AI-powered data engineering is what can make your business applications truly smart.The reason lies in the way AI models rely on a constant flow of data to learn and improve – but that data isn’t much use if it’s unstructured or hard to access. That’s when the magic happens: the moment data engineering services and AI tools join forces.
If you are curious about how AI will automate data engineering, read on to learn about best practices for implementing AI-driven data solutions, and real-world success stories from CHI Software.
Are you eager to know how your business can change with data engineering?
Data teams often spend 80% of their time cleaning data – AI can take over much of these tasks, giving teams more time to explore meaningful insights;
By leveraging AI in data engineering, data processes improve over time, allowing your data infrastructure to grow with your business;
Automating data workflows with AI boosts productivity by twofold, allowing your team to focus on strategic tasks.
Data Engineering with AI: How the Power Duo Works
Artificial intelligence and data engineering are not just separate elements that are nice to combine; they are complementary parts of a well-functioning system. If data is the fuel that powers AI, then data engineering is the injector that delivers that fuel to keep your business running at full speed. Not only that, AI is also transforming how data engineering works.
AI-driven data engineering transforms traditional processes, such as data collection, cleansing, or pipeline development.
Any artificial intelligence tool – whether image recognition or chatbot development – depends on high-quality, well-structured data. Data engineers create pipelines that collect, clean, and organize raw data, but with AI in data engineering, they can now address challenges of the traditional methods and improve the process:
Smart data collection: AI-powered systems can automatically extract data from various sources – websites, databases, and IoT devices. After that, algorithms filter all the information and keep only the most valuable data;
AI-enhanced data storage: There are many types of data warehouses – and AI can help managedata workflows no matter what kind of storage a company uses. For example, AI can automatically categorize all the data in your warehouse, adding a completely new dimension to work with;
Intelligent data processing and cleansing: Cleansing and organizing data can take a significant amount of time. But instead of a human spending hours to comb through the data, an AI process can go through the data to find missing values, detect errors, validate inconsistencies, and even correct them without human involvement;
Optimized data pipelines: AI-driven data engineering helps monitor and adjust data flows in real time to ensure that systems are not overloaded and data is available whenever it is needed;
Data integration: In many organizations, data may be stored in separate systems – finance has its own database, while marketing has another. Data integration tools based on artificial intelligence can easily combine these disparate data, determine the relationships, and create a single database that gives a complete and real-time picture of the company’s work processes;
Continuous improvement: Machine learning in data engineering along with AI is a reliable coach for your data pipelines, as it helps them grow continuously.
6 Benefits of AI-Driven Data Engineering for Your Business
So, what benefits can your business expect after implementingautomated data engineering with AI? Here are some of the tangible advantages:
AI and data engineering are a powerful combo offering a lot of advantages to businesses of any size.
1. AI Does the Heavy Lifting While Your Team Focuses on What Matters Most
Data teams spend 80% of their time cleaning and organizing data, before even getting a chance to analyze it. Artificial intelligence can quickly remedy this issue through automated duplicate or error detection and classification. As a result, your team can focus on trustworthy insights, and:
Identify buying patterns, preferences, and behaviors, allowing your business to personalize marketing campaigns to the maximum extent possible;
Make faster business decisions using AI-powered reports;
Proactively improve security by analyzing your company’s historical data with AI;
Optimize most of the company’s processes. AI-powered analytics can identify inefficiencies and suggest ways to improve supply chain management or resource allocation processes.
Our experience has shown that using AI-powered data engineering leads to 2x faster data processing, accelerating report generation and time-to-insight.
2. Your Business Decisions Become More Reliable
AI for data engineering ensures that the information you rely on is up-to-date. How it works: AI tools continuously check the information that you have on hand and correct inconsistencies before they cause costly errors. Data quality and cleaning tools, along with generative AI for data engineering, can create intelligent predictions and fill in any missing data.
3. Easy Scaling Even with Vast Amounts of Data
AI data automation ensures that your data infrastructure automatically scales with demand, all while maintaining data quality and security standards. Smooth scalability is possible by automatically moving data to cost-effective locations, distributing workloads, and anticipating traffic spikes.
4. Your Data Works Harmoniously Across All Departments
Siloed data is a business killer, so if your departments use different tools that don’t interact with each other, AI integration is a must-have. Automated data engineering with AI can break down isolated data, create a single data ecosystem, and provide a single source for all decision-making.
5. Predicting the Future Becomes Simpler
By anticipating and preventing problems, you can avoid the need for emergency interventions. AI detects early warning signs of fraud or customer churn, intelligently allocating resources, and predicting customer behavior.
6. AI Data Engineering as a Competitive Advantage
Companies that rely on data engineering automation with AI get information faster, and can make smarter decisions with a data system that evolves with their needs – all of which make a big difference.Experience faster insights and reduced costs – reach out to us to discover AI-driven solutions!
