Introducing the Toloka Quality Loop 

For over a decade, Toloka has powered the data behind many of the world’s leading generative and agentic models to help them reason, plan, and act safely in the real world. Our expert-verified, human-generated data has supported breakthroughs across reasoning, coding, text, image, video, and audio generation.

And our focus reflects a hybrid approach that blends human expertise with an adaptive platform to create data pipelines that keep learning and improving.Throughout, we've maintained an unwavering focus on safety and reliability.

As AI systems grow more complex and specialized, so does the challenge of defining and maintaining quality at scale. Frontier projects move at extraordinary speed and demand precise definitions around the meaning of “great quality”.

Pure clean data doesn’t always translate to quality signals for AI training, as diversity and human naturalness often lead to better, more helpful model responses. Given these human-driven nuances and the industry’s 99% quality standards for agentic work, projects now require a more deliberate and coordinated process that scales quickly.

At Toloka, we've developed a new approach to meet these evolving needs: scaling quality iteratively through every data point.

Human signal at scale 

Toloka’s Quality Loop is a closed-feedback system that continually reviews and refines data to strengthen downstream model performance. Individually, each component delivers good results. Together, they provide precise control over every data point for maximum model improvement signal and cost efficiency.

5-stage quality system

Toloka’s quality management system is built on proprietary agentic technology to provide precise control at every step of the data journey.

1. Calibrate with BILT (Quality Agent)

Tech: Quality agent

BILT captures Toloka’s best practices as structured quality standards and automatically applies them across training, quality checks, and LLM verification pipelines.

2. Scope with full depth of industry knowledge

Tech: Delivery agent

We work consultatively with clients to define clear objectives and measurable quality targets before production begins. The Delivery Agent analyses previous projects to generate draft specifications and quality frameworks, which our wider team refines. Together, they establish a detailed Quality SLA that outlines standards and key control points across the pipeline. Using an agent-assisted process shortens setup time while maintaining full transparency and expert oversight.

3. Onboard experts with confidence 

Tech: Expert automatch and anti-fraud agents 

Our Expert Automatch and Anti-fraud Agents identify and verify qualified contributors with relevant domain expertise. These systems analyse background data to detect anomalies and confirm compliance with documentation and data-handling requirements so every contributor meets the quality and security standards defined in the Quality SLA.

4. Fine-tune control with Copilot

Tech: Production Copilot

Domain experts generate high-signal human data while Toloka’s Copilot provides real-time feedback and LLM-driven guideline checks. From clarity and context alignment to guideline adherence, it flags potential issues as work is produced and catches problems early rather than after submission. Depending on client preference, Copilot’s assistance level is adjustable to balance efficiency with authentic human signal, keeping all outputs human-generated.

5. Evaluate and Improve

Tech: Quality and training agents (multi-tool agentic system)

Evaluation across our projects is led by Quality and Training Agents who apply BILT principles to assess content and workflows against the Quality SLA. Their scoring system converts qualitative judgments into traceable metrics that feed directly into expert retraining and automated pipeline refinements, strengthening dataset quality over time.

Make your quality precisely measurable

When clients say "responses should be thorough, but concise," we translate that into concrete targets: 200-400 words, 95%+ completeness, <5% off-topic content, and reviewer ratings ≥4/5. We transform subjective requirements into objective metrics. 

These metrics connect directly to Toloka dashboards, giving clients a live view of dataset health and how bias and signal stability evolve over time.

Our two-pronged error analysis (that’s quantitative tracking plus LLM-driven pattern recognition) identifies both immediate pressure points and underlying trends, enabling proactive prevention instead of reactive fixes.

Quality and ROI at scale

Toloka’s Quality Loop delivers measurable results for complex AI workflows. Clients report a 65% drop in escalations, driven by clearer project alignment and stronger, more proactive quality controls throughout production.

Operational efficiency improves across every stage:

• Reduced rework and waste through real-time quality assistance

• 3× faster ramp-up from agent-powered project scoping

• Higher first-pass acceptance rates enabled by multi-layered evaluation

• Consistent delivery of large, high-quality datasets at scale

• Seamless collaboration between training and research teams

Quality and efficiency aren’t opposing forces. When engineered systematically, they reinforce one another. For frontier labs developing sophisticated agentic systems, we provide the precision and reliability needed to scale safely.

Toloka’s vision on AI data quality 

Toloka's focus on quality delivers consistent results at every stage. In a crowded market, that commitment sets us apart and ensures we're ready to support the next generation of AI systems.

From foundational models to frontier-level agentic systems, Toloka AI’s Quality Loop gives you the clarity and confidence to scale safely.

Toloka is the quality partner enabling clients to push AI's boundaries confidently. From foundational models to cutting-edge agentic systems, learn more about how Toloka’s Quality Loop can support your specific requirements for training and evaluating AI models, and accelerate your development.