30–50%
Survey Fraud
Of unvalidated panel responses contain fraud, bot-generated content, or low-quality data that would corrupt findings if shipped as-is.
Industry research · ESOMAR benchmarks
Data Quality Architecture
Quality isn't a promise.
It's an architecture.
Bad data doesn't look bad — that's what makes it dangerous. The Validation Intelligence Loop is a six-stage quality architecture that catches what surface-level quality checks leave behind.
Live Quality Signal
Live
Decision-grade by default.
- Response Quality Score97.4%
- Bot / AI Detection99.1%
- Flagged & Removed15.6%
- Duplicate Caught100%
- OE Quality Pass94.2%
Average across last 90 days of Robin & Berry deliveries
The Problem
Bad data doesn't look bad.
That's what makes it dangerous.
Most datasets look fine on delivery. Weeks later, when the strategy built on them starts missing, the cost is already locked in. Three industry benchmarks for why validation is not optional.
Material.
Decision Risk
The cost of a misinformed strategic decision based on compromised research — category entry, launch pricing, positioning call gone wrong — is rarely small, and rarely recoverable.
Industry analysis · enterprise research spend
40%
Insight Accuracy Loss
Reduction in the predictive accuracy of research findings when fraud goes undetected — enough to flip the direction of any close-call recommendation.
Research benchmarks · 2024 industry review
Validation Intelligence Loop
Six stages. Zero compromises
on data integrity.
Every Robin & Berry dataset passes through a sequential quality architecture — each stage designed to catch what the previous one cannot. By the end, only decision-grade data survives. Full six-stage Loop applies to Robin & Berry full-service engagements. Sample-only, API, and self-serve projects use a tailored subset — confirmed in the scope of work.
01
Respondent Verification
Digital fingerprinting, duplicate IP detection, device type validation, geo-verification, incognito mode and VPN detection at the point of entry. No unverified respondent enters the survey. Entry Gate.
02
Integrated Quality Logic
Straight-liner detection, speeder flags, internal consistency traps, attention check questions, and behavioural signals like right-click, inspect element, alt-tab, and print-screen detection. Flags: ~12%.
03
AI Respondent Matching
Precision alignment between the respondent’s profile and the study’s targeting objectives. AI-powered sampling ensures each respondent is genuinely qualified — not just screener-compliant. AI Verified.
04
Behavioural integrity checks
Behavioural patterns audited end-to-end — inconsistency traps, context mismatch, engagement depth, and response coherence scored before release. 97.4% Clean.
05
Open-End Fraud Evaluator
Gibberish detection, copy-paste flagging, AI-generated response identification, pattern matching, minimal effort scoring, duplicate text removal, one-word answer filtering. Removes: ~3%.
06
Trend Analysis
Wave-over-wave drift detection, sentiment shifts surfaced from OE responses, cohort-level pattern emergence, and anomaly flags raised before they distort the read. Signal Locked.
Process Timeline
Validation runs before, during,
and after collection.
Quality control has three windows — and most firms only use one. We run checks at all three, because each window catches a different class of problem. Post-hoc cleaning alone is the category default, and it's not enough.
Phase 01 · Before Field
Pre-Collection
- Panel member verification & 90-day activity review
- Device fingerprinting and duplicate-identity screening
- IP validation and geographic targeting match
- Survey programming QA — skip logic, trap calibration
- Attention-check design per study, not templated
Phase 02 · In Field
During Collection
- Real-time fraud detection — speeders, straight-liners
- Live engagement monitoring and behavioural scoring
- Pattern recognition for professional survey-takers
- Instant flagging and quarantine of suspect responses
- Session-level signal tracking across survey path
Phase 03 · After Field
Post-Collection
- AI / ChatGPT detection on every open-ended response
- Open-end quality validation — gibberish, off-topic, copy-paste
- Cross-response consistency and internal-logic checks
- Human expert review on edge-case flags
- Final Quality Certificate attached to the dataset
Fraud Detection
We flag what
other platforms
call acceptable.
Our fraud detection architecture operates across four distinct layers — each catching a different class of bad actor. Together, they eliminate the noise that corrupts research findings.
