TypeSafe

TypeSafe makes System One models, starting with Jev, for fast, structured decisions inside software. You do not chat with Jev. You send it some state, such as a document, a support ticket or a user message, together with a set of typed questions about it, and get back typed answers your code can use directly:

A Noul is a yes or no question, answered with the probability that the answer is yes.
A Choice picks one option from a set you define.
A Score rates the state on ordered levels you describe.

See TypeSafe’s introduction and question types.

How it differs from a chat model

	A chat model, such as Anthropic or OpenAI	TypeSafe
A call	One turn of a conversation	One state and any number of questions
The answer	Text, often streamed	Typed answers, returned all at once
Usage	Per turn, often with cache counters	One usage record for the whole call: input and output tokens
What drives the cost	Mostly the output	Mostly the input: the state is sent with every call

That last row matters for budgets. Asking several questions in one call sends the state once. Asking them one call at a time sends the whole state again with every question. TypeSafe’s own fan-out pattern recommends batching, and under a Stigg budget the difference is easy to see: thirteen questions about one document cost about one document’s worth of input in a single call, and about thirteen when asked separately.

Set up

Wrap a TypeSafeClient from @typesafe-ai/sdk. The SDK recognizes it automatically, and gates and meters its systemOne method.

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

const typesafe = stigg.wrap(new TypeSafeClient({ apiKey: process.env.TYPESAFE_API_KEY }));

const { answers } = await typesafe.systemOne({
  model: 'jev-latest',
  state: { ticket },
  questions: {
    is_refund_request: noul('Is the customer asking for a refund?'),
    urgency: choice('How urgent is this ticket?', {
      Low: 'Can wait for the normal queue.',
      High: 'Needs a reply today.',
    }),
  },
  stiggContext: { customerId: 'customer-acme', entitlement: { currencyId: 'credits' } },
});

Every systemOne call is checked before it is sent and metered when the answers arrive. If access is denied, the questions are never sent, so they are never paid for. See Handling denied access. For the client itself, see TypeSafe’s JavaScript SDK docs.

Requested usage

Because the cost of a TypeSafe call is mostly its input, the best estimate of a call’s usage is the size of the state, and you know it before the call. estimateFromState(tokensPerUnit, charsPerToken) estimates the input tokens from the state’s length (4 characters per token by default) and uses that as the requested usage of the check, so a call the balance cannot cover is denied before it is sent:

import { typesafeRecipe, estimateFromState } from '@stigg/agent-sdk-js';

const recipe = {
  ...typesafeRecipe,
  methods: typesafeRecipe.methods.map((m) => ({ ...m, estimate: estimateFromState(1_000) })),
};
const typesafe = stigg.wrap(new TypeSafeClient({ apiKey }), { recipe });

The estimate is deliberately low rather than high, because an estimate that is too high denies calls the balance could have covered. The real usage is reported after the call either way.

What is reported

Every systemOne response includes a usage object with the input and output tokens of the whole call. The wrapper reads it and reports these metrics:

Method	Event name
`systemOne`	`typesafe.systemOne`

Metric	What it counts
`input_tokens`	Input tokens: the state and the questions
`output_tokens`	Output tokens: the answers
`total_tokens`	The sum of the two, added up by the SDK because TypeSafe does not report a total

Price total_tokens, or the two parts, but never both. There are no cache counters, because a call is not part of a conversation, and no cost, because TypeSafe is not a gateway. Every usage field TypeSafe returns is also included, with a raw. prefix. See How usage is reported.

Getting started

Developer tools

Governance (Beta)

AI agents and LLMs (Beta)

Modeling your pricing in Stigg

Entitlements

Managing customers, subscriptions and contracts

Getting usage data into Stigg

Snap-in widgets

Importing and exporting data

Going live

Workflow automation

Rolling out pricing changes with confidence

Price localization

Managing your account

High-availability and scale

Security & compliance

How it differs from a chat model

Set up

Requested usage

What is reported

​How it differs from a chat model

​Set up

​Requested usage

​What is reported

How it differs from a chat model

Set up

Requested usage

What is reported