Core concepts

Every Simo request has the same shape: a state describing the situation, and a set of questions about it. Simo reads the state once and returns one answer per question.

#State

The state is everything Simo should know to answer: a support ticket, a policy, an agent's history, a screenshot. It can be:

• A string: "The customer says the parcel arrived broken."
• A JSON object (recommended when there are several parts). The keys become labels the model sees:

state = {
    "policy":  "Refunds are allowed within 30 days of delivery.",
    "ticket":  "My order arrived 12 days ago, broken. I want my money back.",
    "history": "(nothing yet)",
}

• Text with media placeholders such as <image:1> or <video:1>, pointing at images and videos sent in media. See Images and video.

Put rules, policies and context in the state, and keep each question short and specific.

#Questions

Questions are a map from an id you choose to a typed question. The id is the key you read the answer back with.

questions = {
    "refund": Noul("The customer asks for a refund"),
    "team":   Choice("Which team should handle this?", {"billing": "...", "shipping": "..."}),
}

There are two families:

See Question types for each one.

#Calibration

A probability is calibrated when it matches how often it is right. Among all the decisions Simo answers with 0.8, about 80% are correct; among those at 0.99, about 99% are.

That makes probabilities usable as thresholds. Pick the threshold from what a mistake costs:

p = r["approve"].noul
if p > 0.97:   approve()        # a wrong approval is expensive: demand near-certainty
elif p < 0.10: decline()
else:          send_to_person()

#One read, many questions

The cost of a request is mostly in reading the state. Questions about the same state are nearly free, so ask everything at once rather than one request per question. See Pricing.

#Models

simo-1-pro is the model available today. On a confident decision it answers at full speed, and only spends extra time deliberating when a decision is genuinely uncertain. See Models.