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What we do

ENTERPRISE DATA QUALITY
for reliable AI

ENTERPRISE
DATA QUALITY
for reliable AI

Quality determines whether AI delivers value or fails in production. We evaluate data reliability across systems, establish scalable quality frameworks, and build foundations that support serious analytics and AI programs. You get trusted data.

ENTERPRISE DATA QUALITY
for reliable AI

POOR QUALITY DATA BLOCKS EVERYTHING

Siloed systems create conflicting data. Teams use inconsistent definitions. Metrics can't be trusted across departments. This is the enterprise data quality reality most organisations face. It blocks analytics, derails AI projects, and slows every decision that requires data.

The business impact is direct. Poor data quality makes AI outputs unreliable. Compliance becomes impossible when you can't trace lineage. Leaders hesitate on decisions because the numbers don't align. Data quality for AI determines whether models deliver value or fail in production. Fix the quality problem, and everything else becomes possible.

BUILD TRUSTED DATA FOUNDATIONS

CONSISTENT, TRUSTED DATA

Enterprise data quality across all business units. Break silos. Establish shared definitions. Build data foundations every team can trust and rely on.

CONSISTENT, TRUSTED DATA

Enterprise data quality across all business units. Break silos. Establish shared definitions. Build data foundations every team can trust and rely on.

CONSISTENT, TRUSTED DATA

Enterprise data quality across all business units. Break silos. Establish shared definitions. Build data foundations every team can trust and rely on.

REDUCE AI MODEL RISK

Lower AI and ML model failure rates with reliable training data. Quality controls catch issues early. Models perform consistently in production environments.

REDUCE AI MODEL RISK

Lower AI and ML model failure rates with reliable training data. Quality controls catch issues early. Models perform consistently in production environments.

REDUCE AI MODEL RISK

Lower AI and ML model failure rates with reliable training data. Quality controls catch issues early. Models perform consistently in production environments.

ACCELERATE DECISION-MAKING

Faster, more confident decisions backed by trusted data. Leaders act without hesitation. Teams align on numbers that match reality and drive business outcomes.

ACCELERATE DECISION-MAKING

Faster, more confident decisions backed by trusted data. Leaders act without hesitation. Teams align on numbers that match reality and drive business outcomes.

ACCELERATE DECISION-MAKING

Faster, more confident decisions backed by trusted data. Leaders act without hesitation. Teams align on numbers that match reality and drive business outcomes.

STRENGTHEN COMPLIANCE

Stronger compliance and governance alignment at enterprise scale. Data lineage tracking. Quality controls. Audit trails that satisfy regulators and reduce operational risk.

STRENGTHEN COMPLIANCE

Stronger compliance and governance alignment at enterprise scale. Data lineage tracking. Quality controls. Audit trails that satisfy regulators and reduce operational risk.

STRENGTHEN COMPLIANCE

Stronger compliance and governance alignment at enterprise scale. Data lineage tracking. Quality controls. Audit trails that satisfy regulators and reduce operational risk.

SCALE AI INITIATIVES

Scalable quality foundations for enterprise AI programs. Clean data supports GenAI and ML reliably. Infrastructure that moves pilots to production systematically.

SCALE AI INITIATIVES

Scalable quality foundations for enterprise AI programs. Clean data supports GenAI and ML reliably. Infrastructure that moves pilots to production systematically.

SCALE AI INITIATIVES

Scalable quality foundations for enterprise AI programs. Clean data supports GenAI and ML reliably. Infrastructure that moves pilots to production systematically.

MAINTAIN CONTROL

Build on open-source architecture you control. Quality frameworks that adapt to your business. No vendor lock-in. Foundations that grow with ambitions.

MAINTAIN CONTROL

Build on open-source architecture you control. Quality frameworks that adapt to your business. No vendor lock-in. Foundations that grow with ambitions.

MAINTAIN CONTROL

Build on open-source architecture you control. Quality frameworks that adapt to your business. No vendor lock-in. Foundations that grow with ambitions.

