Proprietary Artificial Intelligence • QS AI v3.4

Turn Plain English Into Flawless Survey Code.

General LLMs fail at survey logic because they don't understand quota balancing, randomized piping, or complex skip routines. QS AI is custom-trained on millions of verified market research syntax trees to generate production-ready code with zero runtime errors.

Test With Your Questionnaire

Choose Sample Prompt or Logic Pattern:

Create an NPS question (0-10) with branch logic: if rating <= 6 route to Detractor Reason; if >= 9 route to Referral prompt.

// Generated by QS AI Engine v3.4 for Questionnaire Studio
QUESTION Q_NPS {
  type: single_select_scale,
  range: 0 .. 10,
  text: "How likely are you to recommend us to a colleague?"
}

ROUTING {
  if (Q_NPS.val <= 6) {
    goto Q_DETRACTOR_FEEDBACK; // Route to deep-dive reason
  } else if (Q_NPS.val >= 9) {
    goto Q_PROMOTER_REFERRAL;  // Trigger promoter program
  } else {
    goto Q_PRODUCT_EXPERIENCE;
  }
}

Engine Comparison

Why Generic LLMs Fail at Market Research

Survey logic requires strict deterministic constraints, quota balancing, and zero hallucination. See how QS AI compares.

Capability	Generic LLMs	DataPrint QS AI
Survey Syntax Understanding	Prone to hallucinated syntax	100% Validated Syntax Trees
Multi-Tier Quota Balancer	Unsupported / Fails routing	Native Interlocking Quotas
Piped Answer Insertion	Frequent variable collisions	Dynamic Scope & Filter Pipes
Direct Export Formats	Generic text only	Decipher, Confirmit, Qualtrics, CAPI

Turn Plain English Into Flawless Survey Code.

Why Generic LLMs Fail at Market Research

See QS AI Program Your Actual Survey