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 is a technology company that helps businesses build, evaluate, and deploy AI systems — from high-quality training data to production-ready automation.
We understand that every business has unique needs. If there's anything you'd like to clarify about our services, pricing, or how SyncSoft.AI fits into your workflow, our team is here to help.
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