Multi-Modal Data Quality Accelerators

Covasant’s Unified & Multi-Modal Data Quality Accelerators strengthen enterprise data programs by embedding continuous data validation, anomaly detection, and remediation intelligence across structured and unstructured data flows. They seamlessly integrate into existing data platforms and engineering workflows to elevate trust in every data pipeline.

They enable organizations to validate data at ingestion and transformation, score and monitor quality using AI and business rules, and detect anomalies, drift, and completeness gaps. With governed workflows, teams can proactively remediate issues before they impact downstream systems.

Designed as purpose-built accelerators, they combine automation, AI, and data engineering services to rapidly operationalize enterprise data quality. This ensures consistent confidence layers powering analytics, AI models, and RAG systems.

What Does This Do Better Than Others?

Most DQ tools focus on structured data and rely on static, reactive checks. We deliver multi-modal, AI-native data quality that works across the entire enterprise data estate. This accelerator:

Multi-Modal Quality + AI Intelligence

Validates databases, documents, images, audio, and text, powered by LLM-based rule generation and ML-driven anomaly/drift detection that goes far beyond threshold checks.

Governed Remediation + Compliance

Resolves issues through workflows tied to business logic, providing lineage, traceability, and audit-ready controls required for regulated industries.

Hybrid & Multi-Cloud Enterprise Fit

Works seamlessly across Snowflake, BigQuery, Azure, GCP, and on-prem systems with a rapid deployment model aligned to engineering practices.

AI-Ready Foundations

Ensures data pipelines, vector databases, and RAG systems maintain the quality signals necessary for trustworthy AI and model performance.

Use Cases

The Data Quality Accelerator is designed for complex, high-scale enterprise environments and adapts seamlessly across modern data ecosystems.

Data Quality Monitoring Across MES and ERP

Continuously profiles and scores data from SAP and MES systems (e.g., BOM, inventory, quality logs). Detects inconsistencies, missing references, and raises alerts with remediation recommendations.

PII Detection and Anonymization in Customer & Transaction Data

Scans core banking and CRM databases for PII such as PAN, Aadhaar, SSN. Automatically anonymizes and pseudonymizes data based on region-specific compliance policies (GDPR, CCPA, RBI).

Regulated Industries & Critical Reporting

Ensuring Clinical Notes and EHR Text Integrity

Applies multi-modal DQ pipelines to EHR documents and scanned lab reports. Uses semantic scoring, coherence checks, and flags illegible or incomplete data before feeding into ML pipelines.

Privacy-Aware Subscriber Interaction Logs

Applies data masking and differential privacy to call records, IVR transcripts, and usage summaries. Ensures customer identifiers are protected before being sent to analytics or GenAI systems.

If your business runs on data and AI, this accelerator ensures quality, trust, and readiness at scale.

Business Benefits of Using DMA

Operational Efficiency Gains

Automates quality checks across ingestion and ETL, eliminating manual review cycles and reducing engineering time spent on data triage.
Accelerated Analytics & AI Outcomes

Delivers AI-ready, high-trust data pipelines that improve ML accuracy, reduce LLM/RAG hallucination risk, and speed up insight generation.
Stronger Governance & Compliance

Provides defensible lineage, traceability, and governed remediation logs to support enterprise-grade compliance and audit needs.
Continuous Data Reliability

Ensures real-time validation, anomaly detection, and issue resolution so analytics, reporting, and AI systems always operate on trusted data.
Additional Advantages

Reduces quality firefighting, improves data product adoption, and strengthens the foundation for intelligent automation across the enterprise.