by Csongor Fekete | Nov 8, 2025 | AI, Business, Machine Learning
Xpeng recently showcased its latest advancements in AI technology during its AI Day, revealing a powerful, unified AI model driving innovation across multiple verticals including robotics, autonomous vehicles, and flying cars. The company's new in-house custom AI model, XBrain, is at the heart of this transformation. It integrates cross-domain capabilities to improve performance in real-time applications such as robotaxis and autonomous aerial vehicles.
Key takeaways from the presentation include:
- Introduction of XBrain, a multitasking foundational Machine Learning model capable of controlling diverse intelligent systems.
- Demonstration of a large quadruped robot executing real-world tasks using XBrain's capabilities.
- Highlight of Xpilot ADAS, which has evolved into a Level 4 autonomy solution for commercial use with improved real-time perception and control.
- Expansion of capabilities in flying car prototypes, drawing closer to commercial realization.
For businesses, this kind of holistic AI innovation demonstrates the strategic value of building vertically integrated custom AI models. In martech and CRM domains, such synergy can unlock significant productivity, marketing performance, and customer satisfaction gains. Implementing Machine Learning models that span multiple functions—from omnichannel personalization to automated decision-making—enables smarter and faster customer engagement strategies.
HolistiCrm can help organizations navigate similar AI transformations. Applying AI consultancy to design cross-functional models tailored to vertical needs—including sales, customer service, and marketing—drives better ROI and more intelligent automation.
This real-world case from Xpeng reinforces the imperative to invest in AI expertise and a custom AI model strategy built holistically from the ground up.
Source: original article
by Csongor Fekete | Nov 8, 2025 | AI, Business, Machine Learning
October’s AI announcements from Google deliver major advancements with direct implications for martech, customer personalization, and enterprise AI adoption. Key highlights include Gemini AI enhancements, new capabilities in Google Cloud’s Vertex AI platform, and updates to Search and Ads powered by custom AI models.
Gemini, Google's family of large language models, received notable performance boosts. Gemini can now power document summarization, generate content, and assist with support tasks – critical tools for improving customer satisfaction in client-facing roles.
Vertex AI Search and Conversations are now generally available, enabling organizations to build search and chatbot solutions tailored to their business data. This opens the door for companies to deploy holistic, domain-specific Machine Learning models that elevate customer engagement.
Performance updates in Google Ads include AI-automated campaign improvements. Brands can now generate personalized messaging with greater contextual relevance, ensuring higher marketing ROI and better user targeting. These tools empower marketers to deliver refined, data-driven experiences without sacrificing brand integrity.
For an AI agency or consultancy like HolistiCrm, these developments further underline the need for industry-specific AI expertise. Implementing intelligent applications on top of custom AI models can significantly reduce overhead, enhance analytics, and drive business growth.
A practical use-case: A retail customer could use Vertex AI Search to integrate product catalogs into a virtual assistant that guides buyers toward purchases based on preference patterns. Combined with custom marketing algorithms, this solution enhances conversion rates, shortens decision cycles, and builds long-term loyalty.
original article: https://news.google.com/rss/articles/CBMic0FVX3lxTE1Da0dWZkdkZXl0ZndFTk1QREhoSlVxQVgwc3FOTTI3N3YwQUd6M3RsSzhsdkFwcnJqcG5LQmJzbEZWYmQwZnhQZFBFRjBsZFhJVGhsMUlCOGVKWXZ5dndaYVhTeDhCeHVUTEd3a2I0bEE4NUk?oc=5
by Csongor Fekete | Nov 7, 2025 | AI, Business, Machine Learning
The Atlantic’s recent article "The Company Quietly Funneling Paywalled Articles to AI Developers" exposes a pivotal issue in the data ecosystem powering modern AI models: the covert usage of copyrighted, paywalled content to train large language models (LLMs). A shadowy network of aggregators is quietly scraping premium journalism from behind subscription barriers and funneling it to AI developers without proper licensing agreements. This practice threatens content creators’ intellectual property rights and raises serious ethical and legal concerns across the martech and AI space.
Key learnings from the article include:
- Certain AI development entities rely on non-consensual data acquisition to feed large-scale generative models, shaking trust and compliance in data sourcing.
- Content publishers face economic exploitation as their paywalled assets are harvested without compensation, undermining proprietary business models.
- The lack of transparency around data sourcing in AI raises long-term risks for model generalizability, compliance, and brand integrity.
From a business strategy perspective, a more sustainable and holistic approach to AI development hinges on creating custom AI models that are trained on licensed or first-party data. For brands and publishers, this is an opportunity to monetize high-quality proprietary content as premium training datasets through secure partnerships with trusted AI consultancy firms.
For instance, a martech provider can collaborate with a documentary publisher to co-develop a Machine Learning model for targeted content recommendations. By using legitimate, licensed data, the model can uphold content integrity, ensure publisher compensation, and drive measurable increases in customer satisfaction and engagement. This aligns not only with ethical AI development but also boosts the performance of AI-driven marketing strategies.
By investing in custom AI models rooted in ethical, transparent data sourcing, businesses can safeguard trust while unlocking innovation. HolistiCrm’s AI experts consistently emphasize the need for responsible AI practices that create long-term business value without compromising legal or ethical standards.
