We Tell You What We Don't Know
Most AI platforms emphasize what they get right. We believe the more important question is: where might the data be less reliable? Simsurveys is built around a philosophy of quality transparency, giving researchers the information they need to make informed decisions about every data point.
Our quality philosophy: Every synthetic dataset includes per-question confidence scores, AI-generated outlier flags, and interpretive guidance. We do not hide uncertainty — we surface it, so you can decide how to use the data with full awareness of its strengths and limitations.
Multi-Layer QA Process
Every synthetic response passes through a five-stage quality pipeline before it reaches your dataset.
- Contextual Analysis: The system evaluates each question in the context of the full survey instrument, understanding question order effects, topic sensitivity, and expected response distributions for the target demographic.
- Statistical Generation: Domain-specific AI models generate responses calibrated to validated population distributions. Each response is grounded in training data from live panel benchmarks.
- Distribution Analysis: Generated response distributions are compared against expected patterns using KL divergence scoring. Anomalous distributions are flagged before delivery.
- AI Outlier Detection: An auxiliary model independently reviews generated responses for semantic coherence, cross-question consistency, and pattern anomalies that could indicate low-confidence outputs.
- Client Flagging: Questions and respondents that fall below confidence thresholds are explicitly flagged in the delivered dataset with detailed explanations of why the flag was raised.
AI-Powered Outlier Detection
Our auxiliary detection model evaluates every generated response across four dimensions.
Semantic Coherence
Does the response make logical sense given the question? Open-ended answers are evaluated for topical relevance, grammatical structure, and meaningful content.
Cross-Question Consistency
Are a respondent's answers internally consistent? A person who reports high income but selects budget-only product preferences would be flagged for review.
Training Data Sufficiency
Does the model have enough training data for this specific question type and demographic combination? Low-coverage areas are explicitly identified.
Pattern Recognition
Are there signs of repetitive patterns, straight-lining, or other artifacts that would indicate the model is generating low-quality outputs for a particular question?
Question Confidence Scoring
Every question in your delivered dataset includes a confidence score and interpretive context.
Per-Question Metrics
Each question receives a confidence score from 0 to 100 based on training data coverage, distribution alignment, and outlier prevalence. Scores above 80 indicate high confidence. Scores between 60 and 80 suggest the data is usable but should be interpreted with caution. Scores below 60 are flagged as low confidence with specific guidance.
Quality Flags
When issues are detected, the system generates specific quality flags that explain the concern. These include low training coverage for the demographic-question combination, distribution anomalies compared to expected patterns, high outlier rates within the generated sample, and novel question types not well represented in training data.
Interpretive Guidance
Flags are not just warnings — they come with actionable guidance. The system explains what the flag means, how it might affect your analysis, and what steps you can take to account for the uncertainty.
Technical Foundation
The quality monitoring system is built on a dedicated infrastructure separate from the response generation pipeline.
Auxiliary Model Architecture
The outlier detection system uses a separate AI model that is independently trained to evaluate response quality. This model never generates responses — it only evaluates them. By separating generation from evaluation, we avoid the bias of a model grading its own work.
KL Divergence Scoring
Response distributions are scored against validated benchmarks using Kullback-Leibler divergence. This measures how much the generated distribution differs from the expected distribution. Typical high-quality outputs achieve KL divergence scores in the range of 0.05–0.09.
Two-Stage Pipeline
The quality system operates in two stages. The first stage runs during generation and catches issues in real time, adjusting outputs before they are finalized. The second stage runs post-generation as a comprehensive audit, producing the confidence scores and flags that accompany the delivered dataset.
Why This Builds Trust
Transparency about limitations is not a weakness — it is the foundation of trust in synthetic data.
Transparent Limitations
Every dataset comes with honest assessments of where the data is strong and where it may be less reliable. No black boxes, no hidden uncertainties.
Informed Decision-Making
Researchers can make data-driven decisions about which findings to emphasize and which to flag for further validation with live respondents.
Quality Partnership
We treat quality as a shared responsibility. We provide the transparency; researchers bring the domain expertise to interpret the results in context.
Continuous Improvement
Quality flags feed back into our model training pipeline. Areas with low confidence scores become priorities for additional training data collection and model refinement.