Introducing
The decision model for go-to-market.
Ask whether an account will buy, reply or churn. Commit-1 pulls together what's knowable about it today and returns a calibrated probability in under a second, for a fraction of a cent.
0.7 means 70%, measured on accounts and months it never saw.
Learned from what real accounts did next, across many companies' CRM history.
Send a domain. It assembles 150+ features about the account, as of today.
100,000 accounts in minutes, for tens of dollars.
asks about . Commit-1 assembles the account's context, weighs each feature and returns one number.
Run the same question across a segment. Each account gets its own context and its own probability.
Your whole market in about , for about . Color shows probability.
The highest-probability accounts sit at the center. ranks # of 100,000.
One number per account fits any tool that can sort, filter or branch.
Add a Commit-1 column. Every row gets a probability.
| Account | Owner | Commit-1 · opp_90d |
|---|---|---|
| Dana R. | 0.72 | |
| Sam K. | 0.97 | |
| Dana R. | 0.31 | |
| Priya N. | 0.08 |
Let your agent send only when p > 0.3.
Rank every account in a territory on one scale.
Ask about 30, 60 and 90 days to see when an account warms up.
Ask once per play and run the one with the best odds.
Every training example is a real account, frozen on a past date and labelled with what it did next: opened an opportunity, replied, churned, or nothing at all. Commit-1 learned what tends to come before each outcome, across many companies' sales history.
Ask a chatbot whether an account will buy and you get a plausible paragraph. It has never seen what accounts did after a leadership change, or what a hiring spike in the buying team means for next quarter. Commit-1 has.
We replay history in the order it arrived. In training, the model sees an account exactly as it looked on that date, and the label comes only from what happened after. That's what keeps the probabilities honest.
"snapshot_at": "2026-03-01", "account": "", "seller": "", "features": 150, "question": "opportunity within 90 days", "label": 1
Commit-1 returns a probability, not prose. Put it in a filter, a sort or an if-statement.
{ "type": "yes_no", "p": 0.72 }
Each week brings new outcomes. A new candidate trains on them and ships only if it beats the live model on accounts and months it never saw. We tune how context is assembled in the same loop.
Of every 100 accounts Commit-1 scores at 0.7, about 70 open an opportunity. So a rule like "call everything above 0.6" does what you expect. We check this on accounts and months the model never saw in training.
Send an account, a seller and as many questions as you like. Get a probability for each.
{
"model": "commit-1",
"account": "",
"seller": "",
"questions": {
"opp_90d": {
"type": "yes_no",
"instructions": "Will open an opportunity with us in the next 90 days?"
}
}
}
{
"model": "commit-1",
"answers": {
"opp_90d": {
"type": "yes_no",
"p": 0.72
}
}
}
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