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.
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.
What We Build
Our data foundation engagements cover the full spectrum of work that modern AI systems depend on. We start with automated profiling of your source systems across Oracle, Microsoft SQL Server, PostgreSQL, MySQL, and your file stores. We score completeness, consistency, and accuracy. We hunt down duplicates and outliers. Then we build a remediation roadmap that your team can execute alongside us.
From there we map your data lineage end to end. Where does it originate? How does it transform as it flows through your systems? Where does it ultimately land? We build a business glossary so your AI systems and your humans can finally agree on what the same words mean, and we stand up a data catalog using Oracle Data Catalog, Collibra, Alation, or a suitable open source alternative depending on your environment.
The semantic layer is where the real leverage lives. We design an ontology and knowledge graph tailored to your business domain. We enrich metadata so retrieval becomes accurate instead of approximate. We resolve entities across systems so your customer, product, and employee data look like one coherent picture rather than a dozen fragmented ones.
Once the foundation is in place, we build the vector and embedding pipeline that feeds your AI. This includes document chunking strategies tuned to your content, embedding model selection and evaluation, implementation of your vector store whether that is Oracle AI Database 26ai with native AI Vector Search, pgvector, Pinecone, Weaviate, or Milvus, and the incremental refresh strategy that keeps your embeddings current.
Governance ties the whole thing together. We implement row level and column level security so AI retrieval respects your access controls, we classify and tag sensitive data like PII and PHI, we build audit trails for every AI data access, and we set up right to be forgotten workflows for customer data deletion requests.
How the Engagement Runs
Data foundation engagements scale with the size of your data estate and the ambition of your AI roadmap. A typical engagement moves through four phases. We begin with two to four weeks of discovery and profiling where we audit your current state and identify the highest value data sources. Next comes two to three weeks of foundation design where we tailor the semantic layer, governance model, and vector pipeline architecture to your specific use cases. Build and implement is the longest phase and runs from four to twelve weeks depending on scope. We finish with a short handoff and enablement period where your team receives the operations runbooks, training, and ongoing optimization recommendations they need to 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 with the native vector and semantic capabilities in Oracle 26ai first because Oracle is what most of our customers run. But we are equally comfortable across the entire modern data stack including Snowflake, Databricks, Google BigQuery, PostgreSQL with pgvector, and every major vector database on the market.
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.