Skip to content

AI-Assisted Features Overview

Askalot integrates AI assistance throughout the survey research workflow, helping researchers design, execute, and analyze surveys more efficiently while maintaining formal mathematical rigor.

The AI-Assisted Workflow

AI capabilities are embedded at each stage of the survey lifecycle:

flowchart LR
    subgraph design["1. Design"]
        d1[Research Brief] --> d2[Document Analysis]
        d2 --> d3[QML Generation]
        d3 --> d4[SMT Validation]
    end

    subgraph campaign["2. Campaign"]
        c1[Wizard Guidance] --> c2[Strategy Design]
        c2 --> c3[Pool Generation]
        c3 --> c4[Launch]
    end

    subgraph execute["3. Execute"]
        e1[Real Respondents] --> e2[Survey Completion]
        e3[Persona Simulation] --> e2
    end

    subgraph analyze["4. Analyze"]
        a1[Data Collection] --> a2[Pattern Detection]
        a2 --> a3[Insight Generation]
        a3 --> a4[Report Creation]
    end

    design --> campaign
    campaign --> execute
    execute --> analyze

Feature Summary

Feature Stage Purpose Key Capabilities
Questionnaire Generation Design Transform research briefs into validated QML Document analysis, question generation, SMT validation loop
Campaign Management Campaign Guide campaign setup and execution Wizard flow, chat interface, sampling strategy design
Response Generation Execute Simulate campaigns with synthetic data Persona profiles, realistic responses, pipeline testing
Result Analysis Analyze Generate insights from survey data Pattern detection, visualization, report generation

AI-Assisted Questionnaire Generation

Stage: Design | Tool: Armiger

Transform research documents and briefs into formally verified QML questionnaires.

How It Works

  1. Document ingestion - Upload research materials (PDFs, specs, regulations)
  2. Concept extraction - AI identifies measurable dimensions
  3. Chapter organization - Structure questionnaire for large projects
  4. QML generation - Create questions with appropriate controls
  5. SMT validation - Formal verification ensures logical consistency
  6. Iterative refinement - AI fixes validation errors automatically

Use Cases

  • Converting compliance frameworks into assessment questionnaires
  • Transforming research papers into survey instruments
  • Building due diligence checklists from policy documents
  • Creating health assessments from clinical guidelines

Learn more

AI-Assisted Campaign Management

Stage: Campaign | Tool: Targetor

Two complementary interfaces for campaign setup and management.

Campaign Wizard

Guided multi-step flow for beginners:

  1. Campaign Design - Name, questionnaire selection, mode
  2. Target Audience - Demographics, sample size
  3. Sampling Strategy - Stratification factors, distributions
  4. Respondent Pool - Generate and preview pool quality
  5. Review & Launch - Summary, validation, deployment

Chat Interface

On-demand conversational interface for power users:

  • "Merge these two pools to get more respondents"
  • "Show me which interviewers have the lowest completion rates"
  • "Create a new pool with only respondents aged 25-34"
  • "Compare response rates between campaign versions"

AI-Assisted Sampling Strategy

The AI helps design representative samples by:

  • Recommending stratification factors based on research topic
  • Proposing target distributions from population data
  • Analyzing respondent database coverage
  • Warning about potential sampling biases

Learn more

Agentic Response Generation

Stage: Execute | Tool: SirWay + Simulation Engine

Simulate complete campaigns with AI-generated responses for testing and validation.

Simulation Modes

Mode Description Use Case
Single Survey Complete one survey with a specific persona Questionnaire flow testing
Campaign Simulation Run full three-phase campaign lifecycle Pipeline validation
Mass Fill Bulk synthetic data generation Analysis pipeline testing

Persona Profiles

Built-in profiles with demographic and behavioral traits:

  • Young Professional - Tech-savvy, time-conscious, brief responses
  • Family Oriented - Family-focused decisions, detailed responses
  • Retired Senior - Traditional values, thorough responses
  • High Earner - Quality-focused, premium preferences
  • Custom - Define your own demographic/behavioral mix

Three-Phase Flow

Phase 1: Preparation    →    Phase 2: Execution    →    Phase 3: Analysis
(Project, Campaign,          (Persona-based              (Bronze, Silver,
Strategy, Pool, Surveys)     Survey Completion)          Gold Datasets)

Learn more

AI-Assisted Result Analysis

Stage: Analyze | Tool: Balansor

Generate insights from survey data through conversational interfaces.

Key Capabilities

Capability Description
Automated Insight Generation AI identifies significant patterns and trends
Natural Language Queries Ask questions about your data conversationally
Visualization Recommendations AI suggests appropriate charts and graphs
Statistical Interpretation Plain-language explanations of statistical results
Report Generation Automated creation of analysis summaries

Example Interactions

User: "What are the key drivers of customer satisfaction?"

AI: Based on regression analysis, the top 3 drivers are:
    1. Product quality (β=0.42, p<0.001)
    2. Customer service responsiveness (β=0.31, p<0.001)
    3. Value for money (β=0.24, p<0.01)

    These factors explain 67% of satisfaction variance.
    Would you like me to generate a visualization?

Learn more

Integration Across Stages

The AI features work together seamlessly:

Document → Data Flow

Research Documents
    ↓ [AI Questionnaire Generation]
QML Questionnaire
    ↓ [AI Campaign Management]
Campaign with Sampling Strategy
    ↓ [Agentic Response Generation - optional]
Survey Responses (real or simulated)
    ↓ [AI Result Analysis]
Insights and Reports

Shared Context

AI assistants share context across the workflow:

  • Questionnaire structure informs sampling strategy recommendations
  • Campaign demographics guide persona selection for simulation
  • Survey responses feed directly into analysis tools
  • Insights can trigger questionnaire refinements (closing the loop)

Provider Flexibility

Organizations can configure their preferred AI provider:

Provider Models Best For
Anthropic Claude Sonnet, Claude Haiku Default, best reasoning
AWS Bedrock Claude, Llama Enterprise deployments
OpenAI GPT-4, GPT-4o Alternative provider

Customers can bring their own API credentials—no vendor lock-in.

Getting Started