Post
202
Most enterprise AI is still guessing.
We boost attention on repeated tokens.
We connect chunks that look similar.
We extract “semantic” relationships from isolated sentences.
But we’re missing the point.
Business meaning doesn’t come from what data looks like. It comes from how it’s used.
🧩 A contract clause copied into a policy doesn’t mean the same thing.
📄 A word like “status” changes meaning from Jira to FDA filings.
📊 A graph without axes labelled is just noise.
Until enterprises can ground context window data in metadata, workflow, and policy, we’re just building smarter approximations.
Check out my latest blog here:
https://www.caber.com/blog/e74c56ee-a15b-443a-be7b-3f3b82c68406
We boost attention on repeated tokens.
We connect chunks that look similar.
We extract “semantic” relationships from isolated sentences.
But we’re missing the point.
Business meaning doesn’t come from what data looks like. It comes from how it’s used.
🧩 A contract clause copied into a policy doesn’t mean the same thing.
📄 A word like “status” changes meaning from Jira to FDA filings.
📊 A graph without axes labelled is just noise.
Until enterprises can ground context window data in metadata, workflow, and policy, we’re just building smarter approximations.
Check out my latest blog here:
https://www.caber.com/blog/e74c56ee-a15b-443a-be7b-3f3b82c68406