⚡ Behavioural
Survey Behaviour Detection
- Straight-line response patterns
- Speeder detection (time-per-question)
- Internal consistency violations
- Attention trap failures
- Duplicate answer strings
🔐 Identity
Digital Identity Verification
- Duplicate IP address detection
- Digital fingerprinting & device ID
- Out-of-country detection
- VPN and proxy detection
- Incognito mode identification
🖥️ Environment
Environment Integrity Checks
- Right-click / inspect element detection
- Alt-tab and page-switch monitoring
- Print screen flagging
- Viewing page source detection
- Device type enforcement
🤖 AI/Bot
Bot & AI Response Detection
- LLM-generated response identification
- Scripted bot pattern recognition
- Inhuman response timing detection
- Click-farm behavioural signatures
- Cross-survey identity matching
📝 Open-End
Open-End Quality Analysis
- Gibberish and random character detection
- Copy-paste from prompts or other fields
- Irrelevant / off-topic response flagging
- Minimal effort scoring
- One-word and non-substantive responses
📊 Data
Data Processing Quality
- Auto-clean and outlier removal
- Cross-variable consistency checks
- Demographic plausibility scoring
- Response distribution analysis
- Final QC sign-off with quality certificate
OE Fraud Evaluator
Open-ended responses
are one of the strongest
fraud signals.
Free-text answers reveal patterns structured data can't — one of several layers in our quality stack. Our OE Fraud Evaluator runs every open-ended response through eight detection algorithms before it reaches your dataset.
Gibberish Detection
Random characters, keyboard mashing, nonsense strings
Copy-Paste Detection
Prompt duplication, field repetition, external copying
AI Response Identification
LLM-generated text patterns, synthetic response signatures
Minimal Effort Scoring
Single words, non-substantive answers, pattern repetition
OE Analysis — Live Feed
"asdfjkl asdfjkl asdfjkl yes good product nice nice"
Gibberish DetectedPattern RepetitionMinimal Effort
✕ REJECTED
"The product quality is exceptional and provides good value for money. I particularly appreciate the customer service responsiveness."
AI Pattern: LowSubstantiveRelevant
✓ VERIFIED
"As an AI language model, I would say that the product meets consumer expectations and aligns with market standards..."
AI GeneratedLLM Signature
✕ REJECTED
Gibberish DetectorACTIVE
Copy-Paste EngineACTIVE
AI/LLM ClassifierACTIVE
Effort ScorerACTIVE
Bot & Identity Detection
We detect what
respondents don't
want you to see.
Beyond standard duplicate IP checks, our environment monitoring layer captures the behavioural signals that bots, click farms, and motivated bad actors leave behind — even when they try not to.
Geo Verification
Real-time detection of out-of-country responses, GPS spoofing, and VPN routing.
Inspect Element Detection
Flags respondents who open browser dev tools — a key indicator of manipulation attempts.
Alt-Tab Monitoring
Detects when respondents switch tabs during the survey — copying answers from other sources.
Device Enforcement
Restricts survey completion to specified device types — preventing emulator and scripted access.
Digital Fingerprinting
Unique device signatures prevent the same user completing via multiple browser sessions.
Screen Capture Detection
Identifies print screen attempts — protecting proprietary research instruments from leakage.
Quality Certification
Every dataset
delivered with a
Quality Certificate.
We don't ask you to trust us. Every Robin & Berry dataset is delivered with a Quality Certificate — a full audit trail of each validation stage's performance metrics, scoped to the stages applied.
- Stage-by-stage quality breakdown
- Flagged response categorisation
- OE analysis summary per variable
- Final QC sign-off documentation
97.4%
Average Response Quality Score
Entry Verification
99%
Survey Behaviour
87%
AI Matching
98%
OE Quality
94%
Final Output
97%
Live Quality Dashboard
The numbers behind
decision-grade data.
Average performance across recent Robin & Berry datasets — pulled from the Validation Intelligence Loop’s real-time scoring. Every value below is what we ship a dataset against, not what we aspire to.
Live Quality Dashboard
Response Quality Score
97.4%
Bot / AI Detection Rate
99.1%
Flagged & Removed
15.6%
Duplicate Responses Caught
100%
OE Quality Pass Rate
94.2%
Sample of recent project averages · scoped to the validation stages applied per engagement.
Quality Intelligence
Request a data quality
audit for your next study.
✓ Quality Certificate with Every Dataset
See exactly what our Validation Intelligence Loop would catch in your current data pipeline — before bad data costs you a bad decision.