by Csongor Fekete | Aug 19, 2025 | AI, Business, Machine Learning
In the journey to unlock the full potential of artificial intelligence, companies like Profound are setting a new standard for how a custom AI model can drive strategic value across industries. The Sequoia Capital-backed Profound exemplifies a transformative approach to building holistic AI solutions purpose-built for enterprise adoption. Their key differentiator lies not just in technical expertise but in their customer-centric philosophy, designing AI with practical, scalable performance at its core.
The article outlines how Profound blends AI research with business execution, forming tight partnerships with clients to co-create bespoke Machine Learning models that directly solve business problems, from automating workflows to enhancing customer satisfaction. At its foundation is a new architecture that integrates structured enterprise data with cutting-edge generative AI — making AI both contextually informed and operationally relevant.
For businesses operating in performance-driven sectors such as martech and CRM, this model offers a clear blueprint for innovation. Imagine a custom AI model integrated into a CRM platform like HolistiCrm: automatically generating hyper-personalized marketing campaigns that adapt based on real-time customer behavior. The impact? Significant uplift in engagement, reduced churn, and measurable ROI — a true business value multiplier powered by AI.
This case highlights the critical role of an AI consultancy or agency in bridging the gap between raw model capability and applied business value. As enterprises increasingly seek AI experts to guide their transformation, the lessons from Profound's partnership model serve as a masterclass in aligning technology with strategy.
Read the original article: https://news.google.com/rss/articles/CBMijAFBVV95cUxQM2t3UlpNSzZ4emRkQmVFbU5aRmFtMTFFdkRyOTJJZVlfeDd5RzUzN2Flb0poSDE2VF9OZlM1TmRZM2QtRjV3Y3FEcGgtZG9zLVUwekIwbFZaTVpmR00zN1E3eENwdmdsNkIxUXZFS21NSVgxYWtNTkUxSmsxOVZlaG9kc0pxWDJvdF9aaw?oc=5
by Csongor Fekete | Aug 19, 2025 | AI, Business, Machine Learning
Anthropic has significantly upgraded its Claude AI model to support much longer prompts, a move that can unlock more powerful business applications and deeper user interactions. The Claude model can now process up to 200,000 tokens in a single prompt — more than 500% increase over many existing large language models. This advance allows Claude to analyze entire books, technical documentation, or extensive customer communication histories in one go.
For marketing and martech-focused businesses, this development holds transformative potential. Longer context windows make it possible to build custom AI models tailored to intricate customer journeys, enabling AI-driven personalization at scale. A Holistic approach to machine learning can now include the full breadth of customer interaction history, behavioral data, and campaign performance metrics — all in single-model evaluations or strategy planning.
One practical use-case is enhancing customer satisfaction through intelligent CRM systems. Imagine a Machine Learning model embedded within a CRM that digests full email exchanges, call summaries, and purchase history. It can then suggest precise next actions for sales reps or automatically generate hyper-personalized outreach messages that factor in long-term context.
HolistiCrm’s AI consultancy and AI expert teams can leverage this development for building in-depth knowledge graphs of customer data or deploying support chatbots that truly understand ongoing cases, not semi-isolated queries. This increases both performance and efficiency, while creating tangible business value through improved retention and conversion rates.
The next frontier in custom AI lies in handling depth and nuance — and Claude’s extended prompt capabilities open the door.
Read the original article: https://news.google.com/rss/articles/CBMilgFBVV95cUxOUEhqNkEtMS1EbXRBNElhQlFwdEVXZll3cC1ENUtxV3lHLTJhWmJGRDJqeUgwVnlNVENsOFd2MW44aF9kV0JZT3BHeWp3dWRCUXU4eEplNDFUWnc4dVJPenBFYl9NZzdPUUtuNWJiZS1yQXZOejROYk1FdGk5Mk90YUZfOU5STnV0a21zZ0U0UHBFWG1YTGc?oc=5
by Csongor Fekete | Aug 18, 2025 | AI, Business, Machine Learning
The recent lawsuit filed by major book publishers against AI developers raises significant concerns for the future of the AI industry. The core of the dispute, as highlighted in the article “AI Industry Warns That New Lawsuit Could Destroy It Entirely” by Futurism, revolves around the use of copyrighted material for training large language models (LLMs). Plaintiffs argue that AI companies have unlawfully scraped and repurposed thousands of copyrighted books without permission. AI firms, on the other hand, claim such use falls under fair use and is necessary to push the boundaries of innovation.
