Use AI to accelerate review — while keeping human accountability.
MIU applies AI-assisted methods for open-text review, anomaly prioritisation, coding support, synthesis and analytical exploration within a documented quality framework — executed by ResClean, the respondent quality engine we built in-house and let you run for free.
Useful where speed and pattern recognition matter.
Open-text review
Cluster similarity, repetition, irrelevance, language concerns and review prioritisation.
Anomaly detection
Unusual response combinations, source clusters and patterns across large files — 24 checks per respondent, scored in seconds by ResClean.
Coding support
Suggested themes, codeframe drafts and human-reviewed classification workflows.
Insight synthesis
Faster summarisation and hypothesis exploration with traceable source review.
Signals we may review when the data allows.
- Duplicate respondent, source, device or IP indicators.
- Speeders, low engagement and improbable timing.
- Straightlining, patterned answers and logic contradictions.
- Open-end duplication, gibberish, generic or AI-like similarity.
- Geo, language, market and quota inconsistencies.
- Supplier-level removal rates and suspicious clusters.
- Recontact or verification evidence where available.
AI outputs are evidence, not automatic truth.
- Project-specific thresholds and documented criteria.
- Manual review of consequential exclusions.
- Client-agreed rules and escalation paths.
- Reason codes for removed or flagged records.
- Limitations stated in the validation report.
Explore an AI-assisted data quality workflow.
AI accelerates detection and prioritisation while human reviewers remain accountable for quality decisions.
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Design controls before fieldwork
Secure links, source rules, attention checks and questionnaire design reduce avoidable risk.
- Source and link controls
- Attention and consistency checks
- Partner quality briefing
Use AI to prioritise unusual patterns
Large datasets and open text are screened for anomalies, duplication and similarity signals.
- Text similarity clusters
- Response-pattern anomalies
- Supplier-level outliers
Require human review for important exclusions
Analysts inspect context, compare signals and apply client-agreed rules before decisions.
- No blind automated removal
- Documented thresholds
- Escalation for uncertain cases
Make the quality process explainable
The output shows what was checked, flagged, changed and what limitations remain.
- Reason codes and counts
- Quality summary by source
- Transparent limitations
Build fraud controls into the project before fieldwork.
Share the recruitment model, platform, survey, sources, available metadata and risk concerns.