AI-powered VR solution makes ČPP presentation skill development 3x faster

ČPP, a major insurance provider and part of the Vienna Insurance Group, needed a more efficient way to help its employees develop presentation skills. Traditional training lacked real-time feedback and failed to provide measurable progress. In collaboration with BigHub and YORD, ČPP introduced an AI-powered VR platform that delivers personalized analytics, immersive training, and rapid improvements in performance.

faster improvement in presentation skills vs. traditional training (pilot data)

100%

of users reported increased confidence and engagement after VR training

Real-time tracking

of speech clarity, body language, and audience engagement

Client
Česká podnikatelská pojišťovna
Industry
Insurance
Technology
Azure OpenAI, Azure Speech Studio, Azure Storage Account, Container App Jobs
Delivered Solution
AI infrastructure and cloud backend for a new VR training platform
Users
Implementation length
3 months
Discover your potential

"We consider BigHub to be one of the top providers of AI and data solutions in the Czech Republic, which they consistently demonstrate through the projects we carry out together. They not only help us deliver these projects but also actively co-create them with us and proactively seek out opportunities to bring business value."

Vít Fadrhonc
Director of AI department, Kooperativa

Key pain points

  • Lack of immersion and real-world simulation in traditional training methods.
  • No measurable feedback for tracking improvement.
  • Limited scalability and repeatability of instructor-led sessions.
  • Professionals needed to improve clarity, confidence, and non-verbal delivery under realistic conditions.
  • Difficult to personalize training for different roles or situations.

BigHub’s solution

BigHub developed the AI infrastructure and cloud backend for a new VR training platform tailored to presentation skills. The system collects and processes motion and voice data in Microsoft Azure, providing dynamic feedback to each user.

Key solution components:

  • Real-time collection and processing of eye movement, gestures, posture, and speech using a VR headset.
  • Automatic analysis via AI models: detecting filler words, tone of voice, and body language quality.
  • Personalized feedback reports for each session — identifying weak areas and recommending targeted improvements.
  • Integration of Azure services, including Data Lake, Terraform, ADLS Gen2, Entra ID, and LangChain-based logic.
  • Personalized report for HR and trainers to track individual and team progress over time.
  • Designed to scale across departments and roles.

While YORD focused on the VR interface and visual layer, BigHub ensured all data processing, AI logic, cloud infrastructure, and training logic were reliable, secure, and enterprise-ready.

Solution outcomes

Faster skill development

  • Users demonstrated up to 3× faster improvement in public speaking and presenting, compared to traditional classroom methods (based on pilot data).

Increased confidence

  • 100% of users reported higher self-confidence after using the platform.

Data-driven feedback

  • Measured impact in clarity of speech, eye contact, posture, and audience engagement — tracked with real-time AI analysis.

Scalable and reusable

  • Platform can be adapted to simulate various business situations (e.g., internal updates, sales pitches, public speaking).

Next steps

  • Expansion to other departments and roles within ČPP.
  • Development of industry-specific scenarios (e.g., customer negotiations, onboarding).
  • Potential integration with wearable devices and multi-user VR sessions for collaborative learning.
  • Broader rollout across Vienna Insurance Group subsidiaries.
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AI Agents: What They Are and What They Mean for Your Business

Artificial intelligence is experiencing another major wave — this time in the form of so-called AI agents. But what exactly are they, why is everyone talking about them, and how can they benefit your business?
🧠 What Are AI Agents?

An AI agent is a digital assistant capable of independently executing complex tasks based on a specific goal. It’s more than just a chatbot answering questions. Modern AI agents can:

  • Plan multiple steps ahead
  • Call APIs, work with data, create content, or search for information
  • Adapt their behavior based on context, user, or business goals
  • Work asynchronously and handle multiple tasks simultaneously

In short, an AI agent functions like a virtual employee — handling tasks dynamically, like a human, but faster, cheaper, and 24/7.

Why Are AI Agents Trending Right Now?
  • Advancements in large language models (LLMs) like GPT-4, Claude, and Mistral allow agents to better understand and generate natural language.
  • Automation is becoming goal-driven — instead of saying “write a script,” you can say “find the best candidates for this job.”
  • Companies want to scale without increasing costs — AI agents can handle both routine and analytical tasks.
  • Productivity and personalization are top priorities — AI agents enable both in real time.

What Do AI Agents Bring to Businesses?

✅ 1. Save Time and Costs

Unlike traditional automation focused on isolated tasks, AI agents can manage entire workflows. In e-commerce, for example, they can:

  • Help choose the right product
  • Recommend accessories
  • Add items to the cart
  • Handle complaints or returns

✅ 2. Boost Conversions and Loyalty

AI agents personalize conversations, learn from interactions, and respond more precisely to customer needs.

3. Team Relief and Scalability

Instead of manually handling inquiries or data, the agent works nonstop — error-free and without the need to hire more people.

4. Smarter Decision-Making

Internal agents can assist with competitive analysis, report generation, content creation, or demand forecasting.

AI Agents in Practice
Scenario Example Use Case
Customer Support Answering questions, tracking orders, handling complaints
Marketing Planning campaigns, building segments, creating copy and A/B tests
Sales Generating leads, preparing proposals, follow-ups
Logistics Tracking inventory, planning deliveries, monitoring delays
HR Screening CVs, replying to candidates, onboarding
AI Agent vs. Traditional Chatbot: What's the Difference?
Feature Traditional Chatbot AI Agent
Responses Predefined scripts Flexible, contextual
Memory None or short-term Long-term, adaptive
Tasks Simple answers Multi-step workflows
Integration Limited Connects to CRM, ERP, e-shop
Autonomy Low High – plans and decides

What Does This Mean for Your Business?

