Your model is only as good as the humans who trained it. We staff the specialists who train, judge, and red-team frontier AI — across RLHF, safety, multimodal eval, and 50+ languages.
As AI models mature and move into healthcare, legal, finance, security, and enterprise operations, the quality of human input becomes the defining variable. More data is no longer enough. The right expertise — deeply embedded in your program — is what separates models that perform from models that fail in production.
General annotators produce general quality. Credentialed domain experts produce production-grade AI. Every engagement is built around the right specialist — Architects who set the standard, Judges who enforce it, Adversaries who stress-test it — matched to the depth your model actually needs.
A clinical reviewer evaluating clinical RLHF pairs catches failure modes a general annotator never sees. A legal specialist red-teaming a legal AI finds liability traps that prompt engineers miss. A trained safety reviewer identifies dangerous knowledge refusals that only a domain specialist recognizes. The credential is not a credential — it is the capability itself.
For frontier AI labs, regulated enterprises, and government programs, the training data, model outputs, and proprietary prompts used in evaluation are among the most sensitive IP a company holds. We build every engagement with data sovereignty as the foundation — on-premise deployment, secure facilities, air-gapped options, and zero third-party data access. Not an exception. The default. Built for programs where data residency is non-negotiable.
The most effective RLHF, evaluation, and annotation programs are not vendor-to-client. They are team-to-team. Our specialists embed directly into your workflows, tools, and quality framework — building the institutional knowledge that makes feedback more consistent and more valuable over time. A standing capability, not a periodic deliverable.
A Quantryx engagement has four recognizable moments — from calibration through steady-state delivery. Each one shows up in your evals.
Every engagement is staffed from one or more of the practices below. Each is led by specialists matched to the credential and depth the task requires.
Your reward signal is only as good as the ranker. Our judges are domain specialists — clinical reviewers, licensed attorneys, senior engineers, and trained safety evaluators — ranking preferences inside their own domain of practice. A reward model that actually reflects expert judgment.
A physician-authored clinical CoT teaches the model physician-style reasoning. A mathematician-authored proof teaches proof-style reasoning. Not crowd-sourced step-by-step — expert-authored.
Jailbreak discovery, bias detection, harmful-output surfacing, and policy-compliance review — delivered by specialists who work inside your trust & safety workflows. Every finding includes reproduction steps and a recommended mitigation.
Every claim traced to a source document. Every citation verified. Every hallucination logged with reproduction steps. Built for products where a wrong answer is a liability, not a nuisance.
Model risk review, bias audits, and compliance documentation that survives enterprise procurement and regulatory inquiry. Built for AI products entering regulated markets — financial services, healthcare, government.
Entity models, taxonomies, and relationship schemas designed by ontologists. For vertical AI, enterprise search, and RAG systems where context and relationship matter more than surface text.
Planning trajectories, tool-call correctness, sub-task decomposition, workflow completion — evaluated step-by-step, not just on final output. For AI agents that act on the world.
Coherence across 100K+ token contexts. Memory recall in multi-turn sessions. Context-drift in long-running agents. The capability benchmarks don't yet capture — but your users notice.
No rotating crowd workers. No ticket-defined scope. No surprise handoffs. Every Quantryx program is built on a defined POD shape (sized by phase) and staffed from a defined role framework (tiered by depth).
Phase one of every program. Builds the evaluation rubric, gold dataset, calibration set, and kappa baseline with your team. The foundation the ongoing program runs on top of.
Steady-state operations. RLHF, red-teaming, factuality audit, content ops, drift monitoring. Includes embedded program management, QA, and calibration. Scales with your program.
Embedded strategic capacity for AI governance, eval framework design, regulatory readiness, and RFP response. Retainer model with direct access to domain leadership.
Build the ground truth. Design evaluation rubrics, author SFT/CoT training data, establish the gold standard. High-stakes, high-judgment work.
Evaluate against the standard. RLHF preference ranking, hallucination forensics, competitive evaluation, inference quality review. The expanded middle of every program.
Break the model before users do. Adversarial testing, red teaming, domain safety auditing — credentialed specialists only.
Anonymized at client request. Every metric is real and verifiable.
Quantryx was built on a clear conviction: the quality of an AI system is ultimately determined by the quality of human input it receives. Better RLHF data produces better-aligned models. More rigorous red teaming produces safer systems. More expert judgment produces more capable models.
We are an AI services company based in the Bay Area. Embedded in your team, not operating at arm's length — delivering the Cognitive Role Framework and the accountability production AI requires.
We bring operational discipline and domain expertise to every engagement — from frontier AI programs to production AI deployments in regulated enterprises.
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