Typed in, typed out
You send a state, a question and the allowed options. You get back one of those options. Nothing else can come out.
The open foundation System One model for software and autonomous agents. Send a situation, a question, and your allowed choices. It returns the exact decision with calibrated probabilities in a single sub-20ms pass—zero tokens generated, zero hallucinations.
FRQ is Faruq without its vowels. Faruq, said fa-rook, is an Arabic name for the one who distinguishes. Its root, f-r-q, means to separate, to tell apart, to decide.
The user wants to find the cheapest roundtrip flights from New York to London departing Dec 12 and returning Dec 20.
Which tool should execute this?
Model
Which queue. Which tool. Yes or no. Safe or not. A System One model is built for that one job: it reads the question once and picks from the options it was given.
You send a state, a question and the allowed options. You get back one of those options. Nothing else can come out.
Faruq is a 150M-parameter encoder. It scores every option in one read. No text is generated, so there is nothing to parse.
Every option comes back with a probability. Your code can accept the answer, check it again, or hand over to a person.
Platforms
Faruq will come as nodes, apps and plugins that install where you already work, starting with automation platforms such as n8n, Make and Zapier. It reads what just arrived and tells the next step where to go. No code needed.
First
Installed from inside the platform you use. Pick the question, list the options, branch on the answer.
Always
The same model for your own software, from the first day.
Later
Working directly with larger teams that want Faruq inside their own systems.
You pay for decisions, not tokens. The plan is a price per decision, on its own or in bundles.
API
Behind every workflow step is the same call. Describe the situation once, then ask as many questions about it as you need. Each question carries its own options.
A preview of the request shape. The API is not open yet and details may change.
Whatever the decision is about: a message, a document, a conversation so far, a list of tools.
Each one is an instruction plus the options to choose from, or a plain yes or no.
For each question, the chosen option and the probability Faruq gives to every option.
Uses
Each use below is paired with the public benchmark that tests it, and what Faruq scores on it today.
Read a customer's message and send it to the right intent, queue or team.
For each tool an agent has, say whether this request calls for it.
Call a tool, ask a follow-up question, answer directly, or decline.
Does the agreement support a statement, contradict it, or not mention it?
How a piece of financial news feels about each company it names.
Rate a long list of candidate tools by how useful each is for a request.
Maths, graduate-level science, chess and long chains of reasoning. A small model that answers in one pass does not do these, and Faruq scores at or near chance on them today. Use a larger model there.
Results
Numbers from the current development build. Faruq is still in training, so they will change.
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Index on the public Decision Index
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Right on tasks it never trained on
0 is guessing, 100 is perfect. marks a benchmark whose public training set Faruq trained on.
Compared
| Model | Size | Kind | Public index | Full score |
|---|
Early access
Leave your email and tell us which decision you would hand to Faruq. We will write to you when there is something to try.