Your AI is only as trustworthy
as your data.
Most AI programmes encounter obstacles when their model is right but their data that is inaccurate. Inconsistent, duplicated, ungoverned enterprise data produces AI outputs that are incorrect. Covasant's Data Foundation layer solves this before any agent ever queries it.
Why data quality issues cost more as you scale AI.
A data quality problem in a reporting environment produces a bad chart. The same problem in an AI environment produces decisions that your organization acts on. The problems do not shrink on their own as you add more agents.
Your ERP system, procurement system, and accounts payable system have different versions of data about your vendor. This impacts the recommendation that an AI agent provides after processing inaccurate data.
AI models learn to work around missing fields. The records with the most missing data are frequently the most unusual ones. An AI agent trained on data with systematic gaps tends to be least reliable.
Regulators and auditors look for lineage of a decision. Without automated data lineage from source to output, your team pieces together the story from logs across multiple systems.
Personal data accumulates across databases and email archives over years of operation. When a breach occurs, accurate notification requires knowing the full scope of exposure quickly.
Finance defines a customer differently from sales, which differs from operations. When AI agents reason across these definitions simultaneously, they inherit the organizational inconsistency.
Development environments use curated sample data. Production data is larger, messier, and full of edge cases the model has not encountered. Data quality issues must be resolved at the source.
One trusted data foundation for AI.
Each component of the Covasant Data Foundation handles a specific transformation in the data lifecycle. Together they ensure that every agent built on CAMS runs on data it can trust.
Covasant's MDM and data quality engine resolves duplicates, conflicts, missing values, type errors, and referential integrity violations. Maintains a single trusted master record across all source systems.
Automated end-to-end data lineage graph. Policy engine for retention, residency, access, and usage. Regulatory compliance mapping for GDPR, CCPA, HIPAA, and PCI-DSS.
Benefit from field-level encryption at rest and in transit, dynamic data masking, PII detection and classification at scale, and complete audit log of every data access event.
Schema detection and enforcement on arrival. Data format normalization across JSON, CSV, and XML. Dead letter queue handling. Lineage tracking from the first byte received.
Pre-built connectors for SAP, Oracle, Salesforce, Workday, and ServiceNow. Bidirectional write-back so agent outputs flow back into source systems instead of creating new silos.
Domain data model library with pre-built canonical schemas for multiple industries. Semantic layer API for consistent entity naming. The shared data contract that every CAMS agent reasons against.
for an AI-first enterprise.
Clean data is the foundation.
Every enterprise product that Covasant has shipped, and every agent that your team builds on CAMS, runs on the Data Foundation layer. It is the reason those agents produce outputs that your business can trust.
Connect to your existing systems
ConnectCore reaches your ERP, CRM, financial systems, and data warehouses via pre-built connectors. No new systems to operate. Your existing infrastructure stays in place.
Ingest and normalisz with IngestIQ
Batch, streaming, and real-time ingestion pipelines bring data into the platform. Schema enforcement, format normalization, and lineage tracking begin from the first byte.
Resolve quality issues with Data Quality
Duplicates are resolved. Conflicts are arbitrated. Missing values are addressed. Type errors are corrected. Referential integrity is enforced. One trusted master record per entity is established and maintained.
Every agent downstream starts from trust
konaAI, CyberProTX, ARIIA, TPRM, and every custom agent built by your team reads from the Data Foundation layer. The quality, governance, and security controls you establish here propagate automatically to every AI output.
Questions that data and AI leaders ask us
If your question is not here, our data platform team will answer it directly. No sales scripts.
Talk to a data specialist →Enterprise data quality is the accuracy, consistency, completeness, and governance of data across every system that an organization operates. For traditional reporting, a quality failure produces a bad chart. For AI, it produces a confident, wrong decision that your organization acts on at scale. Resolving quality issues at the source before any agent queries the data is essential.
Traditional master data management solutions are designed for periodic reconciliation within a single domain. Covasant Data Quality is built for AI data governance at enterprise scale, resolving duplicates, conflicts, missing values, and referential integrity violations across all source systems simultaneously, maintaining a live master record every CAMS agent reads from in real time. The difference matters most for agentic AI workloads.
The timeline depends on how fragmented your current data environment is and how many source systems need connecting. ConnectCore's pre-built connectors for SAP, Oracle, Salesforce, Workday, and ServiceNow remove most of the integration lead time. Teams that want a clear baseline before committing to a timeline should start with the Enterprise Data Maturity Assessment, which maps quality and governance gaps and identifies what it takes to close them.
The CAMS Data Foundation connects seamlessly with SAP, Oracle, Salesforce, Workday, and ServiceNow via pre-built connectors. IngestIQ handles batch, streaming, and real-time ingestion across JSON, Parquet, Avro, CSV, and XML, with lineage tracking from the first byte. Every data access event is logged and istraceable, satisfying both AI data governance requirements and regulatory audit obligations.
When data is duplicated, contradictory across systems, or full of missing fields, an AI agent produces outputs that are coherent and incorrect and difficult to detect precisely because they are confident. For agentic AI specifically, data quality is a compounding problem. Each agent decision builds on the last. So, a bad input at the foundation cascades rather than stays isolated. This is the core reason the Data Foundation layer resolves quality at the source before any agent queries the data.
Find out what poor data quality is costing your AI programme today.
Use the Covasant Data Quality Cost Calculator to estimate the financial and operational impact of your current data environment.