How Does it Works
Scoping & Taxonomy Design
Understanding Your Risk Landscape
Before we begin any annotation work, we align closely with your team to fully understand your brand’s unique safety guidelines, content sensitivity thresholds, and risk classification requirements. Whether you’re dealing with misinformation, toxicity, adult content, or nuanced brand values, we develop custom taxonomies tailored to your use case.
Our experienced strategists work with you to design detailed annotation protocols that ensure consistent labeling. These include clear definitions, context-specific examples, and edge case treatment—so your annotation foundation is rock solid from the start.
Pilot & Calibration
Precision Through Practice
Once protocols are in place, we initiate a pilot phase where a small sample of your data is annotated by our trained workforce. This is where theory meets practice. The annotated data is then reviewed collaboratively with your team to fine-tune definitions, resolve ambiguities, and align expectations.
Calibration isn’t just about accuracy—it’s about making sure our understanding matches yours. This step ensures everyone is on the same page before we scale, helping prevent costly errors down the line.
Full-Scale Annotation
High-Volume, High-Quality Execution
With the playbook perfected, we transition to full-scale data annotation. Your data is securely ingested into our systems and annotated by a trained, vetted, and domain-aware team. Our multi-tiered quality assurance process includes peer reviews, supervisor audits, and AI-powered validation tools—ensuring maximum accuracy and consistency.
We support diverse annotation types including text, image, video, audio, and multimodal content. From simple labeling tasks to complex judgment calls, we maintain high throughput without compromising quality.
Model Feedback Loop
Smarter With Every Iteration
Annotation doesn’t stop at labeling—it fuels improvement. As your machine learning models evolve, so do our annotations. We integrate model feedback and identify common failure points or edge cases, enabling smarter decisions in future rounds.
Our iterative approach ensures your training data stays ahead of the curve—refined, adaptive, and always relevant to your model’s performance needs. Whether you’re retraining monthly or fine-tuning live models, we help close the loop between human insight and AI efficiency.
