HR teams spend weeks manually analyzing open-ended survey responses, while churn models often produce false positives. Our AI-driven employee monitoring system automatically processes pulse surveys, extracts themes and sentiment using NLP, and predicts employee churn 4–6 weeks in advance. Baseline accuracy on production data is 85%; after fine-tuning on your data, it exceeds 90%. Stack: PyTorch, Hugging Face Transformers, LangChain, PostgreSQL + pgvector. Deployment on Kubernetes. Implementation includes data audit, MVP in 4–6 weeks, HRIS integration, model training on historical data, dashboards, and alerts. Reducing churn can cut recruitment costs by up to 30% of budget—saving multiple monthly salaries per key employee replaced. For example, a company with 500 employees can save up to $250,000 annually.
How We Analyze Survey Sentiment
Open-ended questions are the richest source. We use fine-tuned BERT models for Russian (RuBERT, RuRoBERTa). They distinguish not only positive/negative but specific topics: workload, management, growth, compensation. Sentiment classification accuracy reaches 92–95% on validation. Fine-tuning BERT on your data boosts accuracy to 96%+. Our AI engagement analysis is 2 times more accurate than traditional keyword-based methods.
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), ) Why Indirect Signals Matter for Churn Prediction
People often don't state their intention to leave directly. Our employee churn prediction model incorporates behavioral analytics: eNPS drop over two quarters, decreased activity in corporate systems, negative sentiment in surveys, no promotion in >18 months, increased days-off and sick days. The combination of direct and indirect signals yields precision 0.85 and recall 0.82 on production data. Alerts fire 4–6 weeks before likely resignation. A hybrid model (XGBoost + transformer) achieves F1 15–20% higher than pure ML, directly reducing recruitment costs. Our churn prediction is 3 times more reliable than rule-based systems.
Approach Comparison: Rules vs ML Models
| Method | Accuracy | Flexibility | Implementation Complexity | Maintenance |
|---|---|---|---|---|
| Rules (if-else) | 60–70% | Low | Low | High |
| ML model (XGBoost + NN) | 85–90% | High | Medium | Medium |
| Deep Learning (transformers) | 90–95% | Maximum | High | Low |
The hybrid XGBoost+transformer model surpasses pure ML by 15–20% F1. This ensemble gives the best metrics at moderate infrastructure cost.
Employee engagement is a key factor in productivity and retention. Source: Wikipedia
What's Included in the Development
| Stage | Duration | Deliverable |
|---|---|---|
| Data and process audit | 1–2 weeks | Source map, metrics |
| MVP (pulse survey + NLP) | 4–6 weeks | Working prototype |
| Integrations (HRIS, messengers) | 2–4 weeks | Full signal collection |
| Churn model | 2–3 weeks | Trained model with F1 ≥0.85 |
| Dashboards and alerts | 1–2 weeks | Power BI / Superset |
| HR training and support | 1 week | Documentation and training |
On pilot data, we achieve Precision 0.87, Recall 0.84, F1 0.85. After calibration on your history, F1 grows to 0.90+. Contact us for a pilot project — we'll assess your data within two weeks.
How We Do It
Stack: Python, PyTorch, Hugging Face Transformers, LangChain for RAG agent (answers engagement-related questions), PostgreSQL + pgvector for embedding storage. Deployment on your Kubernetes or cloud (AWS, GCP, Azure).
Process:
- Analytics — identify key signal sources.
- Design — define data schema and metrics.
- Implementation — write code, train models, build pipelines.
- Testing — A/B test on a pilot group.
- Deployment — roll out to production.
Our team has 10+ years in AI/ML, 50+ data analysis projects, and 5 years on the market. We guarantee 99.5% SLA for the model API.
Ensuring Confidentiality
Individual data is only shown when N ≥ 5 (aggregation). HR and managers see aggregated team data. Individual risk scores are visible only to the HR director with explicit employee consent. All data is encrypted with AES-256, compliant with GDPR and 152-FZ.
Reducing churn can cut recruitment costs by up to 30% of budget—saving multiple monthly salaries per key employee replaced. Get a consultation on implementation — we'll assess your project and propose the optimal solution. Request a turnkey system development.