A new suite of tools and services address need for high-quality domain-specific datasets and human feedback pipelines

Keymakr, a global provider of high-quality AI training data solutions, announces a new suite of tools and services focused on data for Large Language Model (LLM) agents and agentic AI systems.

Offering our domain expertise with structured data workflows, we enable AI systems to move from generic responses to truly reliable performance.”

— Anna Sovjak, Chief Revenue Officer at Keymakr

With the rapid growth of autonomous AI assistants, coding copilots, research agents, and multimodal systems, companies increasingly need high-quality domain-specific datasets and human feedback pipelines to ensure models perform reliably in real-world environments. Keymakr’s LLM tools and solutions address this need by providing expert-validated training data, reinforcement learning from human feedback, and safety evaluation workflows for enterprise AI teams.

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.

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In parallel, the company has significantly advanced its Keylabs platform, introducing enhanced text-labeling tools, RLHF workflows, and agent behavior evaluation capabilities tailored for LLM training. The platform now supports scalable multi-turn dialogue annotation, preference ranking, and structured evaluation pipelines, enabling more efficient and reliable development of agentic AI systems.

These technological advancements, combined with accumulated expertise, are now driving the creation of the new LLM suite of tools and services. Particularly, Keymakr dedicated strategic business divisions, with specialized teams, processes, and operational structures focused on LLM and agentic AI.

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“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
Keymakr’s LLM agent training suite combines expert human validation with scalable data pipelines to help companies train, fine-tune, and evaluate AI systems across a wide range of use cases.

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 LLM direction builds on Keymakr’s decade-long experience delivering training datasets for computer vision, robotics, physical AI, and machine learning systems. The company operates with an in-house team of more than 600 specialists and a multi-layer quality assurance system, enabling it to scale complex data operations for enterprise AI projects.

A key component of this strategic direction is Keymakr’s network of domain experts across industries, including healthcare, engineering, agriculture, software development, and finance. These specialists help create and validate datasets that reflect real-world workflows and terminology, improving model accuracy and contextual understanding.

“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.”