Custom data observability & quality platforms that eliminate annual cost of silent data failures

Build intelligent monitoring systems with automated anomaly detection, real-time validation, and AI-powered root cause analysis that prevent data downtime before it impacts business decisions and AI model performance.

Leaders trusting our AI solutions:

$12.9M

Annual cost of poor data quality eliminated with automated monitoring

99.9%

Data reliability maintained with AI-powered anomaly detection systems

<5min

Mean time to detection for silent data failures and quality issues

Proud members and partners of

Challenges Xenoss eliminates with data observability & quality platforms

Silent data failures causing millions of annual losses without detection

Data appears healthy but contains subtle corruption, schema drift, or quality degradation that goes unnoticed for weeks. Traditional rule-based monitoring can’t detect unknown anomalies, allowing bad data to corrupt downstream analytics, AI models, and business decisions.

Manual data quality monitoring that doesn’t scale with enterprise complexity

Teams spend 80% of their time writing custom SQL checks and manually investigating data issues instead of building strategic solutions. Manual processes break down as data volumes grow, creating reactive firefighting rather than proactive system health management.

Lack of real-time anomaly detection for AI and ML model performance

AI models degrade silently due to data drift, schema changes, and quality issues that traditional monitoring tools can’t detect. Without continuous model input validation, enterprises deploy biased or inaccurate AI systems that make costly decisions based on corrupted data.

Inability to trace data lineage and identify root causes during incidents

When data quality issues occur, teams waste days manually searching through complex pipelines to find the source. Without automated lineage tracking and impact analysis, incidents cascade through downstream systems before teams understand affected components.

False positive alerts overwhelming data engineering teams

Generic monitoring tools generate thousands of meaningless alerts that drown out real issues. Teams lose trust in monitoring systems and ignore critical alerts while spending excessive time investigating false alarms instead of genuine quality problems.

Data observability gaps across distributed cloud and hybrid environments

Enterprise data spans multiple clouds, on-premise systems, and edge locations with inconsistent monitoring coverage. Teams lack unified visibility into data health across distributed architectures, creating blind spots where quality issues propagate undetected.

Regulatory compliance violations due to data governance failures

Poor data quality leads to GDPR, HIPAA, and industry compliance breaches when inaccurate or incomplete data reaches regulated systems. Without automated data validation and audit trails, enterprises face regulatory fines and legal exposure from compliance failures.

Reactive incident response delaying business-critical data availability

Data quality issues are discovered only after they impact dashboards, reports, or customer-facing applications. Without proactive monitoring and automated remediation, teams spend hours restoring data availability while business operations suffer from delayed insights.

Custom data observability & quality platform engineering for enterprise environments

What we engineer for enterprise use cases

AI-powered anomaly detection engines with machine learning models

We develop intelligent monitoring systems that learn normal data patterns and automatically detect unknown anomalies, schema drift, and quality degradation. Our ML algorithms identify silent failures that traditional rule-based systems miss, preventing data corruption before it impacts business operations.

Real-time data lineage tracking and impact analysis platforms

We create comprehensive data lineage systems that automatically map data flows across complex enterprise architectures. Our platforms provide instant root cause analysis and downstream impact assessment when quality issues occur, reducing incident response time from days to minutes.

Automated data quality validation and testing frameworks

We build intelligent validation systems that automatically generate data quality checks based on data patterns and business rules. Our frameworks include continuous testing pipelines, automated regression detection, and comprehensive quality scorecards for enterprise data assets.

Enterprise-scale observability platforms for distributed environments

We engineer unified monitoring systems that provide complete visibility across multi-cloud, hybrid, and edge environments. Our platforms handle petabyte-scale data monitoring with consistent quality metrics, alerting, and governance across distributed enterprise architectures.

Intelligent alerting systems that eliminate false positive noise

We develop context-aware alerting platforms that use machine learning to distinguish between genuine quality issues and normal data variations. Our systems reduce alert fatigue by 95% while ensuring critical data incidents are detected and escalated immediately.

Compliance automation and audit trail generation systems

We create automated compliance frameworks that ensure data quality meets GDPR, HIPAA, and industry regulatory requirements. Our systems generate comprehensive audit trails, automated compliance reporting, and real-time policy violation detection for regulated environments.

Self-healing data quality and automated remediation workflows

We build proactive data quality systems that automatically detect, diagnose, and resolve common quality issues without human intervention. Our platforms include automated data cleansing, schema adaptation, and quality restoration workflows for continuous system health.

Data observability APIs and integration frameworks

We develop comprehensive API layers and integration frameworks that connect data observability with existing enterprise tools and workflows. Our systems integrate with CI/CD pipelines, data catalogs, and business intelligence platforms for seamless quality management.

How to start

Transform your enterprise with AI and data engineering—faster efficiency gains and cost savings in just weeks

Challenge briefing

2 hours

Tech assessment

2-3 days

Discovery phase

1 week

Proof of concept

8-12 weeks

MVP in production

2-3 months

Tech stack for enterprise data observability & quality

Why Xenoss is trusted to build enterprise-grade data observability & quality platforms

We solve the complex monitoring challenges that prevent enterprises from achieving reliable, trustworthy data at scale.

Eliminated the millions of annual cost of poor data quality with AI-powered anomaly detection

Engineered intelligent observability platforms for Fortune 500 companies that detect unknown data anomalies, schema drift, and quality degradation using machine learning algorithms. Our AI-powered systems identify silent failures that traditional rule-based monitoring misses, preventing massive financial losses from corrupted data.

Built enterprise-scale observability platforms handling petabyte data volumes

Developed comprehensive monitoring systems that provide unified visibility across multi-cloud, hybrid, and edge environments. Our platforms handle petabyte-scale data monitoring with sub-second anomaly detection, real-time lineage tracking, and automated incident response for mission-critical operations.