FOUR PILLARS OF DATA QUALITY

FOUR PILLARS OF
DATA QUALITY

FOUR PILLARS OF DATA QUALITY

1. GOVERNANCE & OWNERSHIP

We establish clear data ownership and governance structures across your enterprise. Define who owns which data, quality standards by domain, and accountability frameworks. You receive measurable governance maturity scoring that shows gaps and priorities for improvement.

1. GOVERNANCE & OWNERSHIP

2. DATA STANDARDS & LINEAGE

We implement quality standards, semantic consistency, and full lineage tracking. Enterprise data quality requires knowing where data originates, how it transforms, and who can trust it. Standards that scale from source systems to analytics and AI workloads.

2. DATA STANDARDS & LINEAGE

3. QUALITY MONITORING & SCORING

We build continuous quality monitoring with automated scoring across dimensions: completeness, accuracy, consistency, timeliness, validity. Real-time alerts when quality degrades. Dashboards that show quality trends and drive actionable maturity improvements across teams.

3. QUALITY MONITORING & SCORING

4. AI READINESS & MODEL RELIABILITY

We ensure data quality for AI through training data validation and model input reliability checks. Quality controls prevent poor data from reaching models. Production-ready data that supports GenAI and ML at enterprise scale consistently.

4. AI READINESS & MODEL RELIABILITY

PROVEN SUCCESS

Healthcare

Quality teams reclaim days each month with 100% regulatory compliance

Automated care plan standardisation and validation

Secure on-premise deployment protecting patient data

Read the case

Legal Services

Research time cut in half with 100% verifiable AI answers

50-70% reduction in research time across the firm

AI answers traced to real sources with exact citations

Read the case

MEET OUR TEAM

KJELD OOSTRA

Co-Founder

Two decades building software, one decade proving AI can deliver real ROI.

KJELD OOSTRA

Co-Founder

Two decades building software, one decade proving AI can deliver real ROI.

KJELD OOSTRA

Co-Founder

Two decades building software, one decade proving AI can deliver real ROI.

KJELD OOSTRA

Co-Founder

Two decades building software, one decade proving AI can deliver real ROI.

ENRICO KARSTEN

Co-Founder

20 years building and scaling IT businesses, from vision to execution.

ENRICO KARSTEN

Co-Founder

20 years building and scaling IT businesses, from vision to execution.

ENRICO KARSTEN

Co-Founder

20 years building and scaling IT businesses, from vision to execution.

ENRICO KARSTEN

Co-Founder

20 years building and scaling IT businesses, from vision to execution.

JEROEN THIJS

Co-Founder

Serial founder and venture investor focused on scaling data science and high-tech companies.

JEROEN THIJS

Co-Founder

Serial founder and venture investor focused on scaling data science and high-tech companies.

JEROEN THIJS

Co-Founder

Serial founder and venture investor focused on scaling data science and high-tech companies.

JEROEN THIJS

Co-Founder

Serial founder and venture investor focused on scaling data science and high-tech companies.

THE ENTERPRISE DATA QUALITY CHALLENGE

Enterprises struggle with data quality for predictable reasons. Volume exceeds manual quality checking capacity. Legacy systems produce data that conflicts with modern sources. Ownership is unclear, so accountability fragments across teams. Enterprise data quality becomes everyone's problem and nobody's priority until AI projects fail.

Quality problems destroy AI reliability and undermine governance. Models trained on inconsistent data produce unreliable predictions that erode trust. Compliance teams can't trace decisions when lineage is missing. Regulatory pressure compounds these challenges. The EU AI Act requires transparency and traceability, particularly for high-risk AI applications. Meeting these requirements demands quality foundations most enterprises lack.

At scale, data quality for AI requires three critical capabilities. Lineage tracking shows data flow from sources through transformations. Quality monitoring scores data continuously across multiple dimensions. Data contracts enforce standards automatically between systems. Together, these practices make enterprise data quality operational, not aspirational. Organisations with strong quality foundations scale AI confidently. They meet regulatory requirements, trust their outputs, and move pilots to production systematically. Those without quality foundations remain stuck, unable to trust data enough to act on AI insights. Quality isn't infrastructure work. It's the competitive advantage that enables transformation.