So… we’re using AI to check for AI (amongst other things). We realise how that sounds. But if you can move away from the irony for a moment, we’d love to talk to you about our new AI data cleaning tool.

It’s more interesting than it sounds, we promise.

Squeaky clean data is good. But what actually is it?

If you’re already aware of us, you’ll know that we are massive advocates for quality data. It’s why we developed our bespoke tools like Sheep-Dip.

If you’re new here, then hello. We’re Omnisis. And we are nothing without the quality and integrity of our data.

Since developing Sheep-Dip, we’ve gone even further. We’ve developed an automated data-quality reviewer that audits an entire survey’s worth of respondents in minutes. All we have to do is paste a survey ID into our in-house survey software, Warp, and it returns a ranked list of who to scrutinise – and why.

Our new tool essentially applies the judgement that a senior data-cleaning analyst would… at the speed of software and the consistency of a machine. Doesn’t that sound fancy?

Well, admittedly, this all sounds positive. But how does it actually improve my data quality?

Well, we’re glad you asked.

1. It catches what a manual review might miss. It cross-checks every respondent against 10+ independent quality signals simultaneously – speeding, straightlining, contradictory answers, duplicate respondents, gibberish/AI-generated verbatims, timezone-vs-market mismatches, and the panel’s own fraud scores. This is far more than a human can hold in their head while scanning a grid. Even Brian!
2. Every respondent gets the same scrutiny. Inevitably, a human reviewer’s attention may drift after the first few hundred rows. A cup of tea starts looking increasingly appealing after row 500. This tool applies identical rigour to respondent 1 and respondent 5,000
3. It surfaces survey bugs, not just bad respondents. If respondents mention broken images, missing content, or errors, those reports are pulled to the top in a dedicated alert – turning quality control into an early-warning system for scripting problems too
4. Transparent, reviewable verdicts. Every flag explains itself to the reviewer (“2.1s/question vs 8.4s median”, “answered ‘agree’ to two opposing statements”, “timezone West Africa doesn’t fit UK market”) and exports to an annotated CSV, so the human stays in control of the final keep/remove decision.

But just like us after a double espresso, it doesn’t just operate efficiently. It operates intelligently as well.

1. It understands context, not just keywords. It reads the questionnaire first to work out what the survey is actually about and which markets it covers, then judges each response against that — so short but related responses to verbatims like “AI”, “Channel 5”, or a Saudi respondent answering in Arabic are correctly recognised as valid, not flagged as noise or satisficing.
2. It assesses contradictions the same way a person would rather than mechanically assuming all straightlining is bad. Rather than relying on a fixed word list, it identifies which survey statements genuinely oppose each other and only flags respondents whose answer pattern is logically impossible – e.g. strongly agreeing with both “I prefer to shop online” and “I always shop in store”.
3. It’s tuned to respect honest respondents. Typos, slang, regional dialects, brief-but-valid answers, and native-language responses are explicitly protected. 
4. Detects sophisticated bad actors. Keystroke analysis spots pasted or AI-generated text (far fewer keystrokes than characters, tell-tale punctuation), and pattern-matching catches near-identical respondents and IP and response clusters that look fine individually but suspicious in aggregate.

Okay fine, I’m sold. But does this mean my data will be delivered faster?

The short answer is yes, absolutely. The longer answer is…

• Results in minutes, not days. A review that would take an analyst a full day of eyeballing data is returned in a couple of minutes.
• Scaling without extra cost. Whether it’s 100 completes or 5,000, the workflow is identical and the marginal effort is near-zero.
• No fatigue or inconsistency. The same standard is applied every time, on every project, removing the variability between reviewers and the late-afternoon drop-off in attention.
• Frees analysts for judgment work. Instead of hunting for problems, the team starts from a prioritised shortlist and spends its time on the decisions that genuinely need a human.
• One-click action. Flagged respondents can be removed from live data and quotas recalculated directly, closing the loop from detection to resolution.

But don’t worry – we’re not replacing our team’s jobs with AI. Not even close. The tool still requires professional oversight, it just makes their jobs way more time-efficient. This isn’t a case of “AI replacing the analyst” – it’s the analyst’s time, effort and judgement, encoded and scaled. Instead of having to do this process manually, which is a frankly colossal task, it’s now fully automated to make sure more angles are covered. 

Imagine that one of your least favourite, admin-y tasks (that’s also a huge time sink) becomes fully automated. Well, that’s exactly what we’ve done here. The machine does the exhaustive, repetitive scanning… the human makes the final call with better evidence in front of them. This is how AI should be used in market research, in our humble opinion.