Overview
The dirty secret of enterprise AI is that roughly eighty percent of failed AI projects fail because of the data, not the model. Hallucinations, wrong answers, biased outputs, and agents that simply cannot find what they need are almost always data problems dressed up as AI problems.
Viscosity’s Data Foundation for AI service makes your data actually usable by modern AI systems. We clean it, govern it, enrich it with semantic context, and prepare it for vector embedding, retrieval augmented generation, and agentic workflows. When your data foundation is right, every AI project downstream becomes faster, cheaper, and dramatically more accurate.
- Data quality and profiling
- Lineage and catalog
- Semantic layer design
- Vector and embedding pipeline
- Governance and access control
- Operations runbook
Why This Matters
Your database is full of valuable information, but AI systems cannot read it the way humans do. Unstructured documents, inconsistent schemas, missing lineage, stale metadata, and siloed sources all sabotage AI performance in ways that are very expensive to debug later.
The most powerful LLM in the world cannot compensate for data that was never ready to be retrieved in the first place. A proper data foundation is the difference between an AI pilot that embarrasses you in a board meeting and one that genuinely transforms how your business operates.
Your Path Through Foundation
Discover
Two to four weeks profiling source systems, identifying high value data, and scoring readiness.
Design and Build
Tailor the semantic layer, governance model, and vector pipeline. Build pipelines and stand up the catalog.
Operate
Operations runbooks, team training, and ongoing optimization so your team can run the foundation independently.
Why Viscosity Technology
We have spent decades inside enterprise databases. We know where the skeletons are buried, where the performance traps live, and how to make data flow cleanly at massive scale. That hard won database knowledge is exactly what modern AI systems need, and it is exactly what most AI consultancies completely lack.
We work natively with Oracle AI Database 26ai and PostgreSQL pgvector because that is where our customers run their production data. When your architecture spans more than one database, we are vector fluid, which means we can show your team how to replicate vector embeddings cleanly across systems so the same semantic intelligence flows everywhere your data lives.
Who This Is For
This engagement is for organizations whose early AI pilots produced unreliable answers and they want to understand why. It is for teams planning to deploy RAG over their corporate documents. It is for data leaders building knowledge graphs or semantic layers. And it is for companies preparing for agentic AI, where autonomous agents will need governed and well structured access to enterprise data in order to make decisions on your behalf.