Companies that implement AI agents today gain an edge — not just in efficiency, but in customer experience. In a world where “fast replies” are no longer enough, AI agents bring context, intelligence, and action — exactly what the modern customer expects.

What’s Next?

AI agents are quickly evolving from assistants to full digital colleagues. Soon, it won’t be unusual to have an “AI teammate” handling tasks, collaborating with your team, and helping your business grow.

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GenAI Is Not the Only Type of AI: What Every Business Leader Should Know

Generative AI (GenAI) is dominating headlines — from ChatGPT to image generators and copilots in business tools. But while it's powerful, GenAI is only one type of artificial intelligence. And in many real-world business cases, it's not the most suitable one. To make smart AI decisions, you need to understand that AI comes in multiple forms, each designed for specific goals.
🧠 What Is Generative AI (GenAI)?

Generative AI focuses on creating content — text, images, video, or code — by using large language models (LLMs) trained on huge datasets.

Typical use cases:

  • Writing emails, articles, product descriptions
  • Generating graphics and images
  • Creating code or marketing copy
  • Customer support via AI-powered chat

But despite its capabilities, GenAI isn't a one-size-fits-all solution.

What Other Types of AI Exist?
✅ 1. Analytical AI

This type of AI focuses on analyzing data, identifying patterns, and making predictions. It doesn't generate content but provides insights and decisions based on logic and data.

Use cases:

  • Predicting customer churn or lifetime value
  • Credit risk scoring
  • Fraud detection
  • Customer segmentation
✅ 2. Optimization AI

Rather than analyzing or generating, this AI finds the best possible solution based on a defined goal or constraint.

Use cases:

  • Logistics and transportation planning
  • Dynamic pricing
  • Manufacturing and workforce scheduling
✅ 3. Symbolic AI (Rule-Based Systems)

This older but still relevant form of AI uses logic-based rules and decision trees. It is explainable, auditable, and reliable — especially in regulated environments.

Use cases:

  • Legal or medical expert systems
  • Regulatory compliance
  • Automated decision-making in banking or insurance
✅ 4. Reinforcement Learning

This AI learns by trial and error in dynamic environments. It’s used when the system needs to adapt based on feedback and outcomes.

Use cases:

  • Autonomous vehicles
  • Robotics
  • Complex process automation

When Should (or Shouldn’t) You Use GenAI?

What Does This Mean for Your Business?

If you're only using GenAI, you might be missing out on significant potential. The real value lies in combining AI types.

Example:

  • Use Analytical AI to segment your customers.
  • Use GenAI to generate personalized emails for each segment.
  • Use Optimization AI to time and target campaigns efficiently.

This multi-layered approach delivers better ROI, reliability, and strategic depth.

Summary: GenAI ≠ All of AI
AI Type What It Does Best For
Generative AI Creates content Marketing, support, creativity
Analytical AI Makes predictions and scores Finance, risk, analytics
Optimization AI Finds best outcomes Logistics, pricing, planning
Symbolic AI Follows clear rules Compliance, legal, expert systems
Data
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Why Clean Data Matters (And What It Actually Means to “Have Data in Order”)

Everyone talks about how important it is to “have your data in order.” But what does that really mean? How can you tell if your business has a data problem? And why is it so critical—not just for IT teams, but for marketing, operations, product development, or finance?
🧠 What does it mean to have your data in order?

It’s more than storing files in the cloud or keeping spreadsheets neat.

When your data is “in order,” it means that:

  • It’s accessible – people across the company can access it easily and securely
  • It’s high-quality – data is clean, up to date, and consistent
  • It has context – you know where the data came from, how it was created, and what it represents
  • It’s connected – systems talk to each other, there are no data silos
  • It’s actionable – the data supports decision-making, automation, and business goals

In short: Clean data = trustworthy and usable data.

How can you tell if your data isn’t in order?

Here are some common red flags:

Symptom in the company Possible data issue
Different teams report different numbers Inconsistent data sources
Heavy reliance on Excel spreadsheets No integration or centralized platform
Sales and marketing teams don’t align Data silos or lack of system connections
People don’t trust internal reports Poor data quality or visibility
Struggling to personalize customer communication Incomplete or dirty data

These challenges are common—startups, scale-ups, and enterprises all face them at some point.

What are the risks of messy or low-quality data?
Slower decisions

Without confidence in your data, decisions are delayed—or based on gut feeling instead of facts.

Wasted resources

Analysts spend most of their time cleaning and merging data, rather than generating value.

Poor customer experiences

Outdated or fragmented data means poor personalization, errors in communication, or missed opportunities.

Blocked AI and automation efforts

You can’t build predictive models or automation without structured, clean data.

What does it take to “clean up your data”?
Data audit

Map out your data sources, flows, and responsibilities.

Data integration

Connect systems like CRM, ERP, e‑shop, marketing platforms into a unified view.

Implement a modern data platform

Build a central, scalable place to store and manage data (e.g., a data warehouse with BI tools).

Ensure data quality

Remove duplicates, validate formats, ensure consistency.

Define governance

Set clear responsibilities for data ownership, access, and documentation.

What’s the business impact?

✅ A single source of truth

✅ Smarter, faster decision-making

✅ Improved collaboration between departments

✅ Stronger foundations for AI, automation, and personalization

✅ More trust in your reporting and forecasts

Final thoughts: Data isn’t just a cost. It’s an asset.

Many companies treat data as a back-office IT issue. But in reality, data is one of your most valuable business assets—and without having it in order, you can’t grow, digitize, or deliver personalized experiences.

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