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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.

Mathematical Foundation

We created a declarative model for questionnaire specification that fundamentally differs from traditional flow-based approaches. Rather than viewing questionnaires as sequential execution paths, we formalize them as collections of items with preconditions (Boolean predicates determining when questions appear) and postconditions (constraints on acceptable responses). This declarative paradigm, inspired by Hoare Logic, treats the questionnaire as a set of logical relationships where the actual flow emerges dynamically based on respondent answers. Our static analysis investigates all possible paths simultaneously through constraint satisfaction, reasoning about relationships between immutable states rather than simulating execution through mutable values. This enables validation of questionnaires containing hundreds of interconnected questions with complex conditional dependencies—a level of complexity that exceeds human comprehension and often makes traditional validation approaches computationally intractable.

See Theory Documentation for complete mathematical foundations.

Platform Architecture

Our research principles are implemented through four integrated services that work together to provide a mathematically-validated survey workflow from design to analysis:

🛠️ 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 interactive 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 path evaluation
  • Dynamic question rendering
  • Contradiction-free survey execution
  • Field Interview Mode for assisted interviews

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

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 (defined by scalar range or enumeration)
  • 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

  1. Design in Armiger → AI-assisted questionnaire creation with real-time validation
  2. Target in Targetor → Define campaigns and demographic segments
  3. Deploy in SirWay → Collect responses with dynamic flow navigation
  4. Analyze in Balansor → Statistical correction and data export

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.

Next Steps

  • Introduction

    Platform overview and core concepts

    Learn More

  • Quick Start

    Follow the complete workflow from design to analysis

    Start Now

  • Creating Surveys

    Learn QML syntax and best practices

    Guide

  • Mathematical Theory

    Understand the formal foundations

    Theory