AI systems are only as good as the data they can access. Most enterprise AI initiatives stall not because of model limitations, but because data is scattered, inconsistent, ungoverned, or simply not in a form that AI systems can use. RLM's Data Readiness Assessment identifies exactly what needs to be fixed — and in what order — before you commit to a platform.
After conducting data readiness assessments across industries, RLM has identified the patterns that consistently block AI deployment — and the remediation approaches that clear the path fastest.
Enterprise knowledge is scattered across SharePoint, Confluence, email archives, CRMs, ERPs, and shared drives — with no unified retrieval layer. AI systems can't use what they can't access.
Duplicate records, inconsistent formats, missing fields, stale data, and conflicting sources undermine model training and RAG pipelines. Quality problems compound at scale.
High-value enterprise data often exists as PDFs, scanned documents, email threads, and call recordings — unstructured formats that require preprocessing before they can serve as model context.
No data dictionary, unclear ownership, inconsistent classification, and absent retention policies create legal and compliance risk when that data flows into AI systems.
Even clean, well-structured data requires a retrieval layer — vector databases, embedding pipelines, semantic search — to be useful in a RAG architecture. Most enterprises lack this infrastructure.
Customer data, employee records, and other PII in enterprise datasets must be identified and handled appropriately before being ingested into any AI system — internal or external.
A structured engagement that produces a clear picture of your current data landscape and a prioritized remediation roadmap tied to your specific AI use cases.
We catalog every significant enterprise data source — structured databases, document repositories, communication archives, operational systems — documenting location, owner, access controls, volume, format, and refresh cadence.
For each data source relevant to target AI use cases, we assess data quality against key dimensions: completeness, accuracy, consistency, timeliness, and uniqueness. We quantify quality gaps and their impact on model performance.
We evaluate the infrastructure required to make your data accessible to AI systems — embedding models, vector databases, document parsing pipelines, semantic search layers — and identify what needs to be built or acquired.
We produce a prioritized remediation plan — sequencing data quality fixes, infrastructure investments, and governance improvements by their impact on your highest-priority AI use cases. Includes effort estimates and resource requirements.
"RLM brought structure to a process we didn't know how to start. They asked the right questions, surfaced the right vendors, and kept us from making decisions we would have regretted."
"What set RLM apart was that they didn't have a preferred answer. They evaluated our options honestly and told us what they actually thought — even when that meant recommending a smaller vendor."
RLM's AI advisors help enterprises move from uncertainty to a clear, actionable strategy — with no vendor agenda and no technology stack to sell.
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