Cognyzer is an AI data infrastructure and research-focused company built to solve one of the hardest and most under-addressed problems in modern AI development: high-quality, execution-grounded training and evaluation data for advanced language and agentic systems.
We were built by a team of AI engineers who believe that building great datasets starts at the top, with robust engineering structures, research-driven design, and a relentless focus on consistency. When every layer of the process is designed correctly, from task architecture to review pipelines to automated quality checks, the result is data that consistently moves the needle on model performance.
"Cognyzer prefers to let the data speak first, allowing teams to evaluate quality before committing to deeper engagements. We believe that's the most honest way to build trust."
Building high-quality AI data at scale is an engineering and research challenge, and we treat it as one.
| What We Focus On | How We Do It |
|---|---|
| Consistency at Scale | We build structured workflows, review pipelines, and automated validation systems that ensure every piece of data meets the same high standard, regardless of volume or timeline. |
| Engineering-First Infrastructure | From task design templates to agentic workflow integrations, our infrastructure is built to give dataset creators the tools and guardrails they need to produce excellent work reliably. |
| Research-Informed Design | Our core team studies model capabilities and designs task architectures from first principles, ensuring every dataset is grounded in a real understanding of what models need. |
| Automation Where It Matters | We invest heavily in proprietary automation and agentic pipelines that handle repetitive processes, freeing our engineers to focus on the nuanced, high-value work that makes datasets exceptional. |
The result: data that isn't just high-quality in isolation, but consistently high-quality across every task, every batch, and every project.
We design, build, and validate datasets used for model training, fine-tuning, and evaluation across domains including software engineering, desktop automation, and adversarial reasoning.
We develop new benchmarks that evaluate AI models across complex, real-world scenarios including multi-step reasoning, desktop automation, adversarial code comprehension, and more.
We provide fully vetted, technically skilled teams of dataset engineers that can be embedded directly into your workflows with direct core team oversight.
We build proprietary automation pipelines that accelerate dataset creation at scale without compromising on the quality and precision of human-crafted tasks.
We have contributed 250+ Terminal Bench 1.0 tasks and 300+ OSWorld evaluation tasks to frontier AI labs. Our OSWorld tasks are authored at a difficulty level where Pass@8 = 0, meaning even the most capable frontier models fail to solve them in 8 attempts, setting a new bar for agent evaluation rigor.
Our clients include organizations building and training frontier AI models. Due to confidentiality agreements, we cannot disclose specific client names, but our work directly contributes to improving the capabilities of industry-leading AI systems.
Our core team actively researches, develops, and contributes to cutting-edge AI benchmarks. This isn't side work. It is the foundation of everything we deliver.
SWE-Bench is the industry-standard benchmark for evaluating how well AI models can solve real-world software engineering problems. Our public samples showcase the task architecture and delivery structure we follow. Production datasets include full model evaluation results and significantly higher problem complexity.
OSWorld evaluates whether AI agents can perform real tasks on actual desktop operating systems. We are extending this benchmark to Windows and macOS, covering the enterprise platforms where real-world agents need to operate.
Terminal Bench covers high-quality evaluation tasks across system administration, build and deployment, and scientific computing. We have also developed a proprietary automated pipeline that enables rapid, scalable generation of terminal-based tasks with consistent quality.
250+ tasks contributed to Terminal Bench 1.0A benchmark where models are tested on their ability to reason about and debug working code, testing genuine comprehension, not pattern matching. Instead of handing models broken code, we provide fully functional codebases and challenge models to find edge cases and identify risks.
A meta-benchmark that evaluates whether AI models can produce real-world quality datasets on their own, testing a model's understanding of what makes a good training example, how to design tasks properly, and how to define evaluation criteria.
Our core team of four are active researchers who study model architectures, analyze failure patterns, and personally design benchmarks. Every major project has direct core team involvement from initial design through final quality review.
We've invested deeply in building infrastructure that makes consistency possible at scale: structured workflows, automated review pipelines, agentic integrations, and clear quality standards. Quality is built into the process, not dependent on any single person.
Before we build a single data point, we study the model, understand its capabilities, map where it needs to improve, and design task architectures specifically targeted at those areas. Every dataset is purposefully engineered to drive measurable improvement.
Our in-house pipelines and agentic workflows produce datasets at volumes impractical through manual effort alone, while maintaining the precision and consistency of carefully crafted work.
Our team brings years of dedicated experience in AI benchmarking and data engineering. We understand not just how to build data, but what data is actually needed to move models forward.
Unlike large-scale data labeling platforms, Cognyzer is purpose-built for AI benchmark and evaluation data. Every task is designed by researchers who understand model failure modes, not crowd-sourced from general annotators.
Cognyzer is built by a core team of 4 who run the company, supported by a 50+ person engineering team of rigorously vetted contributors and dataset specialists.
We follow a structured, research-driven process for every engagement:
Study the target model's capabilities, limitations, and existing benchmark coverage to identify highest-impact areas.
Architect task taxonomies, define complexity levels, set success criteria, and create consistency guidelines.
Engineering team executes using core team-designed specs, supported by proprietary automation pipelines.
Multi-tier QA: automated checks, engineer review, and core team sign-off before delivery.
We offer flexible engagement models tailored to your specific needs:
Purpose-built training or evaluation datasets. We understand your model's requirements, design a strategy, and deliver production-ready data with full documentation and quality reporting.
Fully vetted dataset engineers embedded directly into your workflows. Ongoing core team oversight with flexible team scaling as your project evolves.
Submit your model, and we run it through our proprietary benchmark suites and deliver detailed performance reports with actionable recommendations.
End-to-end collaboration for research teams building new evaluation frameworks, from task taxonomy design through automated evaluation harnesses.
We believe in contributing to the broader AI research community. Our public work is available on GitHub:
| Repository | Description |
|---|---|
| terminal-bench-training-corpora | Sample task architecture for terminal evaluation across system administration, deployment, and scientific computing |
| OSWorld-Samples | Sample task structure for training and evaluating AI agents on real desktop automation |
| Cognyzer-SWE-Bench_Samples | Sample delivery format for software engineering benchmark problems and evaluation data |
These repositories demonstrate the structure and architecture of how we deliver tasks. Production datasets include full model evaluation results and significantly more complex problem sets, available exclusively to our clients and partners.
We'd welcome the opportunity to discuss how Cognyzer can support your data and benchmarking needs.
We're currently onboarding new clients for Q1 2026. Request a free sample dataset to evaluate our quality before committing.