AI based on experience.

Question
Will this new player ever pay?
Answer
Watch him — spent in week one. Players like that went whale at 37× the rate.
01 · GAMING
37× | whale rate signal
Question
$400 cart abandoned — send a coupon?
Answer
Don't. Customers like her came back on their own 41% of the time. Save the discount.
02 · ECOMMERCE
41% | would return anyway
Question
Reorder this product now?
Answer
Yes, this week. 71% it's gone before the next delivery.
03 · INVENTORY
71% | stockout risk
Question
Safe to merge this change?
Answer
Split it. Changes like this came back as bugs 64% of the time.
04 · CODE
64% | bug-return odds
Question
Run chat on half the cluster?
Answer
Risky — weeks like yours queued 67% of the time. Keep chat at full.
05 · GPU CLOUD
67% | queue odds
Question
Which VMs can we shrink?
Answer
Most — 82% took the downsize clean. Those three didn't. Leave them.
06 · CLOUD COST
82% | clean downsize rate
Question
And something you've never seen?
Answer
That one I won't touch — nothing in my evidence looks like it.
07 · THE REFUSAL
— | refusal
Not an LLM

A small predictive model of a domain.

The LLM talks. TOVANA predicts.
Turn logs into experience, train a focused model, then let any LLM consult it for measured outcomes.

01 · Logs Experience at scale

Millions of logged requests, commits, players, wallets, VMs — what happened before becomes experience.

→
02 · Training ~1 hour

A focused model, trained fast. No giant foundation-model run.

→
03 · Runtime Cheap inference

Small predictions your LLM can call whenever it needs evidence.

Compact domain model · fast retraining · low-cost serving
Logs become experiencePast states, actions, and outcomes become a small predictive model.
LLM on topUse any LLM for language. TOVANA supplies the domain prediction.
Knows its boundaryA probability when evidence exists — a refusal when it does not.