Rights-cleared human intelligence, packaged so AI developers can use it.

HUTE Intelligence Infrastructure is the part of HUTE that serves teams building AI. Its premise is simple: the data that teaches a model to handle people well is hard to get, hard to check and often legally unusable. We do the hard parts, and we design the work so that it can stand on its own commercially, whether or not HUTE trains a proprietary model of its own.

This page describes what the infrastructure is designed to provide. It describes a service model and a technical approach; it makes no claim about customers or contracts. For the underlying analysis, read Research Report 003.

What we build

Expert-authored and expert-verified data

We commission difficult cases from people who understand them: psychologists, linguists, mediators, teachers, skilled professionals and qualified regional contributors. They write multi-turn scenarios, explain why one response is better than another, rank alternatives and build grading rubrics. The aim is to capture how a professional decides, not only what they decide. Small, carefully curated sets can matter a great deal: one study fine-tuned a model on only 1,000 curated examples and concluded that most of a model's knowledge is learned in pretraining, with a small amount of high-quality data teaching it how to respond [1].

Production traces, turned into tasks

When an AI agent does real work for real people, it leaves a trail: the request, the context, the tool calls, the corrections, the outcome. With a business's authorisation, and only then, the most instructive trails can be turned into repeatable tasks with tests that check the result. This is the logic behind benchmarks such as SWE-bench, which rebuilt real software issues into evaluable tasks [2]. We explain the idea in What are AI production traces?

Environments, rubrics and verifiers

Reinforcement learning needs a way to score behavior. Where an answer can be checked by code, we build verifiers. Where it is a matter of judgement, such as consent, tact or respect for a person's boundaries, we build expert rubrics and report disagreement among judges. See reinforcement learning for socially intelligent agents.

Benchmarks and regional evaluations

Versioned, contamination-resistant evaluations for theory of mind, pragmatics, interpersonal repair, multilingual communication and regional context, built with local participation and reported by subgroup. See Research Report 002.

What customers would receive

Every item would carry its provenance and its licence. Customers should pay for capability they can measure, and we will not promise a specific gain before it has been measured.

Rights, consent and compensation

Production traces and human contributions are only usable if the rights are real. For each source we verify who owns the data, what the contracts and the people involved have allowed, which geographies and purposes are covered, and what must be deleted or returned on request. Contributors are paid, and pay and conditions matter: research on crowd work platforms has documented low earnings and the need for worker-side accountability [3, 4]. We say more on our Responsible AI page, and the analysis is in Research Report 003.

Private HUMAN content is not part of this business. Messages, contact graphs, location trails, recordings and AI-twin memories from the HUMAN product are never sold or used for training by default.

Talk to us

If you are trying to improve how a model handles people, tell us the capability you want to move and how you measure it today. We would rather start from a specific failure than from a catalogue. Contact HUTE.