implement core data infrastructure and deliver the first production data products.
- ✓Production data pipelines
- ✓Initial data products
- ✓Quality baselines
Scale & Govern
Week 14-20
Expand data products, implement governance, and build internal team capability.
- ✓Governance framework
- ✓Team training program
- ✓Evolution roadmap
Phase 01
Data Landscape Discovery
Map all data sources, pipelines, storage systems, and consumption patterns. Interview stakeholders to understand pain points and priorities.
- ✓Data landscape inventory
- ✓Quality assessment
- ✓Opportunity matrix
Phase 02
Architecture & Use Case Design
Design the target data architecture anchored to highest-priority business use cases.
- ✓Data architecture blueprint
- ✓Technology selection rationale
- ✓Implementation plan
Phase 03
Foundation Build
Implement core data infrastructure and deliver the first production data products.
- ✓Production data pipelines
- ✓Initial data products
- ✓Quality baselines
Phase 04
Scale & Govern
Expand data products, implement governance, and build internal team capability.
- ✓Governance framework
- ✓Team training program
- ✓Evolution roadmap
Technologies we work with
Battle-tested tools across the modern cloud-native stack
Processing & Orchestration
Storage & Analytics
Visualization & Integration
FAQ
Explore More
Our other services
Technology capabilities that work together - pick what's relevant to your next move.
Let's Talk
Ready to Stop Wasting Data?
Let's audit your current data infrastructure and build the platform that powers your AI, analytics, and business intelligence initiatives.
Based in Düsseldorf, Germany — working with clients across Europe