How AI search helped AAA Auto boost engagement by 5% and simplify mobile navigation

AURES Holdings, one of the largest used car retailers in Central and Eastern Europe and operator of AAA Auto, needed to improve its mobile customer journey. Browsing thousands of cars across multiple parameters was a major pain point — especially for mobile users. To fix that, AURES teamed up with BigHub to deploy a lightning-fast AI search assistant powered by Microsoft Azure OpenAI.

5%

more search result clicks

600

people use daily

Client
AURES Holdings
Industry
Retail
Technology
Azure OpenAI, Vector search engine, GPT LLMs, Secure API layer, Mobile UX Integration
Delivered Solution
AI-powered search
Users
600 people daily
Implementation length
3 months
Discover your potential

"We are collaborating with BigHub on an intelligent search project, and from the very beginning, we have been impressed by their enthusiasm, expertise, and excellent client-oriented approach. What we value most is their proactivity and agile mindset."

Jakub Řehák
Data Science Manager, Aures Holdings

Key pain points

  • Complex filtering and navigation made it difficult for users to find the right vehicle.
  • Mobile users struggled the most, often abandoning their search due to frustration.
  • Traditional search engines returned too many irrelevant or poorly ranked results.
  • Manual product tagging and keyword matching were unsustainable at scale.
  • Customer experience was falling short of expectations in a highly competitive market.

BigHub’s solution

BigHub developed and deployed a GenAI-powered product search assistant tailored for AAA Auto's mobile website — designed specifically to handle natural language queries and accelerate product discovery.

What we delivered:

  • Smart vector-based search integrated with Azure OpenAI Services (GPT model family)
  • AI assistant that interprets vague, complex, or partial queries in real time
  • Seamless integration with AAA Auto’s internal product database and inventory systems
  • Fully mobile-optimized interface with near-instant (<1s) results

Why it works:
Unlike traditional search, the assistant understands real human queries like:

“SUV under 300,000 CZK, automatic transmission, not older than 2018” — and delivers exactly what the user wants, instantly.

Technology stack:
Azure OpenAI | Vector search engine | GPT LLMs | Secure API layer

Results

Engagement & business balue
  • +5% increase in search result clicks, indicating better matching and user satisfaction
  • 600+ users engage daily with the assistant in production
  • Noticeable drop in bounce rate for mobile product search sessions

Customer experience
  • Instant, relevant responses lead to higher satisfaction and longer session duration
  • Users find what they want faster, even with vague or misspelled inputs
  • Smooth, intuitive mobile experience boosts loyalty and reduces friction

Next steps

Following the successful implementation of the AI search, AURES Holdings is exploring:

  • Deeper personalization based on user history and intent
  • Voice search capabilities to further streamline the mobile journey
  • Expansion of the assistant to handle financing, insurance, and aftersales questions

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AI
0
min
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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.

AI
0
min
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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
0
min
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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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