Answer 7 quick questions about your project and get a personalized quality assurance recommendation tailored to your data type, scale, and accuracy needs.
We'll use your info to send a personalized QA recommendation.
Every assessment includes five deliverables — designed to give you a clear, actionable picture of your data quality.
A detailed breakdown of accuracy by task type, annotator, and difficulty tier — showing exactly where your data pipeline excels and where it leaks quality.
We run your existing annotators through a calibration set and measure inter-rater reliability (IRR). You see agreement rates by capability slice so you know which areas need tighter guidelines.
Your data is segmented by domain, modality, complexity, and edge-case frequency. Each slice gets its own accuracy score so you can prioritize improvement where it matters most.
Based on your data type, volume, and accuracy target, we recommend a specific QA tier — from automated spot-checks to full SME review — with projected cost and throughput.
A 30-minute walkthrough of the report with one of our QA leads, covering findings, recommended next steps, and how a pilot engagement would be structured.
Four steps from quiz to quality report — no commitment, no cost.
Answer 7 questions about your project — data type, volume, accuracy target, and current QA process. Takes under 3 minutes.
Send us 200-500 annotated units from your pipeline. We sign an NDA before receiving any data. Supported formats: JSON, CSV, COCO, YOLO, and custom schemas.
Our QA team re-annotates a random subset, measures agreement against your labels, and segments accuracy by capability slice. Turnaround: 3-5 business days.
You get the full report — accuracy gaps, IRR scores, slice analysis, and a recommendation for QA tier and pilot scope — delivered as a PDF and live dashboard.
Anonymized examples from real assessments showing how we identify and resolve quality gaps.
Clinical note annotation had 97% accuracy on diagnosis codes but only 82% on procedure modifiers. Root cause: ambiguous guidelines for multi-step procedures. After guideline revision and one calibration round, modifier accuracy rose to 94%.
Step-level action labeling showed 91% agreement on simple edits but 68% on multi-file refactors. We recommended adding a second-pass SME review for refactor tasks, which brought agreement to 89% within two weeks.
3D bounding box annotation was within spec for cars and trucks (96%) but dropped to 79% for cyclists at long range. We introduced a distance-tier split and separate annotator pool for far-field objects, reaching 93% across all tiers.
SyncSoft.AI 是一家技术公司，帮助企业构建、评估和部署 AI 系统——从高质量训练数据到生产级自动化。
