Keymakr launches new LLM agent training and data solutions to support the next generation of AI systems
Keymakr has announced the launch of a new suite of tools and services focused on training data and evaluation workflows for Large Language Model (LLM) agents and agentic AI systems.
The new direction reflects growing industry demand for high-quality domain-specific datasets and structured human feedback pipelines as companies develop autonomous AI assistants, coding copilots, research agents, and multimodal systems. According to the company, reliable real-world performance increasingly depends on expert-validated data, reinforcement learning from human feedback, and safety evaluation workflows.
Expanding into dedicated LLM and agentic AI operations
Keymakr has long been collaborating with global technology companies and government organizations on data projects for AI agents and advanced AI systems, accumulating significant experience in this field. At the same time, the company has expanded the capabilities of its Keylabs platform, adding advanced text-labeling tools, RLHF workflows, and agent behavior evaluation features designed specifically for LLM training.
These developments led to the launch of a dedicated LLM and agentic AI direction within the company, including specialized teams, processes, and operational structures focused on this area.
“LLM agents are now doing everyday things - building websites, ordering products, and so on. However, there’s a fundamental gap - models are only as reliable as the data that teaches them to work with these tools,” said Anna Sovjak, Chief Revenue Officer at Keymakr. “What we’re building with Keylabs is a system for structuring human judgment at scale, ensuring that agents perform by expert benchmarks and are ready for deployment in real-world environments.”
Full-cycle data solutions for agentic AI
The new LLM agent training suite combines expert human validation with scalable data pipelines and covers the full training and evaluation cycle for AI systems.
These solutions include:
- Training data for agentic AI models to improve reasoning, planning, and decision-making capabilities
- Reinforcement learning from human feedback (RLHF) to align models with human preferences and domain standards
- AI safety testing and red-teaming to identify risks and vulnerabilities in agent workflows
- Data for reasoning, coding, and creative AI systems
- Multimodal and vision-language data preparation for next-generation AI applications
- Simulation environments and virtual RL training scenarios for agent evaluation
Domain expertise at scale
The company states that the new direction builds on its experience delivering training datasets for computer vision, robotics, physical AI, and machine learning systems. Keymakr operates with an in-house team of more than 600 specialists and a multi-layer quality assurance system designed to support complex enterprise AI projects.
A key part of the initiative is the company’s network of domain experts across industries such as healthcare, engineering, agriculture, software development, and finance. These experts contribute to dataset creation and validation to ensure that AI systems can understand real-world workflows, terminology, and edge cases.
“Scaling LLM systems is a knowledge challenge,” said Anna Sovjak. “The real differentiator is how well models understand domain-specific context and edge cases. Offering our domain expertise with structured data workflows, we enable AI systems to move from generic responses to truly reliable performance.”