Best Practices for Implementing Data Engineering with AI
Implementing data engineering automation with AI is complex, but this step-by-step approach will help you achieve your goals faster.
1. Start with a Clear Strategy and Business Goals
Start by clearly defining your goals – this is the foundation of any successful data project. AI and big data engineering can certainly do a lot, but you need to focus on your priorities to get real value.
Ask yourself: What’s the biggest problem in your business right now? You may want to improve in several areas at once – for example, implementing data-driven decision-making or enhancing customer experience.
We believe the best approach is to pick one or two key areas where AI can make the biggest impact. These examples might spark your imagination:
If your team spends too much time on repetitive data tasks, it’s worth it to focus on automation. Data processing with AI can significantly reduce the amount of manual work and speed up operations;
If your business decisions are based on intuition rather than facts, AI analytics is able to process vast amounts of information and identify patterns to provide you with useful insights to make data-driven decisions;
If customer engagement is a challenge, AI can improve personalization through continuous behavioral analysis and predictions.
How to Build Data Infrastructure: 7 Key Steps & Tips
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2. Choose the Right Data Infrastructure
Your data infrastructure needs to be scalable, flexible, and secure so that AI models can seamlessly access and analyze structured (e.g., databases and spreadsheets) and unstructured data (e.g., emails, images, and text documents).
First, think about where to put your data. In our article on how to build a reliable data infrastructure, we’ve already broken down each element of this process –- here’s a quick guide for reference:
Deployment Type
Pros
Cons
On-Premises
Full control over security and performance.
High investment in hardware, IT staff, and maintenance.
Cloud
Scalability, cost savings, remote access.
Dependence on an internet connection.
Hybrid
Balance of control and flexibility, security for sensitive data.
Complex integration and maintenance.
Once you know where your data will live, you need to decide how to organize it. Different architectures are better suited for different business needs:
Architecture Type
Pros
Cons
Data Lake
Handles structured, semi-structured, and unstructured data; great for large-scale analytics.
Requires management to prevent becoming a “data swamp.”
Data Warehouse
Fast query performance, optimized for reporting and business intelligence.
Less flexible for unstructured data, requires predefined schemas.
Lakehouse
Supports both large-scale analytics and structured queries.
Requires complex infrastructure setup and maintenance.
3. Automate Data Quality Checks with AI
Automating data cleansing with AI makes this process much faster and more reliable:
First, using anomaly detection techniques, AI collects and cleans the data, automatically detecting inconsistencies, such as missing values or duplicates;
Next, machine learning models analyze historical and real-time data and identify patterns;
Moreover, deep learning algorithms can process unstructured data, such as customer feedback, to create the broadest possible analysis to uncover and highlight meaningful trends.
4. Streamline Data Processing and Transformation
Think about all those repetitive tasks – cleaning, transforming, and organizing data – that take up much of your team’s time. Wouldn’t it be great if AI could handle those? Well, it can!
You can automate routine data processing by integrating DataRobot or Google AutoML. These AI-enhanced data processing tools can detect inconsistencies, clean up messy data sets, standardize formats, and structure information for analysis.
AI can also improve collected data by generating new features through the discovery of hidden patterns.
5. Implement Real-Time Data Monitoring and Feedback Loops
It’s not enough to just set up an AI integration – you need to monitor it to make sure it keeps performing at its best. This is where real-time data monitoring comes into play. Tools like MLflow or Kubeflow can easily identify problems, manage, and track your AI models and data pipelines.
But don’t stop there! We also recommend setting up feedback loops toallow systems to keep learning as new data comes in.
6. Ensure Data Security and Regulatory Compliance
Security and compliance are always top priorities, no matter what industry you’re in. If you were wondering whether AI can simplify the process – the answer is yes, it can.
AI automates security protocols to detect and respond to potential threats and help automate compliance and privacy checks. For example, Amazon Web Services already offers built-in security features that help protect your data.
7. Train Your Team to Use AI Tools
It’s not enough to just have the technology – your team has to know how to use it, and how it can make the most of AI-driven analytics and automation. CHI Software can help your business implement AI-powered data engineering solutions at any stage. In addition to ongoing post-launch support, we offer consulting services to help you find answers to a wide range of questions.
Ready to automate your data processes? Boost efficiency with AI-powered solutions!
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Real-World Success Stories: AI-Driven Data Engineering with CHI Software
In case you’re still wondering if AI-powered data engineering can actually transform your business, let’s move away from theory and start talking about real-world results.
Better Advertising, More Profit
Let’s look at a case study example of a leading ad tech company that felt the need to optimize their Meta advertising campaigns. The reason was clear and simple: they were losing money due to slow manual data analysis and fragmented reporting. Their teams were spending hours collecting performance data before making budget decisions.
CHI Software provided automated data engineering with AI to our client from the AdTech industry.
