by Csongor Fekete | Jul 14, 2025 | AI, Business, Machine Learning
Microsoft's recent blog post introduces Deep Research, a capability in the Azure AI Foundry Agent Service that enables a new level of contextual awareness and information retrieval. This innovation enhances the accuracy and depth of Machine Learning models by integrating large-scale retrieval-augmented generation (RAG) systems for enterprise search.
The key takeaway is how Deep Research allows agents to search across internal and external sources intelligently, apply synthesis to results, and generate responses that reflect holistic understanding. It’s powered by custom AI models and optimized orchestration layers for superior performance, user relevance, and scalability.
In the context of martech and customer engagement, this evolution provides a compelling use case. HolistiCrm could develop an AI-powered virtual assistant for marketing and customer service teams that takes advantage of Deep Research’s RAG pipeline. By integrating structured CRM and unstructured external data, businesses can offer contextual, fast-tracked insights to customers—boosting satisfaction, retention, and conversion.
For example, a virtual agent can dynamically synthesize product info, competitor insights, and past customer interactions to tailor high-impact campaigns or answer client queries in real-time. This delivers measurable business value by reducing manual research time, increasing customer satisfaction, and ensuring marketing personalization—all crucial metrics for any high-performing AI consultancy or martech AI agency.
original article: https://news.google.com/rss/articles/CBMioAFBVV95cUxQM2VQcnNVOEh5SFJrcjFCTWlza2VsV3JZOXRnbE03TzdVSGV3NU1TMDJtWnFvTFppV2RQLV9MTXRqY24wRzZEU1FaZHh6RUYxYUtsNVE5RloxNnBhcE9FVVptRTBlazBCMng2VTc2SFJzRng4THJzYVhCd0lsaXFGSGpXTTZkLXpkdmdUenJGOXR0Z2dEOGhGalZ2RnQ0eFUx?oc=5
by Csongor Fekete | Jul 13, 2025 | AI, Business, Machine Learning
The complexity and critical nature of AI systems in regulated sectors—like pharmaceuticals and medical devices—highlight a growing need for robust and standardized AI testing methodologies. Microsoft’s recent article, "AI Testing and Evaluation: Learnings from pharmaceuticals and medical devices," presents key takeaways that are increasingly relevant for businesses deploying mission-critical Machine Learning models across industries.
One crucial learning from the pharmaceutical domain is the emphasis on rigorous validation and transparent reporting. Just as life-saving drugs undergo methodical, multi-phase testing, AI systems should follow similar structured evaluation frameworks. This means going beyond traditional model accuracy metrics and incorporating continuous monitoring, context-aware validation, and model explainability. The article suggests adapting proven regulatory approaches, including designated roles similar to those in medical trials—like data stewards and evaluators—to facilitate accountability and traceability in AI development.
A use-case inspired by these learnings could be in the marketing technology (martech) sector. For instance, a company deploying a custom AI model for automated customer segmentation and campaign personalization can benefit immensely by applying a "pharma-style" testing approach. Creating predefined testing protocols, ensuring data bias evaluation, applying version control of model updates, and setting up human review checkpoints can drastically improve model performance and customer satisfaction.
By adopting these techniques, businesses gain not only risk mitigation but also a competitive edge in trust and transparency—a critical currency in AI-driven customer interactions. A holistic implementation of these principles through a competent AI consultancy or AI agency can become a business accelerator, ensuring the long-term viability and scalability of marketing operations powered by AI.
original article: https://news.google.com/rss/articles/CBMixAFBVV95cUxNNFJrRnFiUldWMFpiamlFa3AtdnZrQVVyVzJRSF9pMEctdWtmR19nNU95bFVRQzhCNC0wclNzVmNRb01qcjdBcEJXN3hGYXNXT3ZSZ0x0QXhwVmpoRFdiNGlUUTVyNGdjZlVzYkV1cUo2VjFqeUd0d3hqUTFtSjQwc0N3Sk05N2lVOGdKMXYtM2t2Zk1UaFZjdUhTeXJfU0RlSmVqcG8xNDFrMFZpbU9ueFZYR01fWFBBY3dLM0x3UjdEM3ZO?oc=5
by Csongor Fekete | Jul 13, 2025 | AI, Business, Machine Learning
In a recent study highlighted by Yale Insights, five leading AI models were tested on their ability to fact-check public statements—using several of Donald Trump’s claims as the benchmark. The research exposed significant gaps in performance, accuracy, and consistency. While the models often offered factual corrections, they frequently failed to identify misinformation comprehensively or confidently, with results varying across platforms and depending heavily on prompt phrasing.
Key learnings include:
- AI models show promise in real-time content verification but currently lack consistent precision.
- Sensitivity to prompt construction influences the validity of responses, raising questions about model reliability.
- Transparency of data sources and confidence scores are crucial for trust in AI-generated outputs.
- There is noticeable variance between general-purpose models and those fine-tuned for specific tasks.
The implications for martech and business strategy are significant. For brands handling large-scale content—blogs, social media, or customer communications—implementing a custom AI model trained specifically to detect misinformation can build trust and protect brand reputation. This is particularly relevant in political marketing, healthcare, and finance, where false or misleading claims have serious consequences.
