Frontier Alignment
High-complexity logic projects that move models from text generators to true reasoning agents. We provide the expert human intelligence that underpins the most advanced GenAI and reasoning systems in the world.
Data Capabilities
Six purpose-built services, each designed for a specific and critical dimension of frontier model alignment.
Chain-of-Thought Reasoning Traces
Expert-written step-by-step logical paths for complex problem solving across mathematics, formal logic, and multi-step planning. These reasoning traces form the foundation of next-generation reasoning models, teaching them to think through problems rather than generating plausible-sounding completions. Contributors are selected for domain depth and trained to produce traces that are both correct and verifiably structured.
Subject Matter Expert RLHF
Preference ranking and nuanced comparative feedback from verified PhDs, MDs, and JDs to ensure domain accuracy across medicine, law, science, and finance. Standard crowdsourced annotation cannot evaluate the correctness of a clinical diagnosis or a legal argument. Subject matter expert RLHF delivers feedback from experts who genuinely understand what correct looks like in your target domain.
Supervised Fine-Tuning Demonstrations
Gold-standard for multi-turn dialogues, instructional tasks, and creative writing, the training signal that defines model behaviour at scale. Our human-written SFT demonstrations give models a precise example of what good looks like, reducing dependence on synthetic data and improving instruction-following fidelity across diverse prompt types.
Adversarial Red Teaming
Systematic stress-testing for safety, bias, and jailbreak vulnerabilities before public release, conducted by skilled adversarial prompt engineers operating against documented risk taxonomies. Our red teamers produce structured findings that feed directly into your and alignment training pipeline, ensuring comprehensive coverage across safety categories rather than ad-hoc probing.
Knowledge Rubric Design
Custom scoring frameworks that define what helpful and honest means for your specific model, built by domain experts who understand nuance at depth. Rubrics are the instruction set for your evaluation programme, translating abstract quality criteria into precise, independently scoreable dimensions that enable consistent and scalable automated assessment across your model development cycle.
Multilingual LLMaaJ Managed Service
Turnkey managed service that combines calibrated LLM judges with targeted human oversight to deliver structured, rubric-based evaluations of multilingual model outputs at scale. Appen owns the entire evaluation pipeline end to end, from prompt engineering and model selection through to ongoing monitoring, with alignment maintained through weekly stratified human QA sampling and proprietary confidence scoring that routes uncertain cases to expert human reviewers for adjudication.
Ready-to-Use Datasets
Appen's proprietary dataset catalogue includes ready-licensed frontier alignment data covering human preference pairs, instruction-following datasets, and constitutional AI training collections. Off-the-shelf AI datasets accelerates development without sacrificing the quality standards frontier model development demands.
Adversarial Prompts for LLM Red Teaming
Benchmark-style adversarial prompt dataset for systematically probing unsafe and misaligned behaviour in large language models, with harm category annotations for repeatable frontier-scale safety evaluation.
Conversational Smartphone Speech
Natural conversational speech including toxic content, enabling stress-testing of moderation and refusal behaviour under realistic interactive conditions.
Computational Science Academic Journal Corpus
Peer-reviewed academic journals supporting evaluation of factual grounding, hallucination behaviour, epistemic uncertainty, and value-aware reasoning in frontier models.
Case Studies
See how leading AI organisations have used Appen's frontier alignment data to accelerate model development and improve quality at scale. Cohere partnered with Appen to scale preference-based fine-tuning for their Command LLM, logging over 2,400 expert contributor hours across 12 weeks.
How Cohere Scaled Preference-Based Fine-Tuning for Enterprise LLMs
Supervised fine-tuning and real-time LLM evaluation with preference annotation at scale for enterprise language models.
Improving Multilingual LLM Performance with Supervised Fine-Tuning
Human preference rankings across 70 dialects to fine-tune a multilingual model for cultural and linguistic nuance.
Rapid-Sprint LLM Evaluation & A/B Testing in Complex Domains
Model accuracy benchmarking and responsible AI compliance using sprint-based evaluation with subject-matter experts.
Rubric-Based Reward for Unverifiable Domains
Developing structured evaluation frameworks to provide reliable reward signals in domains where ground truth is ambiguous.
Insights & Resources
Read our guide on human evaluation vs automated benchmarks or explore our deep dive in the Mastering Large Language Models whitepaper for expert analysis on frontier model alignment, methodology, and the data practices that distinguish leading AI teams.
Multilingual LLM-as-a-Judge Managed Service for Evaluation at Scale
Appen's Multilingual LLMaaJ Managed Service delivers rubric-based LLM evaluation across numerous use cases and languages - combining automated speed with human oversight and cultural precision at scale.
RLVR: Building Reliable, Auditable AI Systems
Reinforcement Learning with Verifiable Rewards trains models to earn rewards only when outputs pass programmatic checks.
Guide to Human-in-the-Loop Machine Learning
How HITL machine learning combines human judgment with automated systems to improve AI accuracy.
Why Human Evaluation Still Outperforms Automated Benchmarks for Reasoning Models
A detailed comparison of human and automated evaluation methodologies across complex reasoning tasks.
Ready to train AI LLMs with confidence?
Talk to our team about frontier model alignment data, from supervised fine-tuning demonstrations to adversarial red teaming at scale.