Get started

Codiv developer platform

Codiv runs open System One models. They answer typed questions about your data with a calibrated probability for every option, in milliseconds, instead of generating text.

You send a state, such as a support ticket, a log line, a document or any JSON, together with questions of three types: noul (yes/no), choice (pick a label) and score (a point on a scale). Codiv returns the full distribution for each question, so you can route, filter and escalate on thresholds you choose. The first model on Codiv is OpenJev, which is open weights and open source.

Quickstart →Make your first request in under five minutes.Writing questions →noul, choice and score, and how to phrase them.API reference →Every field of POST /v1/systemone.Coming from Jev? →Keep your SDK and change one URL.

A first request

curl https://api.codiv.ai/v1/systemone \
  -H "Authorization: Bearer $TYPESAFE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openjev-latest",
    "state": "Hi, my Stripe connection keeps failing with a 403 and we launch tomorrow.",
    "questions": {
      "department": {
        "type": "choice",
        "instructions": "Which team should handle this",
        "criteria": {
          "billing": "Payment or subscription issues",
          "technical": "Bugs or integration problems",
          "sales": "Pricing or account questions"
        }
      },
      "frustration": {
        "type": "score",
        "instructions": "How frustrated the customer appears",
        "criteria": ["Calm, just stating facts", "Frustrated but civil", "Very angry, strong language"]
      },
      "is_urgent": {"type": "noul", "instructions": "The message conveys urgency"}
    }
  }'

from typesafe_sdk import Choice, Noul, Score, TypeSafeClient

# Reads TYPESAFE_API_KEY and TYPESAFE_BASE_URL=https://api.codiv.ai
client = TypeSafeClient()

response = client.system_one(
    "Hi, my Stripe connection keeps failing with a 403 and we launch tomorrow.",
    {
        "department": Choice(
            instructions="Which team should handle this",
            criteria={
                "billing": "Payment or subscription issues",
                "technical": "Bugs or integration problems",
                "sales": "Pricing or account questions",
            },
        ),
        "frustration": Score(
            instructions="How frustrated the customer appears",
            criteria=["Calm, just stating facts", "Frustrated but civil", "Very angry, strong language"],
        ),
        "is_urgent": Noul(instructions="The message conveys urgency"),
    },
    model="openjev-latest",
)

print(response.choices["department"].choice)   # "technical"
print(response.scores["frustration"].score)     # 1.0
print(response.nouls["is_urgent"].noul)        # 1.0

import { TypeSafeClient, choice, noul, score } from "@typesafe-ai/sdk";

// Reads TYPESAFE_API_KEY; baseURL can also come from TYPESAFE_BASE_URL
const client = new TypeSafeClient({ baseURL: "https://api.codiv.ai" });

const response = await client.systemOne({
  model: "openjev-latest",
  state: "Hi, my Stripe connection keeps failing with a 403 and we launch tomorrow.",
  questions: {
    department: choice("Which team should handle this", {
      billing: "Payment or subscription issues",
      technical: "Bugs or integration problems",
      sales: "Pricing or account questions",
    }),
    frustration: score("How frustrated the customer appears", [
      "Calm, just stating facts",
      "Frustrated but civil",
      "Very angry, strong language",
    ]),
    is_urgent: noul("The message conveys urgency"),
  },
});

console.log(response.answers.department.choice); // "technical"

Response

{
  "model": "openjev-0.1",
  "answers": {
    "department": {
      "type": "choice",
      "choice": "technical",
      "probabilities": {"billing": 0.0, "technical": 1.0, "sales": 0.0},
      "confidence": 1.0
    },
    "frustration": {
      "type": "score",
      "score": 1.0,
      "legend": {"0": "Calm, just stating facts", "1": "Frustrated but civil", "2": "Very angry, strong language"},
      "probabilities": {"0": 0.0, "1": 1.0, "2": 0.0},
      "confidence": 0.97
    },
    "is_urgent": {"type": "noul", "noul": 1.0}
  },
  "usage": {"input_tokens": 175, "output_tokens": 0}
}

When to use a System One model

Classification and routing, like triage, moderation, intent or topic, when you need a label and a probability rather than prose.
Many decisions per item. Ask dozens of questions about one state in a single request; they are answered in parallel.
Hot paths. A request with a few questions typically finishes in under 100 ms of model time.
Calibrated thresholds. Every answer is a distribution, so you decide how sure is sure enough.

Use a generative model when the output itself is text, such as drafting a reply. A common pattern pairs the two: the System One model decides whether and where something should go, and a generative model writes.

For AI assistants

Every docs page is also available as Markdown: add .md to its URL, for example /docs/quickstart.md. An index of all pages is at /llms.txt, and the whole documentation in one file is at /llms-full.txt.