Mastered real-time data lineage tracking and automated root cause analysis

Created sophisticated lineage platforms that automatically map data flows across complex enterprise architectures and provide instant impact analysis when quality issues occur. Our automated root cause analysis eliminates manual investigation overhead and prevents incident escalation.

Eliminated 95% of false positive alerts through context-aware monitoring

Engineered intelligent alerting platforms that use advanced ML algorithms to understand normal data patterns and eliminate alert noise. Our context-aware systems reduce monitoring fatigue while ensuring critical data incidents are detected immediately.

Implemented automated compliance frameworks for regulated industries

Built comprehensive compliance automation platforms with real-time policy violation detection, automated audit trail generation, and regulatory reporting systems. Our frameworks ensure data quality meets strict regulatory requirements while providing complete compliance visibility.

Developed self-healing data quality systems with automated remediation

Created intelligent data quality systems that automatically identify anomalies, perform root cause analysis, and execute remediation workflows. Our self-healing platforms include automated data cleansing, schema adaptation, and quality restoration maintaining 24/7 system health.

Engineered unified observability APIs integrating with enterprise tool ecosystems

Built extensive API layers and integration platforms that seamlessly connect data observability with CI/CD pipelines, data catalogs, business intelligence tools, and enterprise workflows. Our integration frameworks enable complete observability adoption without disrupting operations.

Delivered production observability systems processing millions of events per second

Deployed observability systems handling real-time monitoring of millions of data events with consistent performance and reliability. Our platforms maintain sub-second anomaly detection and alerting while processing complex quality validations across distributed enterprise architectures.

Featured projects

Retail | AI & ML

Multi-agent extendable hyperautomation platform for enterprise accounting automation

Learn More

Oil & Gas | AI & ML

ML-based virtual flow meter

Learn More

AdTech

Implementing a proprietary SDK with a lightweight tracker for a new generation mediation platform

Learn More

Solution development | High load

Developing a gaming advertising platform with 1.4B monthly video impressions

Learn More

Solution development | AI & ML

Building performance-oriented mobile DSP with innovative user behavior prediction mechanism

Learn More

Solution development | AI & ML

Fast rollout of AI-powered creative management platform used by Nestlé, Adidas & Uber

Learn More

Solution development | High load

Building a video-on-demand platform with 1.1M monthly users for a leading content distributor in Europe

Learn More

Solution development | High load

Reducing infrastructure costs by 20 times for a programmatic ad marketplace with 1B audience reach

Learn More

AdTech | High load

Multifunctional Customer Data Platform (CDP)

Learn More

Finance & Banking | Hyperautomation

Unified multi-modal neural network for improving credit scoring accuracy

Learn More

Prevent data downtime with AI-embedded observability platforms and automated quality management

Talk to our platform engineers about creating intelligent monitoring systems with real-time anomaly detection, automated root cause analysis, compliance automation frameworks, and unified observability APIs that integrate seamlessly with your enterprise data infrastructure and eliminate manual quality monitoring overhead.

Xenoss team helped us build a well-balanced tech organization and deliver the MVP within a very short timeline. I particularly appreciate their ability to hire extreme fast and to generate great product ideas and improvements.

Oli Marlow Thomas,

CEO and founder, AdLib

Get a free consultation

What’s your challenge? We are here to help.

Leverage more data engineering & AI development services

AI capabilities

Data Engineering

Data fabric
Data platforms
Data pipelines
Data infrastructure
Data migration
Data mining and preparation
Infrastructure cost optimization
Data stack integration
Real-time data solutions
Data analytics solutions
Data visualization & reporting solutions
Enterprise application modernization services
Data strategy
Data engineering consulting
Business intelligence

Machine Learning and automation

ML & MLOps
ML system TCO optimization
Model & algorithm development and integration
RPA (Robotic Process Automation)

Custom data observability & quality platforms that eliminate annual cost of silent data failures

Challenges Xenoss eliminates with data observability & quality platforms

AI-powered anomaly detection engines with machine learning models

Real-time data lineage tracking and impact analysis platforms

Automated data quality validation and testing frameworks

Enterprise-scale observability platforms for distributed environments

Intelligent alerting systems that eliminate false positive noise

Compliance automation and audit trail generation systems

Self-healing data quality and automated remediation workflows

Data observability APIs and integration frameworks

Tech stack for enterprise data observability & quality

Built enterprise-scale observability platforms handling petabyte data volumes

Mastered real-time data lineage tracking and automated root cause analysis

Eliminated 95% of false positive alerts through context-aware monitoring

Implemented automated compliance frameworks for regulated industries

Developed self-healing data quality systems with automated remediation

Engineered unified observability APIs integrating with enterprise tool ecosystems

Delivered production observability systems processing millions of events per second

Multi-agent extendable hyperautomation platform for enterprise accounting automation

ML-based virtual flow meter

Implementing a proprietary SDK with a lightweight tracker for a new generation mediation platform

Developing a gaming advertising platform with 1.4B monthly video impressions

Building performance-oriented mobile DSP with innovative user behavior prediction mechanism

Fast rollout of AI-powered creative management platform used by Nestlé, Adidas & Uber

Building a video-on-demand platform with 1.1M monthly users for a leading content distributor in Europe

Reducing infrastructure costs by 20 times for a programmatic ad marketplace with 1B audience reach

Multifunctional Customer Data Platform (CDP)

Unified multi-modal neural network for improving credit scoring accuracy

Related content

Best practices for building scalable, reliable, and secure data pipelines

Building enterprise knowledge bases with LLMs: architecture considerations for Vanilla RAG, GraphRAG, and agentic RAG

Data tool sprawl is draining your budget: Here’s how to fix it