Automated Data Quality Monitoring System
Build an automated pipeline that continuously monitors data quality across sources, detects anomalies and schema drift, and alerts stakeholders with actionable insights.
Workflow Orchestration
Schedule and coordinate data quality checks across multiple sources on recurring intervals
Python-native workflow orchestration with built-in scheduling, retries, and observability — ideal for recurring data quality pipelines
Visual workflow builder with 400+ integrations for teams that prefer low-code orchestration of monitoring jobs
Durable execution engine for complex, long-running data validation workflows that must survive failures
Data Extraction & Profiling
Connect to databases, APIs, and files to extract data samples and compute quality metrics
Converts diverse document formats into structured data, enabling quality checks on unstructured sources like PDFs and emails
Crawls web-based data sources and APIs to pull fresh data for validation against expected schemas and ranges
Document parsing pipeline that normalizes heterogeneous inputs before quality assessment
AI-Powered Anomaly Detection
Use LLMs and statistical models to identify data anomalies, schema drift, and quality degradation patterns
Programmatic LLM framework that lets you define typed data quality validators and anomaly classifiers with optimizable prompts
Unified LLM gateway to route quality analysis prompts across providers with fallback, keeping monitoring costs low
Structured output extraction ensures anomaly reports follow consistent schemas for downstream alerting
Observability & Alerting
Track quality metrics over time, visualize trends, and trigger alerts when thresholds are breached
Traces every AI-driven quality check with cost and latency tracking, making it easy to audit why an anomaly was flagged
AI observability platform with built-in drift detection dashboards suited for monitoring data quality model performance
Lightweight LLM observability layer that logs all quality-check inference calls with usage analytics and alerting hooks
Reporting & Knowledge Base
Store quality findings, generate human-readable reports, and maintain institutional knowledge of data issues
Builds a knowledge graph of data quality findings so teams can query historical issues and root causes in natural language
RAG pipeline over quality reports lets stakeholders ask questions about past incidents and resolution patterns
Conversational interface for non-technical users to explore data quality dashboards and ask about flagged anomalies