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Cluster · Structure

Analysis that shows its work.

Tables you can hand a client without a caveat, and open-ends coded on dedicated offline models with a coding frame your analyst approves before it counts. The evidence stays attached to every number.

Q5. Satisfaction n=1,000 · weighted
Total Male (A) Female (B)
Top-2 box 68% 63% 72%A
Very satisfied 31% 27% 35%A
Mean (1–5) 3.86 3.78 3.94A

Sig. tested at 95% confidence

Data Processing

We build banner and tab tables that make standout correlations and takeaways visible at a glance. You define the banner plan — typically 10–12 cuts across the top — and we deliver documented tables with base sizes and confidence levels, ready for interpretation.

  • Documented, QA’d data tables ready for analysis
  • Confidence intervals and base sizes clearly marked
  • Replicable format: banner plans rerun without rework across waves
$800
Starting
3–5d
Standard

Spoiler-AI

Proprietary product

Our proprietary tool for analyzing open-end responses at scale. It reads thousands of verbatims, proposes a coding frame, and categorizes every response — on dedicated GPU servers running our own offline models, never piped through OpenAI or Anthropic. The reviewable, client-approved coding frame is the core feature: AI with a review step, not AI replacing rigor.

  • Turnaround in hours, not business days
  • Reviewable, editable coding frames — you approve what ships
  • Runs on dedicated offline models; your data never trains anyone else’s
Hours
Turnaround
Offline
GPU models
$0.28–0.45
Per response

Example deliverable

A tab, the way your client reads it.

Banner cuts across the top, documented bases, significance tested at 95% confidence.

Q5. Overall satisfaction with current provider Base: All respondents · weighted
Gender Age
Total Male (A) Female (B) 18–34 (C) 35–54 (D) 55+ (E)
Base (n) 1,000 492 508 311 389 300
Top-2 box 68% 63% 72%A 61% 66% 77%CD
Very satisfied 31% 27% 35%A 24% 30% 40%CD
Somewhat satisfied 37% 36% 37% 37% 36% 37%
Neither / nor 18% 21% 16% 24%E 19% 11%
Bottom-2 box 14% 16% 12% 15% 15% 12%
Mean (1–5) 3.86 3.78 3.94A 3.71 3.83 4.05CD

A B C … letters mark results significantly higher than that column at the 95% confidence level.

How Spoiler-AI codes open-ends

  1. 01 Reads verbatims Spoiler reads through thousands of open-ended responses.
  2. 02 Proposes a frame It clusters them into a draft coding frame.
  3. 03 You approve You review and edit the frame before anything is coded.
  4. 04 Codes everything Every response coded — quantifiable output in under 24 hours.

Have a study in mind?

Spoiler-AI processes every job on dedicated offline models. Your data stays inside secure infrastructure.