At HolistiCrm, integrating such targeted Machine Learning models into marketing workflows helps validate claims before distribution, thereby increasing customer satisfaction and compliance. AI consultancy services become essential here—not just for deploying tools, but for refining them to align with business goals, datasets, and tone.
A real-world use-case: a political campaign or advocacy group using a custom fact-verification tool to validate public messaging in real time. This ensures holistic communication, reduces reputational risks, and enhances messaging performance. With a reliable internal content validation system, businesses can scale outreach while maintaining accuracy and integrity.
Read more in the original article.
Original article
by Csongor Fekete | Jul 12, 2025 | AI, Business, Machine Learning
Mayo Clinic researchers have developed a custom AI model capable of detecting surgical site infections (SSIs) through patient-submitted photos. This Machine Learning model, trained on a dataset of over 1,000 images, achieved 91% accuracy and outperformed both patients and non-infectious disease clinicians in identifying infections. The model was designed to help patients and care teams identify early signs of infection and intervene quickly, reducing complications and the burden on healthcare systems.
This use of AI highlights the power of combining holistic health data ecosystems with visual recognition models to drive better care outcomes. Notably, the system maintains high performance across diverse lighting and image quality conditions—an essential factor in real-world patient photo submissions.
The application of custom AI models in healthcare illustrates how AI consultancies and martech platforms can build decision-support models that scale human expertise. For AI experts and AI agencies, similar approaches can be extended to other image-based diagnostics in areas like dermatology, wound care, and chronic condition monitoring. Beyond healthcare, industries seeking to elevate customer satisfaction through proactive service interventions can draw parallels from Mayo Clinic’s results.
For CRM and martech providers like HolistiCrm, the key takeaway lies in empowering customer journeys with accessible, automated insights. Whether it's visual detection of product use issues or personalized service optimization, integrating Machine Learning models that analyze real-time visual data can unlock business value while increasing customer satisfaction and loyalty at scale.
original article: https://news.google.com/rss/articles/CBMi5gFBVV95cUxNTWtzdUxrcFJQYUM0VkJURThxX2xRdTREZzZFSFdYMFRGWmJWRnFYWDBPb19ZYkw5d1RSY1hUREFycUNIa01OUmpibDAyeXRlZk05QTcxV285bU5CVnNadlk3cTdpWTMwbWRlbkJwSzR1and0NDFoY3VESzE3WHA4VDAwSlpPOVNwMWtMTVdJNjFVelpkRjVleExoZnlqaTFHaVB5VFJwSlZnX3VFU2FKUGpEQU0ycTdPT2k0elFtU2Nud2xBeFlQdk1PMWN2TndNTnBQUFhnY2ZaYWMzeldjRGNCeVpRUQ?oc=5
by Csongor Fekete | Jul 12, 2025 | AI, Business, Machine Learning
A recent study explored the unexpected ability of ChatGPT to perform spacecraft piloting tasks with remarkable precision, a function typically reserved for highly-specialized machine learning systems in aerospace. In test simulations, the large language model responded effectively to complex commands, adjusting spacecraft trajectory and orientation autonomously. While not originally designed for this use-case, early indications suggest that generalist AI models like ChatGPT can adapt to highly technical environments through carefully structured prompts and scenario-based planning.
The core learning from the article is that natural language models can serve as interfaces for complex control systems, even in mission-critical applications like aerospace navigation, when paired with the right data and environments. This insight opens up a range of martech and commercial opportunities far beyond space travel.
In sectors like marketing or customer service, this innovation signals how custom AI models can be deployed in business-critical environments outside of traditional use-cases. For instance, a Holistic AI agency approach could leverage similar prompt-based reasoning engines to optimize marketing performance—automating campaign orchestration, interpreting analytics on the fly, or even adjusting real-time bidding strategies by modeling human-like decision making under pressure.
A specific martech use-case involves deploying a Machine Learning model via a natural language interface to dynamically coordinate multi-channel promotional activities based on customer engagement signals. Much like spacecraft navigation, campaign steering involves reacting rapidly to changing variables—click-through rates, conversion windows, and customer satisfaction metrics. Embedding a custom AI model trained on brand-specific performance data allows marketing teams to focus on strategy, trusting the AI for tactical execution.
This application can dramatically increase business value by reducing decision lag, decreasing operational costs, and improving customer satisfaction. As the article illustrates, the potential extends far beyond its original realm, making the case for AI consultancies like HolistiCrm to push innovation into high-performance marketing systems.
Read the original article: https://news.google.com/rss/articles/CBMixgFBVV95cUxOWUlERmJ6V2JVaG9MOWZlY1BwUTcwRXp5eHg0VG1yYUlxOWFNMTdNOHJxWUhrekdDMVM0QXF3Mmd4NlRmYUdiRFdPaU5UZ1ZYQjJ6aWxJOTZEbTZiUUhGQXdVVUhkbEJwVTNpVldjMXZuSFZXYUtteDdhUUhLR3VwOEVDYnF3OU9nT0V5a0tuQUxKOHdTZHlmZXFURXhuQVZZZWE3cm00akhGbVQ5UVhoWWdBY0JmNjNMN1dFSUVuSk9LM3lQLWc?oc=5
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