AI assistant PROKOOP, Kooperativa’s sales team cuts down
 on admin and focuses more on clients

Kooperativa, a major player in Central Europe’s insurance market and part of the Vienna Insurance Group, needed a way to reduce administrative strain on its sales and operations teams. Its employees were losing valuable time searching for internal information, answering routine inquiries, and handling repetitive requests. That all changed with the introduction of PROKOOP, a custom AI assistant designed by BigHub and built securely on Azure.

5000

conversations per month

8%

time saved for managers

94%

of users appreciate faster access to information

44%

adoption rate after 3 months in production
Client
Kooperativa pojišťovna
Industry
Insurance
Technology
Azure Cloud, Data Lake, Terraform, ADLS Gen2, vector databases, LangChain, LangGraph, Entra ID, multiple LLM models including OpenAI models, LLM agents, RAG agentic framework, OCR pipeline​.
Delivered Solution
AI assistant providing sales representatives support 24/7.
Users
8000
Implementation length
3 months
Discover your potential

“What used to take 10 minutes of searching now takes 10 seconds. When our team started two years ago, we knew it wouldn’t be easy. But now, seeing PROKOOP in action, hearing the positive feedback, and holding the VIG XELERATE award in my hands — I know it was all worth it.”

Zuzana Šlapalová
Internal Sales Manager, Kooperativa

Key pain points

  • Sales and support staff spent too much time manually searching internal systems.
  • Information retrieval was inconsistent and often inaccurate.
  • Administrative overhead reduced face-to-face time with clients.
  • The volume of repetitive internal questions placed pressure on central methodologist and product teams.
  • Kooperativa needed a secure, smart solution to support client-facing employees and optimize internal processes.

BigHub’s solution

BigHub delivered a tailored enterprise AI solution built on the Microsoft Azure ecosystem, combining modern LLM agents with internal data security, infrastructure, and scalability. Two core components were implemented:

1. AI helpdesk for sales representatives
An always-on AI assistant embedded into internal systems, capable of:

  • Answering queries about insurance products and claims handling
  • Comparing Kooperativa’s offerings with those of competitors
  • Pulling information from internal data sources and external websites
  • Reducing reliance on human specialists for basic queries
  • Providing 24/7 support in a secure, monitored environment

2. AI Chatbot for branches
A conversational interface deployed across branch offices, helping staff:

  • Resolve questions related to contract terms, claims, and offers
  • Navigate complex documentation quickly using RAG (Retrieval-Augmented Generation)
  • Search through structured and unstructured data in real-time
  • Operate seamlessly on Azure using LangChain, LangGraph, and multiple LLMs

Technology stack:
Azure Cloud | Azure Data Lake | ADLS Gen2 | Terraform | Vector databases | Entra ID | LangChain | LangGraph | Multiple LLMs incl. OpenAI | RAG agentic framework | OCR pipeline

Results

Business efficiency
  • 5,000+ conversations/month across departments
  • 94% of users say the assistant provides faster, more accurate information
  • The AI assistant operates 24/7 and reduces response times dramatically

ROI & adoption
  • Pays for itself in the first year of operation
  • 44% adoption rate after just 3 months
  • Clear cost-effectiveness thanks to low running costs and increased productivity

Time savings & internal impact
  • 8% of managers’ time saved by reducing repetitive questions
  • 1% time saved for sales reps, freeing them to focus more on clients
  • Methodology and product teams significantly unburdened

Subjective improvements
  • Higher satisfaction among frontline employees due to easier access to information
  • Improved internal knowledge sharing
  • Increased consistency in responses, improving client communication

Next steps

Kooperativa is now working with BigHub to expand the assistant’s capabilities further, including:

  • Onboarding new employees through AI-led knowledge sharing
  • Enabling voice-first interactions for even faster internal support
  • Integrating multimodal inputs (e.g., documents, forms) for richer automation

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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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