Original article: https://news.google.com/rss/articles/CBMijAFBVV95cUxNSDN4SEdZZVE3bWVzU0ZIM0x6VVdta3VWeE1yLXlaekEtYXF3d3R2UVVKVGgxRnZLbjNMV3ZfV0RaOUE1dFZtdTlLN0hNTXFUR3FyOXpVaDY0R3JTdHlILXg1RmtkSHByNmdTcWdSdDE2TlY2bHRoNTRUa2gtcm9nZFFNX2Z1RDd6TDZkUQ?oc=5
by Csongor Fekete | Nov 7, 2025 | AI, Business, Machine Learning
Google has recently removed an internal AI model after it generated false and damaging claims about a U.S. senator, including an unsubstantiated accusation of sexual assault. According to the report by Engadget, the AI model was part of Google’s experimental research and not deployed in consumer-facing products. Nonetheless, the incident highlights significant risks in deploying large-scale generative AI systems without appropriate safeguards.
Key takeaways from this event underline the importance of responsible AI development, rigorous testing, and human oversight. Models trained on vast internet data can unintentionally replicate harmful narratives or hallucinate misinformation, creating both reputational and legal risks for businesses.
For a martech or CRM business such as HolistiCrm, this case serves as a cautionary lesson in AI governance. Instead of relying solely on open-ended generative models, leveraging custom AI models trained on curated, domain-specific data can ensure more accurate, brand-safe outputs. This approach not only enhances performance but also supports customer satisfaction by avoiding misinformation and enhancing trust.
A use-case example: Implementing a holistic Machine Learning model for automated customer support chatbots in CRM systems. By utilizing a vetted training dataset focused on company-specific communications, chatbots can deliver high-quality responses without the risk of hallucinating harmful content. In addition to improving operational efficiency, this boosts customer satisfaction and prevents reputational damage—generating long-term business value. An AI agency or AI consultancy can further audit and fine-tune such models to align with corporate guidelines and compliance requirements.
This highlights why having an AI expert on board to guide ethical, safe, and effective AI integration is no longer optional—it's critical in today’s environment.
Read the original article: https://news.google.com/rss/articles/CBMivgFBVV95cUxQNW5TVjAwNmwtd0gzV196c202aEpLMzhham5XLUZna19WTmlnTFd4RktYZkxpNDZKdEJuOTlfU0JRck1NQTdHbVRhWmdHY3ROSWZSMjJsVjRkZTQzSlJXVUIybk9mNWs3QTRLall4NnR5dm1rUm9INVdtVTZjcEFXdUxEZU5mOGVhRVdESkZZQ29KQ05yOVVLSHgxSnNTaUhYUVdYcDZMeUFGT0FKTVRTR0JVUm9abW8tMWxvbjh3?oc=5 (original article)
by Csongor Fekete | Nov 6, 2025 | AI, Business, Machine Learning
The recent initiative reported by the University of Houston, titled “A Global AI Collaboration,” showcases the powerful potential of international team efforts in advancing artificial intelligence and machine learning technologies. This project united faculty and students from six universities across the United States, Mexico, and Latin America to develop next-generation AI models for diverse industries, from healthcare to energy.
One standout learning from the collaboration is the emphasis on culturally informed datasets and practices. Partner institutions tailored Machine Learning models with region-specific data to ensure that AI applications are contextually relevant and equitable—a practice that significantly boosts end-user satisfaction and long-term adoption. The global scope of this education-oriented collaboration also added value by training the next wave of AI experts across multiple disciplines.
For businesses in martech and CRM ecosystems, such as HolistiCrm, the learnings from initiatives like this highlight the value of custom AI models trained on domain-specific and culturally tailored data. These models can power personalized marketing strategies, improve performance in customer segmentation, and elevate engagement through predictive analytics. A concrete use-case: implementing a multilingual and culturally tuned recommendation engine that dynamically adapts campaigns based on regional customer behavior. This not only strengthens brand connection but also maximizes the ROI on marketing spend.
By taking cues from academia’s holistic and inclusive approach to ML development, forward-thinking AI consultancies and agencies can redefine how business applications translate research into real-world performance gains.
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by Csongor Fekete | Nov 6, 2025 | AI, Business, Machine Learning
Google's rapid advancement in generative AI took a pause this week as it removed its Gemma large language model from its AI Studio platform. The decision was prompted by concerns raised by U.S. Senator Marsha Blackburn, who claimed the model generated defamatory and false statements about her. This incident underscores the growing scrutiny surrounding AI-generated content and the accountability of tech companies in managing model outputs.
Key takeaways from the situation include:
- Deployment of AI models in public platforms demands rigorous safeguards against misinformation and defamation.
- Regulatory pressure on AI companies is intensifying, elevating the need for transparency and responsible data governance.
- Pre-release testing, context fine-tuning, and content filtering are no longer optional—they’re essential for maintaining trust and legal compliance.
A relevant use-case in marketing and martech industries highlights why this matters. When brands deploy custom AI models for content creation—be it copywriting, chatbot interactions, or campaign generation—safeguarding against reputational risk is critical. A hallucinated output from a Machine Learning model can lead to customer dissatisfaction, brand damage, or worse, legal complications.
A holistic approach promoted by AI consultancy firms like HolistiCrm involves building and validating models tailored to industry-specific use-cases. AI experts can develop custom AI models that prioritize accuracy, contextual relevance, and ethical safety—enhancing both customer trust and marketing performance.
The incident is a reminder that in the race for innovation, responsible deployment is not just a feature—it’s a strategic business value.
Reference: original article
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