ChatGPT vs DeepSeek: An Artificial Intelligence Showdown

Artificial intelligence (AI) has stopped being a technology of the future to become an essential component of the digital transformation that companies face today. Tools like ChatGPT and DeepSeek have revolutionized the way we interact with data, allowing us to automate processes, optimize decision-making and improve the user experience.

In this article, we will explore how these technologies can be applied in B2B and B2C environments, maximizing their impact on large companies.

ChatGPT: Natural language processing and generation

Developed by OpenAI, ChatGPT is a transformer-based model that has proven to be a powerful tool for various business applications. Its ability to understand and generate natural language makes it a key ally for automation and operational efficiency.

Use cases of ChatGPT in the corporate environment

  • Automated customer service ChatGPT can be integrated into chatbots and customer service systems to provide quick and accurate answers to user queries. This not only improves the customer experience but also reduces the workload of the support team, allowing agents to focus on more complex cases.
  • Content creation Companies can use ChatGPT to generate high-quality content for blogs, social media and marketing campaigns. Its ability to understand context and write coherent text makes it an effective tool for communication teams.
  • Language translation In a globalized environment, ChatGPT facilitates communication between multicultural teams through real-time automatic translation, streamlining collaboration between departments and branches in different countries.

Integration of ChatGPT in large companies

In corporate environments, ChatGPT can be integrated into CRM and ERP platforms, allowing for more efficient customer management and optimizing the automation of internal workflows. In addition, its use in internal virtual assistants facilitates access to key information for employees and work teams.

DeepSeek: Artificial intelligence applied to data search and analysis

While ChatGPT excels at language generation, DeepSeek specializes in advanced search and information retrieval. This AI is capable of analyzing large volumes of data and providing relevant information in real time, making it an essential tool for strategic decision-making.

Use cases of DeepSeek in the business sector

  • Internal search optimization In large corporations, the amount of data generated can be overwhelming. DeepSeek facilitates the search for information within corporate databases, helping employees find key documents in a matter of seconds.
  • Competitive intelligence DeepSeek can analyze market trends and assess the position of the competition. By providing accurate data in real time, it helps companies adjust their strategies proactively.
  • Research and development In sectors such as biotechnology, pharmaceuticals and technology, DeepSeek tracks scientific publications and patents to identify new opportunities for innovation and development.

Implementation of DeepSeek in the business ecosystem

DeepSeek can be integrated into ERP systems and data analysis platforms, allowing companies to access relevant information instantly. This implementation promotes collaboration between departments and improves efficiency in data management.

ChatGPT + DeepSeek: An artificial intelligence ecosystem for companies

The combination of ChatGPT and DeepSeek allows companies to enhance their operational efficiency by integrating AI into their processes. Some of the main advantages of this synergy include:

  •  Greater operational efficiency: Automation of processes and reduction of search and content generation times.
  • Data-driven decision making: Access to accurate information in real time to optimize business strategy.
  • Improved customer experience: Faster and more personalized responses, which increases customer satisfaction and loyalty.

The future of artificial intelligence in the business environment

As AI continues to evolve, tools like ChatGPT and DeepSeek are redefining the way companies operate. The integration of these technologies is not only a competitive advantage but a necessity to stay at the forefront in a constantly changing market.

At Hike & Foxter, we help companies implement AI-based solutions to optimize their performance and maximize their impact on the market. If you want to discover how AI can transform your business, contact us and let's talk about the future of technology applied to your company.

Ready to take your company to the next level with AI? Let's talk!

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Group your data like a pro: clustering with K-Means and BigQuery ML

Working with large volumes of marketing data—whether it’s web traffic, keywords, users, or campaigns—can feel overwhelming. These data sets often aren’t organized or categorized in a useful way, and facing them can feel like trying to understand a conversation in an unfamiliar language.

But what if you could automatically discover patterns and create data groups—without manual rules, endless scripts, or leaving your BigQuery analysis environment?

That’s exactly what K-Means with BigQuery ML allows you to do.

What is K-Means and why should you care?

K-Means is a clustering algorithm—a technique for grouping similar items. Imagine you have a table with thousands of URLs, users, or products. Instead of going through each one manually, K-Means can automatically find groups with common patterns: pages with similar performance, campaigns with similar outcomes, or users with shared behaviors.

And the best part? With BigQuery ML, you can apply K-Means using plain SQL—no need for Python scripts or external tools.

How does it actually work?

The process behind K-Means is surprisingly simple:

  1. You choose how many groups you want (the well-known “K”).

  2. The algorithm picks initial points called centroids.

  3. Each row in your data is assigned to the nearest centroid.

  4. The centroids are recalculated using the assigned data.

  5. This process repeats until the groups stabilize.

The result? Every row in your table is tagged with the cluster it belongs to. Now you can analyze the patterns of each group and make better-informed decisions.

How to apply it in BigQuery ML

BigQuery ML simplifies the entire process. With just a few lines of SQL, you can:

  • Train a K-Means model on your data

  • Retrieve the generated centroids

  • Classify each row with its corresponding cluster

This opens up a wide range of possibilities to enrich your dashboards and marketing analysis:

  • Group pages by performance (visits, conversions, revenue)

  • Detect behaviors of returning, new, or inactive users

  • Identify products often bought together or with similar buyer profiles

  • Spot keywords with unusual performance

How many clusters do I need?

Choosing the right number of clusters (“K”) is critical. Here are a few strategies:

  • Business knowledge: If you already know you have 3 customer types or 4 product categories, start there.

  • Elbow Method: Run models with different K values and watch for the point where segmentation no longer improves significantly.

  • Iterate thoughtfully: Test, review, and adjust based on how your data behaves.

Real-world examples

With K-Means in BigQuery, you can answer questions like:

  • What types of users visit my site, and how do they differ?

  • Which pages show similar performance trends?

  • Which campaigns are generating outlier results?

Grouping data this way not only saves time—it reveals opportunities and issues that might otherwise go unnoticed.

Conclusion

If you're handling large data sets and need to identify patterns fast, clustering with K-Means and BigQuery ML can be a game-changer. You don’t need to be a data scientist or build complex solutions from scratch. You just need to understand your business and ask the right questions—BigQuery can handle the rest.

Start simple: take your top-performing pages, group them by sessions and conversions, and see what patterns emerge. You might uncover insights that completely shift how you approach your digital strategy.

 

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