We bring you the next-gen Decision Intelligence

Every response is scored on quality as it arrives, so only trustworthy answers reach your analytics.

See how scoring works

Where opinions
become decisions
Decision Intelligence

Every response, scored automatically

Flickly monitors the quality of every response in real time with a scoring engine called Decision Intelligence.

A 100-point quality score, transparent by design

Base score
Step 01 · 100 points
100
Full quality budget
Assigned to every respondent the moment they begin.

Everyone starts at 100

A perfect quality budget assigned to every respondent. Nothing is assumed, the number only moves when there is a real reason.

  • Perfect starting score for everyone
  • Nothing assumed up front
  • The score only moves with evidence
Deductions
Step 02 · quality rules
Full straight-lining
Likert grid
-12
Diagonal pattern
Likert grid
-3
Uniform option index
Choice patterns
-8
Severe low drag engagement
Ranking
-7
Low-quality text
Text quality
-5
LOI speeder
Timing
-14
Running score51/100

Points come off for quality issues

Decision Intelligence watches for low-effort and dishonest patterns, and each detected issue carries its own penalty.

  • Straight-lining and diagonal patterns
  • Speeders who finish too fast
  • AI-flagged gibberish or off-topic text
  • Every rule carries its own point value
Threshold
Step 03 · your quality line
0Your threshold, e.g. 50100
Qualified
89
Excluded
38

Below the line, out of the data

You set the disqualify threshold for each study, and it stays fully visible. Fall below it and the response is excluded automatically.

  • You set the threshold, 50 shown as an example
  • Above the line: counted in analytics
  • Below the line: excluded automatically

Decision Intelligence, tuned to you

Every study is different. Custom DI lets you shape how Flickly judges quality, so the data you act on always meets your bar.

0
quality signals
0
signal categories
0
point scale
Set your own standards

Dial in 18 quality signals across 4 categories to define exactly what a good response looks like for your study.

Adapts to every study

From quick pulses to high-stakes research, calibrate how strict scoring runs on a 100-point scale and move the pass line to match.

Clean data, automatically

Your standards run on every response in real time, so only trustworthy answers reach your analytics.

Write your own quality rules with Custom DI

Custom DI catches contradictions the built-in rules cannot. Set the condition, set the deduction, and every response is checked automatically.

IF two answers can't both honestly be true
"I have never bought this category"
AND
"I would definitely buy it again"
THEN deduct points from the response score
−6pts

You choose the conditions, how they join, and the penalty. Rules score alongside the built-in signals on every new response.

Raw responses to decision-ready data

Our DI engine automatically clears out the noise and low-quality data, leaving you with a clean, reliable set that's ready to act on.

Raw responses
312 collected
#2481Packaging feels premium, I'd buy itUnscored
#2482asdfjkl; zxcvbnmUnscored
#2483Agree · Agree · Agree · Agree · AgreeUnscored
#2484Great value for the price pointUnscored
#2485Finished the survey in 38sUnscored
#2486Would recommend it to a friendUnscored
Decision-ready
184 kept for analytics
#2481Packaging feels premium, I'd buy itQualified
#2482asdfjkl; zxcvbnmGibberish
#2483Agree · Agree · Agree · Agree · AgreeStraight-lined
#2484Great value for the price pointQualified
#2485Finished the survey in 38sSpeeder
#2486Would recommend it to a friendQualified

Start making decisions you can trust

Bad data leads to bad decisions. Flickly cleans your raw responses automatically, so you get a clear, reliable set to act on. Spend less time doubting your numbers and more time using them.

Frequently asked questions

What is Decision Intelligence?

How does Flickly make sure survey data is accurate?

What types of bad survey responses can Flickly catch?

Can I customize the quality rules for each survey?

How does Flickly check open-ended text responses?

What decides whether a response is disqualified?

Why does survey data quality matter for research?