Welcome to Askalot¶

Askalot is an AI-assisted questionnaire platform where artificial intelligence and formal mathematical validation work together to create logically coherent surveys.

What is Askalot?¶

Askalot combines AI-powered questionnaire generation with SMT-based formal verification to ensure survey logic is mathematically sound. The platform relieves AI from the cognitive burden of simultaneously managing question content, conditional dependencies, logical consistency, ordering, and avoiding contradictions—allowing it to focus on generating meaningful questions while mathematical validation ensures coherence.

Platform Architecture¶

Askalot consists of four integrated services working together to provide a complete survey workflow:

🛠️ Armiger - Questionnaire Design¶

Browser-based development environment for creating QML questionnaires with AI assistance and real-time SMT validation.

AI-assisted questionnaire generation
Z3 constraint solver integration for logical validation
Interactive flow visualization with Mermaid diagrams
Reachability analysis (ALWAYS, NEVER, CONDITIONAL)
Postcondition validation (TAUTOLOGICAL, CONSTRAINING, INFEASIBLE)

Access: armiger.askalot.io

🎯 Targetor - Campaign Management¶

Build, launch, and track survey campaigns with demographic targeting.

Import respondents from user databases
Target specific demographic factors
Track campaign progress and response rates
Manage multiple concurrent campaigns

Access: targetor.askalot.io

📋 SirWay - Survey Execution¶

Lazy-evaluation based survey platform with dynamic form creation.

Functional flow navigation with precondition checking
Dynamic question rendering based on current state
Contradiction-free survey execution
Real-time state persistence

Access: sirway.askalot.io

📊 Balansor - Statistical Analysis¶

Post-stratification and statistical correction of survey responses.

Demographic rebalancing for representativeness
Data export to CSV, SPSS, R formats
Statistical analysis and reporting
Response weighting and correction

Access: balansor.askalot.io

QML: Domain-Specific Language for Surveys¶

QML (Questionnaire Markup Language) is a YAML-based domain-specific language designed specifically for AI-assisted questionnaire generation. QML uses declarative syntax with mathematical semantics, allowing AI to focus on question content while SMT solvers handle logical validation.

Why QML?¶

Current AI tools struggle with complex survey generation because Large Language Models face an overwhelming cognitive burden when attempting to simultaneously:

Generate semantically meaningful questions
Track conditional dependencies between questions
Ensure logical consistency across all paths
Maintain correct question ordering and flow
Avoid contradictions and unreachable dead ends

QML solves this by separating concerns: AI generates questions, SMT validates logic.

Mathematical Foundation¶

Every QML questionnaire is backed by formal mathematical validation:

Preconditions: - Boolean predicates determining when questions are reachable
Postconditions: - Boolean predicates constraining valid responses
SMT Solving: Z3 theorem prover validates all logical paths
Reachability Analysis: ALWAYS, NEVER, or CONDITIONAL reachability classification
Global Validity: Mathematical theorems ensure survey-wide logical consistency

See Theory Documentation for complete mathematical foundations.

Quantitative Analysis Philosophy¶

Askalot is fundamentally designed for quantitative analysis where all survey responses are represented as numerical values, enabling rigorous mathematical analysis and formal verification.

Integer-Based Outcomes¶

Every question type in QML produces integer outcomes:

Closed questions: Direct numerical encoding (Switch: 0/1, Radio: integer labels, Editbox: numeric input)
Multiple selection: Bit mask encoding allowing mathematical operations
Ranges: Szudzik pairing function encoding two integers as one

Open-Ended Question Handling¶

For open-ended questions, Askalot will transform free-text responses into numerical representations through:

AI-Powered Clustering: Automatic grouping of similar responses into thematic categories, each assigned a numerical code
Sentiment Analysis: AI analysis of text sentiment, converting qualitative responses into numerical sentiment scores
Hybrid Approaches: Combination of clustering and sentiment analysis for multi-dimensional numerical representation

Future Development

Open-ended question transformation is currently under development. The infrastructure supports integer-only outcomes, with text-to-number transformation planned for future releases.

This quantitative-first approach enables:

Mathematical validation of survey logic via SMT solvers
Statistical analysis with standard quantitative methods
Formal verification of consistency and reachability
Data export to statistical packages (SPSS, R, CSV)

Complete Workflow¶

Design → Create questionnaire in Armiger with AI + SMT validation
Campaign → Build and launch campaign in Targetor with demographic targeting
Execute → Collect responses in SirWay with lazy evaluation
Analyze → Correct and export results in Balansor with statistical rebalancing

Application Domains¶

While Askalot was designed for survey research, the combination of AI-assisted generation and formal mathematical validation makes it applicable to any domain requiring logically consistent, adaptive questioning:

Academic Research - Survey research with mathematical validation guarantees
Clinical Trials - ePRO/eCOA (electronic Patient-Reported Outcomes/Clinical Outcome Assessments) with adaptive questioning
Emergency Medical Triage - Diagnostic tools for first responders using adaptive questioning to identify critical conditions rapidly
Legal Case Discovery - Dynamic question generation for legal interrogation, efficiently navigating complex decision trees
Critical System Verification - Formally verified operational procedures for safety-critical systems (spacecraft pre-launch, nuclear reactor protocols, particle accelerator startup)

The formal verification ensures that critical steps cannot be bypassed, safety interlocks are properly enforced, and every possible state leads to appropriate verification steps—transforming traditional checklists into mathematically guaranteed protocols.

Getting Started¶

Quick Start

Complete workflow from design to analysis

Quick Start
Creating Surveys

Step-by-step guide to QML questionnaire creation

Creating Surveys
Mathematical Theory

Formal foundations and theorems

Theory
QML Syntax

Language reference and semantics

QML Syntax

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