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AI-native data quality and enrichment
DataCommand applies large language models, automated stewardship, and explainable quality scoring to the master data problems that legacy tools struggle to solve at speed or scale.
Data quality tools built before AI cannot match what AI now demands.
Most enterprise data quality and MDM software was architected in an era when quality meant rule-based matching, regex patterns, and human-curated reference data. The technology still works that way. It was designed a decade before large language models existed and a decade before enterprises started running them in production.
That gap shows up at scale. Data teams spend months tuning rules that an LLM can replicate in a weekend. Stewardship backlogs grow because every exception needs a human. Migrations stall on data quality issues that should be solved automatically. Master data programmes that should take six months take eighteen, and the maintenance never ends.
Built on the IBM watsonx stack. Tested against the real problem.
DataCommand started as a research question. If large language models could draft a legal contract, translate a document, and answer a technical query, could they also do the unglamorous, high-stakes work of matching, merging, and enriching enterprise master records?
The work began on IBM's watsonx.ai platform under the Partner Plus programme. We benchmarked the Granite foundation models against open-source alternatives and against the rules engines our clients were already running. We built test harnesses on synthetic master data, then on real client data under NDA. Where the model performed, we kept the approach. Where governance and explainability were weak, we engineered the controls ourselves.
The platform that emerged is not a wrapper around a chatbot. It is a working enterprise system, with the governance, integrations, and deployment patterns that procurement teams require.
Benchmarked watsonx Granite, GPT-4, Claude, and Mistral against rules-based enrichment
Tested matching, merging, and stewardship on synthetic and real master data
Integrated watsonx.governance for model oversight and explainability
Built deployment patterns for client tenant and managed service modes
Validated against ISO 27001, GDPR, and NIST CSF requirements
An enterprise data platform that does the work, not just the analysis.
DataCommand runs the full master data pipeline. Ingest, match, merge, enrich, validate, steward, deploy. The AI handles the cases legacy tools cannot. Human stewards handle the exceptions the AI flags. Everything is logged and explainable for audit.
Automated match and merge
LLM-driven entity resolution across customer, supplier, product, and asset domains. No rules to tune; the model adapts to your data.
Enrichment from any source
Pulls supplementary data from approved external sources and internal systems. Validates, normalises, and writes back to master records.
Explainable quality scoring
Every record carries a quality score with the reasoning attached. Stewards see why a record is flagged and what to do about it.
Stewardship workflows
Human-in-the-loop review for exceptions. Configurable approval chains. Role-based access and audit logs as standard.
Multi-domain coverage
Customer master, supplier master, product master, asset master, MRO catalogues. One platform, multiple domains.
Deployment flexibility
Runs in your tenant, in a managed Wiz environment, or hybrid. Migrates from existing MDM platforms without re-platforming.
Architected for enterprise. Hardened for production.
DataCommand runs on IBM's watsonx platform with full enterprise security, governance, and deployment controls. The architecture supports both client-tenant deployment and managed service operation.
Foundation models
IBM Granite (data, code)
Multi-model orchestration
Fine-tuned for enterprise domains
Governance
watsonx.governance
Explainability and lineage
Audit logging
Integrations
Collibra · Microsoft Purview
SAP · Dynamics · Salesforce
Snowflake · Databricks · MS Fabric
Security
ISO 27001 controls
AES-256 at rest, TLS 1.3 in transit
Role-based access · SSO
Deployment
Client tenant or managed service
Azure, AWS, IBM Cloud
Kubernetes orchestration
Compliance
GDPR aligned
NIST CSF mapped
EU AI Act readiness