The case has sparked alarm across the martech and AI consultancy space. If courts side with the publishers, the decision could paralyze the development of general-purpose language models by making it financially or legally infeasible to collect and use large, diverse training datasets.
This legal battle underscores the need for a more holistic approach to AI development—balancing innovation with fairness, regulatory compliance, and respect for intellectual property. Companies deploying custom AI models must now consider not just performance but also data ethics and licensing frameworks to ensure long-term viability.
In terms of business value, a practical use-case might involve creating a legally-compliant, domain-specific Machine Learning model for marketing that leverages licensed or customer-provided content rather than scraped public data. For instance, a retail marketing team could use HolistiCrm’s AI agency services to build a model trained on customer reviews, CRM logs, and campaign data—enabling personalized email targeting that boosts satisfaction and conversions without legal risk.
By acting as an AI expert and advisor in this emerging regulatory environment, HolistiCrm helps businesses future-proof their martech strategies with responsible AI.
Original article: https://news.google.com/rss/articles/CBMiZEFVX3lxTE5jYzFrdEF6NWpJRHRaQm1qWWJ5VVJlMzFvUEZvbG1tNW9UYkJZWWJPcjhzdWpfU21qa25GZ2VkbUZ5R0FoNUhnX1pmeTJHS1FQSXAzdUFOM3dYRnZXWEVqNFdkNU0?oc=5
by Csongor Fekete | Aug 18, 2025 | AI, Business, Machine Learning
As the adoption of artificial intelligence continues to accelerate, one pressing challenge is emerging at the intersection of AI deployment and global sustainability: energy consumption. In the recent Goldman Sachs article, “Bridging the Gap: How Smart Demand Management Can Forestall the AI Energy Crisis,” the spotlight falls on the growing energy requirements of AI infrastructure and how strategic energy management can offset risks to grid stability and environmental impact.
Key takeaways from the article include:
- AI’s skyrocketing energy demand could stress energy grids and increase carbon emissions unless proactively managed.
- Smart demand management and real-time energy allocation strategies, such as dynamic load balancing and AI-powered energy forecasting, are essential in optimizing power usage.
- Policy and infrastructure reform, along with public-private partnerships, will be critical to aligning AI advancement with sustainable energy practices.
From a business perspective, this shift presents both a challenge and an opportunity. For industries like martech, sales automation, or customer data platforms that increasingly depend on Machine Learning models, aligning energy efficiency with custom AI models can enhance performance while reducing operational costs. Holistic AI solutions that include demand forecasting and intelligent resource allocation are vital in this regard.
A use case in marketing could include a HolistiCrm deployment where energy-aware Machine Learning models optimize campaign delivery schedules based on predicted server load and grid capacity. This not only reduces energy costs but improves performance and data throughput during peak marketing hours—directly impacting customer satisfaction and ROI.
Businesses seeking to integrate these practices should work with an AI consultancy or an AI agency with expertise in energy-conscious model deployment, ensuring that smart demand management becomes a core part of their long-term AI strategy.
Read the original article: https://news.google.com/rss/articles/CBMi1AFBVV95cUxNaVQtQmVyVUhGbFRQTF9INnRNdXRqVlhGZkR6RnlKSDhvek02MjR5d1dadTRuaHdkS2F1SHFvRjBVQXZnekFZb3BmUVlGSzd2cjBDNzJLNnhVT2lUVVBDeDlWc3U0TnR1SE1ma2FTTG5BZkNRbGthQThJcG1FUE5jMV9aNVJ3VkQtWkFjS3ZkY05CMk9IR2M4WlhXYVZQSHI1Mk9rTHRITk9BOGNjazI5YUdoNWZXNXhmMkhZQXBtQXktUEgydXU4M0JTUnlkMUMwNVhVVA?oc=5 (original article)
by Csongor Fekete | Aug 17, 2025 | AI, Business, Machine Learning
NVIDIA’s recent initiative to expand Physical AI into urban and industrial environments underlines a powerful evolution in real-time data processing, edge AI deployment, and the creation of safer and smarter spaces. The collaboration with partners like Cisco, Johnson Controls, and Siemens illustrates how purpose-built AI platforms are transforming complex infrastructure into intelligent ecosystems.
