Quick Start Guide

This guide walks through the complete Askalot workflow from questionnaire design to statistical analysis.

Time to complete: ~15 minutes

Step 1: Design in Armiger

Navigate to armiger.dev.askalot.io for AI-assisted questionnaire creation with real-time SMT validation.

Create Your Questionnaire

Using AI Assistant:

Describe your survey requirements:

"Create a customer satisfaction survey. Ask about overall satisfaction, then conditionally ask about specific issues if satisfaction is low."

The AI generates QML code while the SMT solver validates logical consistency in real-time.

Example QML:

qmlVersion: "2.0"
questionnaire:
  title: "Customer Satisfaction Survey"
  codeInit: |
    satisfaction_level = 0
    needs_followup = 0

  blocks:
    - id: b_main
      title: "Your Experience"
      items:
        - id: q_overall
          kind: Question
          title: "How satisfied are you with our service?"
          codeBlock: |
            satisfaction_level = q_overall.outcome
            if q_overall.outcome <= 6:
                needs_followup = 1
          input:
            control: Slider
            min: 0
            max: 10
            labels:
              0: "Very Dissatisfied"
              5: "Neutral"
              10: "Very Satisfied"

        - id: q_reason
          kind: Question
          title: "What is the primary reason for your low rating?"
          precondition:
            - predicate: needs_followup == 1
          input:
            control: Radio
            labels:
              1: "Product quality"
              2: "Delivery time"
              3: "Customer service"
              4: "Pricing"

        - id: q_recommend
          kind: Question
          title: "Would you recommend us to others?"
          input:
            control: Switch
            on: "Yes"
            off: "No"

SMT Validation

Armiger automatically validates:

• ✓ Reachability: All questions can be reached
• ✓ Consistency: Validation rules are satisfiable
• ✓ Acyclicity: No circular dependencies
• ✓ Global satisfiability: Valid completion paths exist

Validation Output:

✓ All items reachable
✓ No circular dependencies detected
✓ Postconditions satisfiable
✓ Global formula: SAT

Flow paths: 2
  Path 1: q_overall → q_recommend (satisfaction > 6)
  Path 2: q_overall → q_reason → q_recommend (satisfaction ≤ 6)

Step 2: Build Campaign in Targetor

Navigate to targetor.dev.askalot.io to configure demographic targeting and launch your campaign.

Create Campaign

1. Click "New Campaign"
2. Configure settings:
3. Name: "Q4 Customer Satisfaction"
4. Target responses: 500
5. Duration: 14 days

Import Respondents

Upload CSV or connect to user database:

user_id,email,age,gender,customer_segment
1001,[email protected],34,M,premium
1002,[email protected],28,F,standard

Configure Demographics

Target demographic distribution:

Age Distribution: - 18-24: 15% | 25-34: 30% | 35-44: 25% | 45-54: 20% | 55+: 10%

Customer Segment: - Premium: 30% | Standard: 70%

Targetor automatically calculates sample sizes and monitors real-time completion by segment.

Launch

1. Attach the Customer Satisfaction questionnaire — this formally verifies it and pins the campaign to that exact version
2. Review configuration
3. Click "Launch Campaign"

Targetor generates unique survey links and begins distribution.

Step 3: Execute in SirWay

Respondents access sirway.dev.askalot.io via unique links.

Lazy Evaluation

SirWay evaluates questions dynamically based on current state:

1. Initial state: Load questionnaire, initialize variables
2. Dynamic flow: Present questions with satisfied preconditions
3. Collect response: Execute codeBlock, update state
4. Re-evaluate: Check preconditions for remaining questions
5. Present next: Show newly eligible questions

Example Flow

Respondent: Slides satisfaction to 4/10
→ codeBlock executes: needs_followup = 1
→ Precondition evaluated: needs_followup == 1 ✓
→ Question appears: "What is the primary reason?"
→ Selects: "Delivery time"
→ Final question: "Would you recommend us?"
→ Survey complete

Contradiction-Free Guarantee

SirWay ensures respondents never encounter:

• ❌ Questions with unsatisfiable preconditions
• ❌ Validation rules contradicting previous answers
• ❌ Dead ends with no path forward
• ❌ Circular dependencies

Step 4: Analyze in Balansor

Navigate to balansor.dev.askalot.io for statistical correction and data export.

Build a Bundle

Balansor's home page is a board of Bundle cards. A Bundle binds one project, one questionnaire, and the campaigns you want to analyze into a single Bronze → Silver → Gold pipeline — every dataset step is scoped to it.

1. Create a Bundle for "Q4 Customer Satisfaction" (project + questionnaire + its campaigns)
2. Extract the Bronze dataset — raw responses with respondent demographics, no adjustments yet
3. Balansor displays actual vs. target demographics

Response Distribution

Segment	Target %	Actual %	Deviation
Age 18-24	15%	12%	-3%
Age 25-34	30%	33%	+3%
Premium	30%	35%	+5%
Standard	70%	65%	-5%

Derive the Silver Dataset

Deriving Silver codes any open-ended answers into categories first, then calculates post-stratification weights over that coded case base:

\[ w_i = \frac{N_{\text{target},g}}{N_{\text{sample},g}} \]

Example Weights: - Age 18-24: 1.25 (under-represented) - Age 25-34: 0.91 (over-represented) - Premium: 0.86 (over-represented) - Standard: 1.08 (under-represented)

Corrected Results

Before Rebalancing: - Average satisfaction: 6.8/10 - Recommend rate: 72%

After Rebalancing: - Average satisfaction: 6.5/10 (adjusted) - Recommend rate: 68% (adjusted)

Export Data

Export in multiple formats:

CSV:

user_id,q_overall,q_reason,q_recommend,weight,segment
1001,4,2,0,1.08,standard
1002,8,NULL,1,0.86,premium

SPSS (.sav): Variable labels and weights included

Excel / Parquet: Full dataset with the weight column, ready for your analysis tool

Summary Statistics: Weighted means, confidence intervals, cross-tabulations

Complete Workflow Trace

Design → Campaign → Execute → Analyze

Armiger:

AI generates questionnaire
→ SMT validates: ✓ SAT, ✓ No cycles, ✓ All reachable
→ Saved as a new version of the questionnaire

Targetor:

Import 10,000 users
→ Select 1,200 (stratified sampling, 2.4× oversampling)
→ Launch campaign
→ Send unique survey links

SirWay:

User 1001 clicks link
→ Load QML, init: satisfaction_level=0, needs_followup=0
→ Present: q_overall
→ Collect: 4 → Execute: needs_followup=1
→ Present: q_reason (precondition satisfied)
→ Collect: 2 (Delivery time)
→ Present: q_recommend
→ Collect: 0 (No)
→ Complete: 3 items, 45 seconds

Balansor:

Load 500 responses
→ Calculate weights by demographic
→ Apply post-stratification
→ Export corrected data
→ Result: Weighted satisfaction 6.5/10 (CI: 6.2-6.8)

Next Steps

→ Creating Surveys - Learn QML syntax in depth

→ Theory - Understand mathematical foundations

→ API Reference - Integrate Askalot programmatically

Summary

Key Workflow:

1. ✓ Design with AI + SMT validation in Armiger
2. ✓ Build campaigns with demographic targeting in Targetor
3. ✓ Execute surveys with lazy evaluation in SirWay
4. ✓ Analyze with statistical correction in Balansor

Key Innovation: Cognitive offloading—AI generates questions, SMT ensures consistency, you focus on research goals.