Glossary
Tabular memory.
A retrieval architecture for structured data: rows and columns from spreadsheets, databases, and reports. Lets AI answer aggregate and analytical questions ("how many," "what's the average," "show me the top N") that don't make sense over unstructured text.
Vector RAG works on prose. GraphRAG works on relationships. Tabular memory works on numbers, rows, and aggregates. The same question can need any of the three. A serious system uses all three.
What this looks like in practice
What questions need this.
"What was our average deal size last quarter?" "Which customers spent more than $10k?" "Sum the hours logged by department this month." Vector RAG can't aggregate. A graph can't sum. Pass these to a generic AI assistant and you get a confident wrong answer or an "I don't have access to that data" deflection. Both are useless.
Why spreadsheets break basic AI products.
Most RAG products treat a CSV like prose: chunk it, embed it, hope a semantically-similar chunk answers the question. It almost never does. Ask "top 5 customers by revenue" and you get back the chunk that contains the words "top 5 customers by revenue," which is the header row, not the answer. Structured data needs structured query, not semantic retrieval.
Why we built it.
Buyers don't think in retrieval architectures. They think in questions. If half your team's questions are quantitative ("how many," "what's the average," "show me the top N"), a system without tabular memory is incomplete. The hard part is detecting which architecture a question needs and routing it there automatically.
The third leg of memory.
Most AI products forget that businesses run on numbers as much as prose. Tabular memory closes that gap. Combined with vector RAG and the knowledge graph, it means the right question reaches the right architecture without anyone choosing for it.
Bring your knowledge.Simply ask.
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