AI Employee Engagement Analysis System Development

We design and deploy artificial intelligence systems: from prototype to production-ready solutions. Our team combines expertise in machine learning, data engineering and MLOps to make AI work not in the lab, but in real business.
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AI Employee Engagement Analysis System Development
Medium
~1-2 weeks
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Development of AI System for Employee Engagement Analysis

Employee engagement is a predictor of productivity and attrition. An AI system analyzes multiple signals and provides early warning about declining engagement before employees start looking for new jobs.

Signal Sources

Direct (surveys):

  • Pulse surveys (3–5 questions weekly)
  • eNPS (Employee Net Promoter Score) quarterly
  • 360-degree feedback
  • Stay interviews / Exit interviews

Indirect (behavioral):

  • Activity in corporate systems (login frequency, tool usage)
  • Participation in corporate activities
  • Use of training platforms
  • Working time patterns (overtime, early departures)

Analysis of Survey Text Responses

Open-ended survey questions are the richest source. AI analyzes:

  • Sentiment of text responses
  • Mentioned topics (management, growth, salary, team, workload)
  • Sentiment changes between periods
def analyze_survey_responses(responses: list[SurveyResponse]) -> EngagementAnalysis:
    topics = extract_topics(responses)
    sentiment_by_topic = {
        topic: analyze_sentiment([r for r in responses if topic in r.topics])
        for topic in topics
    }
    return EngagementAnalysis(
        overall_score=calculate_engagement_score(responses),
        sentiment_by_topic=sentiment_by_topic,
        risk_employees=identify_at_risk(responses),
        top_positive_themes=get_top_themes(sentiment_by_topic, sentiment="positive"),
        top_negative_themes=get_top_themes(sentiment_by_topic, sentiment="negative"),
        recommended_actions=generate_recommendations(sentiment_by_topic),
    )

Churn Prediction

Churn model based on a combination of signals:

  • Declining eNPS over the last 2 quarters
  • Declining activity in corporate systems
  • Negative sentiment in surveys
  • No promotions longer than X months
  • Increase in days-off and sick days

Predict churn probability → alert manager: "Ivan may leave the company — we recommend a 1:1 meeting to identify the reason".

Privacy

Individual data is shown only if N ≥ 5 (aggregation). HR and managers see aggregated data for their team. Individual risk assessments — only HR director with explicit employee consent for such analysis.