launching Ground: memory for AI systems
tl;dr
Ground is memory infrastructure for AI systems. It decides what information an AI is allowed to see, trust, and cite. When evidence is missing, it refuses.
This is a memory problem, not a model problem.
the problem I kept seeing
Most AI systems do not fail because the model is weak.
They fail quietly because memory is vague, stale, or untrusted. The model guesses. The system ships wrong answers with confidence.
RAG pipelines help, but most teams still end up with the same failure mode:
- no clean boundaries
- no freshness rules
- no versioning
- no citations you can trust
- no honest refusal
what memory actually means
Wrong definition:
AI that remembers things
Correct definition:
A system that decides what information an AI is allowed to see, trust, and cite.
Memory is not storage. Memory is selection, freshness, and boundaries.
the three layers every serious AI system ends up with
- memory
- reasoning
- action
Most teams keep trying to improve layer 2. The leverage is layer 1.
Ground is built to be that layer.
what Ground does
Ground sits between raw data and reasoning models.
It returns:
- accurate memory
- clear boundaries
- cited context you can audit
Or it refuses when evidence is missing.
No guessing. No hidden uncertainty.
founder notes that shaped the product
If you are building with AI, you will eventually rebuild memory yourself. The category is real.
The trap is shipping a system that always answers. That looks great in demos and breaks in production.
Infrastructure should feel boring when it works. That is how you know it is correct.
who this is for
Small serious teams building AI products who need production behavior:
- tenant isolation
- predictable retrieval
- versioning and freshness
- citations you can trust
- refusal when memory is missing
If that is you, this will resonate.