A key takeaway from the article is the use of NVIDIA Metropolis—an AI framework designed to interpret sensor and camera signals at the edge. This enables real-time action for safety, efficiency, and operational continuity across smart cities, airports, manufacturing plants, and utilities. Deploying AI at the edge not only reduces latency but also minimizes bandwidth costs and boosts performance reliability.
Another learning centers on the role of scalable partnerships and open models. Through integration with NVIDIA’s Jetson and IGX platforms, businesses can adopt AI solutions without rebuilding their existing systems, enabling a holistic approach to digital transformation.
This is especially relevant for companies investing in martech or seeking to push customer satisfaction through operational safety and sustainability. A potential use-case in the realm of smart retail could involve deploying edge-based Machine Learning models in stores to monitor crowd density, optimize store layouts, or personalize on-site marketing in real time. Holistic AI consultancy services can rapidly implement custom AI models that improve customer experience while enhancing compliance and reducing overhead.
For enterprises looking to enhance their martech stack or infrastructure safety via AI, working with a dedicated AI agency focused on measurable business value and custom solutions makes a significant difference in long-term impact.
Original article: https://news.google.com/rss/articles/CBMihgFBVV95cUxQQXNEd3pSYzZFZld5MzI3d0ZUNk9WS21DYy0xSEY0YmdWRkJYekFhTVdFMWRBOFhtcks3SnhCMU4yTVJ2TGY1RWY2bWtISm5wM0pvSERHcjZTTUN6WEpBcmNpSDRLS01NbTdGRG5hQlRpZmZ4WWQ0WDZkdFRFNTViSkFZbkpWZw?oc=5
by Csongor Fekete | Aug 17, 2025 | AI, Business, Machine Learning
In the rapidly evolving landscape of artificial intelligence, NVIDIA’s latest research in "Physical AI" represents a new frontier where machine intelligence meets real-world complexity. The article “NVIDIA Research Shapes Physical AI” details breakthroughs in simulating and teaching robots to understand physical environments using advanced Machine Learning models and compute power from NVIDIA’s platforms.
Key takeaways include:
- Development of custom AI models specifically designed to train physical agents in simulation environments.
- Use of high-fidelity synthetic environments like Isaac Sim to enhance performance in real-world robotic applications.
- Integration of vision, touch, and motor control into unified models that lead to significantly improved autonomy and decision-making in robots.
- Open-source frameworks and collaboration with academia and industry to accelerate innovation.
For a martech or CRM-driven company like HolistiCrm, the application of Physical AI can be extended beyond robotics. Consider a use-case where immersive customer simulations are built using similar high-fidelity digital twins. Custom AI models trained in virtual environments can predict customer behavior, optimize user journeys, and enhance satisfaction with smarter, real-time recommendations.
As an AI consultancy or AI agency, implementing such simulations allows businesses to test campaigns, user interfaces, and service flows without deploying in live environments—saving costs and improving marketing agility. This holistic approach drives business value by merging physical customer behavior insights with software-led decision systems.
Embracing NVIDIA's approach to Physical AI is a step toward more robust, predictive, and adaptable machine learning applications that align with the future of human-AI interaction.
original article: https://news.google.com/rss/articles/CBMic0FVX3lxTE41aXp0LUVad0ljOUw5WUNTRE5GMTFlWFdrVmdwSWdQNjJrb3hoUWJiU3BYU2xibEZnTkVpTjNzajFycVZQU0N3dlFiU2NWejBVeXhFU09fbUx1ZHpOSUplOFV2MGxrVU9mYVhCMU1pZzlhTzQ?oc=5
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