The World’s #1
OLCP Database
OnLine Cognitive Processing
for
Humans and Agents
Introducing Cognitive SQL. Declaratively Simple AI for Lakehouse
Why RAG Fails Enterprises
going into Production
probabilistic results.
SQL with reasoning
on PB scale data
No Vector DB • No RAG-Pipelines •
No LLM Orchestration
Cognitive Join
Reason across documents and relational tables.
Example
Spot AI investment signals by joining earnings transcripts with market data
Cognitive Classify
Apply semantic classification across large datasets.
Example
Classify rows and documents into fraud risk, complaint type, or renewal intent.
Cognitive Topic Discovery
Discovery topics of interest from existing datasets.
Example
Discover Weight Loss impacts of Diabetes drugs in clinical trials
Ontology Views transform
TBs of files into queryable tables
Unleash your agents
to deterministically query without
probabilistic vector search.
Extract inferred insights and facts from PDFs, docs, Spreadsheets, and multimedia
Reuse persistent inference-results across many questions
Enable millisecond-latency AI queries by incrementally refreshing in the background.
Enterprise AI
Use Cases
Cognitive SQL
enables reasoning across
structured and unstructured data.
Financial Intelligence
Detect market-moving announcements by joining earnings transcripts with financial datasets.
Seamlessly plugs into
your Lakehouse
Purpose built for Humans and Agents
Iterate in minutes,
without RAG-pipelines
Q. List candidates who had deep backend coding for 3+ years, then transitioned to Engineering Management (excluding product/project roles)
Resume ingestion from storage
S3
Extract text + OCR + metadata (layout-aware)
Unstructured
SELECT Candidate_Name, resume_url
FROM resume_ontology
WHERE cognitive_classify (
raw_text,
">=3y deep backend coding, then transitioned to
Engineering Management (exclude product/project
roles).",
['Yes','No']
) = 'Yes';