Designed around your model objective.
Each program is designed around the model objective, target tasks, domain requirements, language needs, and reviewer expertise. Most programs are delivered through Argos Myriad, the Argos Data Platform, with its customizable tooling providing the task environment, embedded QA controls, and secure expert workflows. For clients who prefer to operate inside their own platforms or through structured file exchange, programs are configured to integrate accordingly.
Where LLM Training Data Services is applied.
Three ways we train.
Each program is built around the model objective, target users, operating conditions, and performance requirements.
Domain-specific pre-training data services for building stronger, more relevant LLM foundations
End-to-end RLHF training datasets built from expert human feedback for model alignment and optimization
Human-in-the-loop data preparation for retrieval-augmented LLM workflows
Training data, treated as an engineered AI data operation.
LLM performance depends on the quality, relevance, and consistency of the data used to shape model behavior. Generic or poorly governed training data introduces noisy signals, weak domain adaptation, hallucination risk, and unreliable outputs in production.
Argos Data combines multilingual depth, domain specialists, and structured operational governance to deliver training data that holds up under enterprise review. We define task criteria, data formats, reviewer qualifications, and validation rules before production begins. Datasets are consistent, auditable, and ready for downstream model development.
For enterprise AI teams, this makes training data a measurable input into model performance, connecting reviewer expertise and quality controls directly to instruction following, domain accuracy, and multilingual reliability in production.
High-quality, model-ready datasets for training, adaptation, alignment, and continuous improvement.
LLM Training Data Services give enterprise AI teams high-quality, model-ready datasets for training, adaptation, alignment, and continuous improvement. The result is stronger instruction following, better domain performance, improved multilingual reliability, reduced model error, and more dependable LLM behavior in production environments.
Automated Response Evaluation at Large-Scale AI Training Volume
Argos Data built a unified LLM evaluation environment that managed 70,000 long-form prompt-response annotations with 10–12 embedded quality checks per task, without fragmenting reviewer workflows.
Optimizing Multimodal LLMs With a Custom Annotation Tool
Argos Data built a custom multimodal annotation tool in two weeks, helping a global technology provider cut quality issues by 98% and reduce project backlog by 90% across 4,000+ image conversation threads.
Multilingual Spoken Agent Evaluation at Scale, with Zero Backlog
Argos Data deployed an automated three-pass-plus-adjudication pipeline for multilingual spoken agent evaluation across five languages, cutting per-task time by more than half while maintaining zero ingestion backlog.
From pilot to production.
Share your model objective, language coverage, and quality requirements. A member of our team will follow up to scope a structured, human-in-the-loop data program.