Service - Data Quality

Synthetic Data QA

AI-generated training data reviewed by expert humans for bias, hallucination, distribution drift, and edge-case coverage. Gartner predicts 60%+ of training data will be synthetic by 2027 this is the trust layer it needs before it touches your model.

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60%+
Gartner forecast: share of AI training data that will be synthetic by 2027
6
Quality dimensions checked: accuracy, bias, diversity, distribution, edge cases, provenance
Auto+Human
Statistical distribution analysis combined with domain expert human review
Data Card
Full provenance documentation delivered with every verified synthetic dataset
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Bias DetectionHallucination ScreeningDistribution AnalysisEdge Case CoverageData ProvenanceQuality VerificationDiversity AuditLabel AccuracyBias DetectionHallucination ScreeningDistribution AnalysisEdge Case Coverage
Synthetic Data Quality QA
✓ DIVERSITY: 9.2/10
✓ REALISM: 8.8/10
⚠ UNDER REVIEW ×12
✗ FAIL ×8
▼ BATCH QUALITY DISTRIBUTION
FAILREVIEW71% PASS ▶
✓ 71.4% PASS RATE · BATCH 18
Data Quality - What It Is

Synthetic data that humans trust

Synthetic data are training examples generated by LLMs, image generation models, or simulation engine which offers cost and scalability advantages over human-collected data. But it introduces a new set of quality risks that human-collected data does not have a systematic biases from the generator, hallucinated labels, distribution drift from real-world data, and blind spots in edge case coverage. The trust layer that synthetic data needs to reach production.

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Live Annotation Interface

Synthetic Data Quality Assurance Dashboard

Quality specialists evaluate synthetic data batches across diversity, realism, coherence, and training utility filtering out low-quality generations before they enter the fine-tuning pipeline.

ConcaveLabel Studio - Synthetic QA · Project: HealthBot Training Data · Batch 18 of 24
SAMPLE ID DOMAIN DIVERSITY REALISM COHERENCE STATUS
SYN-B18-0041 Symptom Triage
9.2
8.8
9.5
 PASS
SYN-B18-0042 Medication Query
5.8
8.4
6.1
 REVIEW
SYN-B18-0043 Lab Result Interp.
2.8
3.4
2.2
 FAIL
SYN-B18-0044 Appointment Booking
9.6
9.1
9.3
 PASS
SYN-B18-0045 Emergency Triage
7.2
4.1
6.7
 REVIEW
Batch Size:2,400 samples Pass Rate:71.4% Avg Quality:7.6 / 10 Generator:GPT-4o · Claude 3.5
How It Works

Three things the pipeline does on every synthetic data QA project

Distribution fidelity analysis
Generated data checked for statistical drift against real reference distributions with topic imbalance, entity frequency anomalies, and demographic over-representation caught before your fine-tuning run begins.
Contamination and leakage detection
Every synthetic example checked for verbatim or near-verbatim overlap with known benchmarks (MMLU, HellaSwag, GSM8K, HumanEval). Benchmark contamination reported at the example level, not just flagged as present.
Human quality spot-check with structured rubric
5–10% sample reviewed against a structured quality rubric with factual accuracy, instruction relevance, response quality, and format compliance assessed per example by domain specialists.
Pipeline Capabilities

What the infrastructure delivers

Adversarial Diversity Engine
The generator systematically varies scenario parameters, linguistic style, and edge-case distribution to prevent training on homogeneous synthetic patterns that don't generalize.
Human-in-the-Loop Filtering
Every generated batch passes domain-expert review gates before delivery. Rejection rates are published per batch so you can audit generation quality and trend over time.
Format-Native Delivery
Data arrives in task-specific schemas compatible with Axolotl, LLaMA-Factory, and OpenRLHF—ready to feed directly into your training run without transformation.
Quality Dimensions

Six dimensions we verify in every synthetic dataset

🎯
Factual Accuracy
Domain expert review of a sampled subset to verify that synthetic examples contain correct facts, accurate labels, and valid reasoning chains. Hallucinations introduced by the generator are identified and flagged. Especially critical for medical, legal, and scientific synthetic data.
⚖
Bias Audit
Statistical analysis of demographic representation, topic distribution, sentiment bias, and outcome distribution across protected categories. Detects both over-representation (the generator favoring certain groups or opinions) and under-representation (systematic blind spots).
📊
Distribution Alignment
Statistical comparison of your synthetic dataset against a real-world reference distribution (where available). Identifies topic gaps, length distribution mismatch, vocabulary drift, and instruction type imbalance. Recommendations for targeted data augmentation to close gaps.
🔲
Edge Case Coverage
Systematic check for coverage of known edge cases, minority classes, and failure-prone scenarios. LLMs tend to generate "comfortable" middle-of-the-distribution examples we identify and flag systematic under-coverage of edge cases that your model will need to handle.
🔍
Label Accuracy
For synthetic datasets with automatic labeling (e.g., image generation with CLIP-based labeling, or instruction-response pairs labeled by the same LLM that generated them), expert human reviewers verify label correctness on a sample. Detects systematic labeling errors introduced by the generation pipeline.
📋
Data Provenance
Documentation of the complete generation methodology: model used, prompt templates, generation parameters, sampling strategy, and post-processing steps. Enables reproducibility, licensing compliance review, and future debugging when model behaviour is unexpected.
What You Get

Verified synthetic data backed by measurable quality proof

Every synthetic data QA project delivers three core outputs alongside the audited dataset.

Audited & Filtered Dataset
Synthetic dataset with flagged examples removed or corrected, benchmark contamination filtered, and distribution imbalances documented. Delivered in your preferred format (JSONL, Parquet, HuggingFace dataset) with a clean/flagged split.
QA Audit Report
Distribution fidelity scores, contamination scan results with matched benchmark examples, human quality spot-check findings, and a per-category error breakdown. Every finding is traceable to specific examples in the dataset.
Data Card with Known Limitations
Full ML data card documenting synthetic generation method, contamination scan scope, distribution analysis methodology, human review coverage, known limitations, and recommended use cases for the audited dataset.
Pricing

Per-dataset
QA pricing

Priced per dataset based on size and review depth required. Includes automated analysis, domain expert human review sample, full QA report, and data card. Volume discounts for large datasets.

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Small (<5K examples, 25% human review)$2.5K – $5K
Medium (5K–50K examples, 15% review)$5K – $11K
Large (>50K examples, 10% review)$10K – $24K
Synthetic data generation + QA bundleCustom quote
Remediation execution (gap-filling + re-QA)+50% on base
Free audit (100 examples)$0

Get 100 examples verified free

Send us a 100-example sample from your synthetic dataset. We will run our full QA analysis bias check, distribution analysis, human review and return a quality report at no cost.

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