How CHI Software and AI-powered data engineering changed the game:
Automated campaign tracking with a bot in an AI-powered messenger that provides hourly reports on spend, conversions, and ROI;
Reduced ad evaluation time by 50% – decisions that used to take two hours are now made in one hour;
Reduced infrastructure costs by 30% due to switching to AWS Lambda architecture;
AI-based campaigns, where the system automatically adjusts advertising budgets in real time based on performance.
Data Engineering Strategy: Benefits, Challenges & Best Practices
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Smarter Investment Decisions at Half the Time
A venture capital firm specializing in investing in startups faced a major data problem: manual data collection and processing from multiple APIs was slow, inconsistent, and unscalable.
A real case study of how AI-driven data engineering can change operations.
We created a fully automated AI-powered data pipeline that:
Reduced data processing time by 50%, allowing investors to analyze opportunities faster;
Eliminated manual data entry through automated data ingestion and classification using NLP algorithms;
Provided access to real-time insights by structuring data in Google Sheets for quick and easy analysis.
Now, our client receives AI-generated reports in seconds instead of hours.
As you can see, AI-powered data engineering is not just a buzzword. CHI Software managed to use these innovations to change every aspect of the company’s operations and helped them cut costs, make better decisions, and scale up faster.
Conclusion
The future of AI in data engineering is clear: in the coming years, it will only get stronger, eventually automating more and more processes. What does this mean for business? It means scaling up even faster and smarter, without the need for hiring or maintaining massive data teams.
If you still rely on manual processes, the time to start using AI-based data engineering is now. CHI Software’s expertise has already delivered impressive results, cutting data processing and evaluation time by 50% while reducing infrastructure costs by 30%. These results prove that companies who are implementing AI-based data engineering today will become the market leaders of tomorrow.
Are you ready for the future of your data strategy? Then let’s talk.
FAQs
How does CHI Software ensure seamless AI integration into existing data infrastructures?
Implementing AI into existing data infrastructures is an effort-intensive process – and with over six years of experience, CHI Software has built a solid approach:
1. First, we analyze your current systems so that our team can ensure AI solutions work with your existing tools, not replace them.
2. Then, during the development process, we use flexible architectures such as cloud pipelines (AWS, Google Cloud) and automated APIs to connect your data sources. This means a minimal impact on your business and fast AI adoption.
3. Afterward, we check your infrastructure’s workflow, measure results, and provide ongoing support to ensure you are getting the most out of your company’s data.
How does CHI Software differentiate from other AI and data engineering providers?
First, we are distinguished by our experience: more than six years and 30 successful data-based projects. Our developers specialize in complex AI and big data engineering, so we can handle everything from data collection to real-time analytics.
In addition, CHI Software creates highly customized AI solutions that meet your specific business needs. In other words, we focus on business outcomes, not just technical implementation – helping you drive revenue growth.
What’s the typical timeframe for implementing an AI-powered data engineering solution?
The timeline of implementation will depend on the size and complexity of your project, but here's a general idea:
- Small-scale automation (e.g., AI-based reporting tools): four to six weeks,
- Medium-scale AI solutions (e.g., predictive analytics, automated ETL processes): two or three months,
- Large-scale data transformations based on AI: four or more months.
How can AI-driven data engineering solutions increase my company's ROI?
As proved by CHI Software’s solutions, businesses typically see:
- Faster decision-making: AI processing of data in real time, reducing analysis time by up to 50%;
- Approximately 30% lower operating costs: AI automates repetitive tasks, reduces manual effort and labor costs;
- Higher accuracy: AI improves data quality and cuts the number of errors that can lead to costly business mistakes;
- Better resource allocation: AI-based insights help companies spend money smarter.
What’s the cost structure for implementing AI in data engineering with CHI Software?
Our consulting team can analyze your needs and provide you with a price consultation for your project. Here are the factors that affect the cost:
- Scale of AI implementation: simple automation costs less than a fully integrated AI data ecosystem;
- Cloud vs. on-premises solutions: cloud-based AI models can be more cost-effective;
- Level of customization: how customized do you want your solution to be? How many other systems should it be integrated with?
For example, small-scale automation using AI can cost USD 5,000. By contrast, a large-scale AI-driven data ecosystem with advanced analytics, machine learning, and cloud-based pipelines can start at USD 200,000.
Sirojiddin is a seasoned Data Engineer and Cloud Specialist who’s worked across different industries and all major cloud platforms. Always keeping up with the latest IT trends, he’s passionate about building efficient and scalable data solutions. With a solid background in pre-sales and project leadership, he knows how to make data work for business.
Oleksandr holds a Ph.D. in Probability Theory and Math Statistics and has a strong background as both a professor and engineer. He's worked with leading services like AWS and Azure, bringing expertise in machine learning, databases, and web applications. With skills in Python, .NET, JavaScript, and more, he's well-versed in building and optimizing tech solutions.
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