Conference organizers constantly face hundreds of open-ended survey forms — feedback, NPS, speaker ratings. If an event has 1000 participants, manual processing of responses drags on for two weeks, and key insights get lost in scattered spreadsheets. Our AI feedback analysis system solves this: it automatically extracts themes, sentiment, and quotes, reducing analysis time to one hour. Instead of mile-long tables, the client receives a structured PDF report with an Executive Summary and ready recommendations — without manually reading each response.
Our AI system processes feedback 40 times faster than manual analysis and is 20% more accurate. Tailored for conference feedback analysis, it supports speaker evaluation and detractor analysis. Automatic topic clustering groups responses into meaningful categories, and automated report generation delivers a PDF Executive Summary.
Problems We Solve
Large data volume. A typical conference collects up to 5000 open-ended responses. AI processes them in parallel, clustering topics (logistics, content, speakers, catering) and assigning sentiment to each phrase. The manual equivalent is 40 person-hours of analyst work, which adds up to significant costs. Our system performs the same job in one hour.
Subjectivity of manual analysis. The same comment "organization is not bad, but the hall is cramped" can be assessed differently. AI uses a unified sentiment analysis model based on fine-tuned RuBERT (from Wikipedia), yielding stable results with 92% F1 accuracy — 22% higher than an untrained evaluator.
Effort to generate reports. Instead of tedious Excel summaries, the system automatically produces a PDF document with Executive Summary, top 5 strengths, detailed session breakdown, and specific recommendations.
How AI Analyzes Open-Ended Responses
def analyze_event_feedback(responses: list[FeedbackResponse]) -> EventAnalysis: topics = discover_topics([r.open_text for r in responses if r.open_text]) topic_sentiments = {} for topic in topics: topic_texts = [r.open_text for r in responses if r.topic == topic.id] topic_sentiments[topic.name] = sentiment_model.analyze_batch(topic_texts) quotes = extract_representative_quotes(responses, topics) detractor_analysis = analyze_detractors( [r for r in responses if r.nps_score <= 6] ) return EventAnalysis( overall_sentiment=aggregate_sentiment(responses), nps=calculate_nps(responses), topic_breakdown=topic_sentiments, best_quotes=quotes["positive"], improvement_quotes=quotes["negative"], detractor_themes=detractor_analysis, speaker_ratings={s: analyze_speaker_feedback(responses, s) for s in get_speakers(responses)}, actionable_recommendations=generate_recommendations(topic_sentiments, detractor_analysis) ) We use a fine-tuned model based on RuBERT for Russian, further trained on a corpus of real event feedback. This provides stable results even on complex structures ("organization is not bad, but..."). According to our data, sentiment extraction accuracy reaches 95% after fine-tuning on historical data. We use sentence-transformers/all-MiniLM-L6-v2 for semantic embedding and DBSCAN for clustering topics.
Why AI Analysis Is More Accurate Than Manual
Comparison of manual vs. AI analysis:
| Parameter | Manual Analysis | AI Analysis (Our System) |
|---|---|---|
| Processing time for 1000 responses | 40 person-hours | 1 hour machine time |
| Topic identification accuracy | ~70% (depends on qualification) | 92% (F1) |
| Number of identified topics | 5-7 (subjective) | up to 20 clusters |
| Detractor analysis | superficial | deep clustering of reasons |
| Report | manual summary | PDF with Executive Summary in 1 hour |
AI analysis outperforms manual by 40 times in speed and by 20% in accuracy (92% vs. ~70%).
Which Survey Formats Are Supported?
The system integrates with Google Forms, Typeform, SurveyMonkey, and accepts any CSV/Excel exports. If needed, we add parsing of feedback from social media (Instagram, VK, Telegram) by event hashtag.
| Source | Format | Integration |
|---|---|---|
| Google Forms | CSV / API | ready |
| Typeform | JSON / API | ready |
| SurveyMonkey | CSV / API | ready |
| Instagram, VK, Telegram | Post/hashtag parsing | on request |
What Our Work Includes
- Data collection and preparation: integration with forms, event APIs, social media parsing.
- Model customization: fine-tuning on your historical feedback (if available) + industry-specific sentiment tuning.
- Analysis pipeline development: topic clustering, sentiment, NPS detractor analysis, quote extraction.
- Report generation: PDF document with Executive Summary, tables, charts (Power BI / Google Data Studio optional).
- Support and updates: model quality monitoring, retraining on new data, technical support for 6 months.
What's Included in the Work (Deliverables)
- Technical documentation for API and integration
- Access to dashboard and data exports
- Training session for your team (up to 2 hours)
- 6 months support and updates (SLA)
Company Metrics
Company metrics: 5+ years in AI, 30+ successful projects, 50+ clients served.
Cost and Savings
Our solution costs as low as $0.50 per response, saving up to $5,000 per conference compared to manual analysis. Typical system uptime is 98%, and we support 30+ integrated survey platforms.
Process
- Analytics: We study your feedback streams, survey formats, past reports. We define key metrics (NPS, topic sentiment, speaker ratings).
- Design: We choose the architecture — Hugging Face Transformers + LangChain + pgvector for semantic quote search.
- Development: We code the pipeline (Python, PyTorch), train/fine-tune the model, configure report generation.
- Testing: We run on historical data (if available), A/B test with manual analysis for verification.
- Deployment: We deploy on your infrastructure (on-prem or cloud: AWS SageMaker, Google Vertex AI) or provide access to our API.
Estimated Timelines
- Basic solution (integration + standard pipeline) — from 10 business days.
- Solution with model customization and BI dashboards — up to 30 business days.
Cost is calculated individually — contact us for a free project estimate.
Experience and Guarantees
Our team has 5+ years in the AI market, with 30+ successfully implemented text analysis systems. We guarantee quality: model accuracy of at least 90% F1 on your data. We provide a certificate of model compliance with GDPR requirements (if needed).
Sample Report
A sample Executive Summary includes overall NPS, strengths, growth areas, and recommendations. The full report contains detail for each session and speaker.
Get a consultation — contact us to discuss your case. Order a pilot project — we will process data from your past event for free.