Who Is Building Ground Truth for AI Agents? Top 5 best companies

As AI agents become increasingly complex, the importance of the high-quality data they are trained on grows as well. Modern agentic systems require high-precision ground truth datasets, human feedback, response evaluation, chains of thought, and data for RLHF. This is exactly why companies engaged in data preparation and validation have become key elements of the AI ecosystem. 

What to look for when choosing a data provider? 

Before choosing a ground truth provider for AI agents, several factors should be evaluated:

• Quality control and verification – a reliable QA technology that combines automated algorithmic checks with multi-level human auditing.
• Availability of subject matter experts – complex AI systems can only be created by people with relevant expertise.
• Support for RLHF and model evaluation – modern models heavily rely on human feedback and evaluation.
• Tools and integration – the ability to process large volumes of data and work with multiple languages and data types.
• Security and compliance – strict adherence to GDPR, ISO/IEC 27001, and SOC 2 standards, especially if agents will work with confidential corporate data.
• A combination of human expertise and automation allows maintaining both quality and speed simultaneously.

Top 5 companies building ground truth for AI agents

1. Scale AI 

One of the most prominent giants of the industry and a key partner for leading AI laboratories (OpenAI, Meta, Microsoft). The company has built a massive ecosystem for generative AI, LLMs, autonomous systems, and next-generation agents. In addition to classic labeling, they specialize in RLHF, model testing, performance evaluation, and AI safety.

Thanks to its colossal infrastructure, the company can support large-scale, ultra-complex projects. The company has its own Scale GenAI platform and a network of subject-matter experts who provide reinforcement learning and model evaluation services at an industrial scale.

Best suited for: large AI companies that need millions of volumes of high-quality textual and multimodal data within tight deadlines.

2. Keymakr 

The company has made a powerful leap forward by offering targeted solutions specifically for agentic AI, computer vision, NLP, and complex autonomous systems. Instead of scattering efforts across standard mass labeling, they combine high-precision manual annotation from their in-house team with automated quality-control tools. A flexible approach and the ability to work with custom requirements make the company attractive to both startups and enterprise clients.

Their own Keylabs platform is a powerful proprietary enterprise solution for image, video, and 3D data annotation with integrated ML automation algorithms, annotator action tracking, and multi-level QA management. They involve subject matter experts to write complex chains of thought and deploy them in RLHF.

Best suited for: custom AI projects, computer vision, multimodal agents, developers of deep research systems, and robotics.

3. Surge AI 

A company that shifted the focus from quantity to the flawless quality of human feedback. It focuses on creating datasets for complex tasks related to logical reasoning, security, and model alignment. Thanks to this, Surge AI has become one of the key players in the field of data preparation for modern LLMs and AI agents that must operate with nuances of language and context.

The platform utilizes a unique framework for adversarial testing, allowing humans to intentionally “break” models to identify vulnerabilities, alongside automated LLM-based QC bots to verify human labeling.

Best suited for: RLHF tasks, building reasoning datasets, alignment tasks, and deep model evaluation.

4. Labelbox

This provider offers complex software to the market. Labelbox positions itself as a full-fledged data management platform, providing clients with tools for dataset preparation, process automation, model evaluation, and the organization of human feedback within a single interface.

The Labelbox platform includes modules for data management, labeling, and testing & evaluation. It allows companies to centralize their data operations. Instead of simply buying a ready-made dataset, teams get an environment where they can automate labeling with their own models and flexibly integrate both internal annotators and external crowdsourced workers.

Best suited for: teams and companies seeking full control and tools to manage the entire data lifecycle and build their own AI infrastructure.

5. Appen  

One of the oldest, largest, and most experienced players in the global AI data market. The company’s main advantage is a colossal global network of contributors and support for hundreds of languages and dialects, allowing it to collect and annotate large-scale multilingual text, audio, and visual datasets.

The Appen platform automates task distribution and quality control across a network of millions of contributors. It offers the capability to scale rapidly in geography and language. For AI agent developers, this means access to ground truth data that accounts for local cultural contexts, speech specifics, and local regulatory requirements.

Best suited for: large-scale data collection, AI localization, and large multilingual projects for international markets.

Who holds the keys to the autonomous future? 

The irony of artificial intelligence development is that the more autonomous AI agents become, the more heavily they depend on humans during their formative stage. We attempt to delegate complex work to machines – from managing investment portfolios to performing surgical operations – but the safety of this transition rests entirely on the shoulders of ground truth providers.

The companies we reviewed today are literally writing the rules of the game by which the global digital economy will live tomorrow. When building your own AI agent, it is essential to consider that while the model architecture or algorithms represent the car’s chassis, the ground truth is its fuel, engine, and roadmap. And the cleanliness and expertise of this fuel will determine whether your agent reaches its destination or spins out on the very first sharp turn of the real world.