AI Data
The data that makes models and agents work — and the QA that proves it did.
Agent trajectories, reasoning traces, preference data, evaluation sets and RL environments. Built by a named team under a documented quality process, with inter-annotator agreement measured and reported rather than asserted.
Positioning
We're not the cheapest, and that stopped being the interesting question.
Bulk annotation is being automated and priced accordingly. The work that still requires people is the work that requires judgment — classifying why an agent failed, correcting a reasoning chain, deciding which of two outputs a domain expert would prefer and being able to say why.
No AI lab owns a piece of us.
We're independently held. No model provider, cloud vendor or AI laboratory holds equity or board representation. Your data isn't training anything of ours, and every person who touches it is a salaried employee under an individual confidentiality agreement.
Read the Full Independence StatementFive Things
Judgment work, not volume work.
Agent trajectory & failure-mode data
Real production traces, captured, classified against a taxonomy built for your domain, and turned into corrected trajectories and a regression corpus. Recurring work with a compounding return.
Recurring packages →Reasoning trace data
Chain-of-thought, and — more valuable — chain-of-thought correction: where the reasoning went wrong and what the right path was. Multi-layer QA with full traceability.
4–8 weeks →RLHF & preference data
Preference pairs and rankings from people who understand the domain. Hand-selected and qualified against a reference set, not crowdsourced.
Scoped per corpus →RL environments
Runnable environments — repository and CLI harnesses, browser and GUI harnesses — with verifiable reward functions and a test suite.
6–10 weeks →Multilingual & Southeast Asian language data
Vietnamese, Thai, Indonesian and Khmer preference data, safety adversarial sets, cultural-nuance evaluation, multilingual jailbreak testing.
3–6 weeks →Quality Process
Three layers, and a number.
Annotator, qualified against a reference set before touching client data.
Peer review on a defined sample rate, escalating on disagreement.
Subject-matter audit against the rubric, with findings fed back into rubric revision rather than into individual correction.
We report inter-annotator agreement per project and per dimension. When it's low we fix the rubric, not the data.
Judge the work, not the pitch.
Request dataset samples and you'll get real, representative output — with the QA metadata attached.