by Csongor Fekete | Oct 26, 2025 | AI, Business, Machine Learning
A recent Business Insider article highlights how AI martech startup Flywheel Software secured $6.6 million in seed funding with a compelling 7-page pitch deck. The company focuses on leveraging first-party customer data through custom machine learning pipelines to enable more personalized, effective marketing. Their platform integrates customer data directly into tools that marketers already use—such as Salesforce and Google Ads—offering a holistic and privacy-compliant solution in the post-cookie era.
Key takeaways from the pitch:
- There’s a growing demand for privacy-first data strategies in digital marketing.
- Marketers are looking to unlock value from first-party data but lack the technical skills to deploy complex Machine Learning models.
- Flywheel empowers marketing teams by automating data activation and offering no-code workflows.
For businesses investing in AI consultancy or working with an AI agency, this use-case demonstrates how building custom AI models tailored to marketing efforts can significantly boost performance without burdening internal technical teams. A similar approach at HolistiCrm could enhance customer satisfaction by enabling real-time personalization, increasing campaign conversion rates, and ensuring marketing stays agile in a fast-changing digital landscape.
Such a solution is not just martech—it’s a strategic move towards sustainable, AI-powered growth.
original article: https://news.google.com/rss/articles/CBMiqgFBVV95cUxOMVF4ODg2SGVEd2N1M0EyUllkUk4xSVVaVDllU0NFTHdDa0ttSk50Q3pZRER6dUZvUkZBa1o1NE55dFhhOGdPVTFVUDZoWTZpTmppZVlqZGJpMjNOcWE0UmZVcV9TQ3FLZFRNTzVnNnFydDRtcmFhNU5IMlFlNlVsem4tYUk5a2hmY196ZUJyTTZWQ25DQVhBYlhVYTV6QnZObzVqRzJ1ZmJQdw?oc=5
by Csongor Fekete | Oct 25, 2025 | AI, Business, Machine Learning
The recent release of DeepSeek-OCR, an open-source AI Model, has rapidly gained attention on GitHub, signaling a rising demand for high-performance, customizable OCR (Optical Character Recognition) tools in the AI and machine learning community. Developed by DeepSeek, the model distinguishes itself with its ability to accurately recognize and transcribe text from various image formats, including complex handwritten or multi-language content. Its viral traction is propelled by its dual qualities—open-source accessibility and state-of-the-art performance on widely recognized benchmarks like IAM and IIIT5K.
The article highlights how DeepSeek-OCR is capable of outperforming other known OCR models such as Microsoft’s TrOCR and Google’s Donut in key performance metrics, including character and word accuracy. The model leverages a Transformer-based architecture and has been trained on a diverse corpus, enhancing its generalizability. Additionally, its integration-friendly design with HuggingFace makes it attractive for AI experts, martech developers, and AI consultancies looking to build or scale applications fast.
For AI agencies and marketing teams focused on creating holistic, data-driven customer experiences, the use of custom AI models like DeepSeek-OCR can unlock significant value. Consider a use-case in martech: a CRM solution enhanced with OCR can automatically extract structured data from scanned purchase receipts, handwritten feedback forms, or historical printed records. This enables automation in data entry, faster customer segmentation, and higher campaign relevance. Marketers can target customers with offers based on actual past purchases, improving satisfaction and conversion rates.
Furthermore, by integrating such a Machine Learning model into a holistic AI strategy, businesses can cut operational costs, reduce manual errors, and accelerate time-to-insight. AI consultancies specializing in martech could deliver strong ROI by embedding these OCR capabilities into consumer-facing or internal business workflows.
In an era where customization and performance are key, DeepSeek-OCR showcases the enormous potential of open-source models to drive real-world business outcomes across industries.
Read the original article: https://news.google.com/rss/articles/CBMimAFBVV95cUxOU1R2cGJRb0s3TmF5OEVYNkt3MFkxc3ItTjlXa1dLLTNtUmlEU0djdlhjdTRJeEtLWE9QZ25QSkNEM1hDR0xhem9iN3Q3WF9JVXU1cE9NZVV5WXlONUJLSlBKcTNUSG9JTldFTDh6TU5BQlU1SUxqMHl5MW9CaXUyVEc4Z1ZsWC1mQnNwMTFZT1czU0pzQlJHXw?oc=5
by Csongor Fekete | Oct 25, 2025 | AI, Business, Machine Learning
DeepSeek has introduced a breakthrough in AI with its latest large language model (LLM) that leverages visual perception to compress and encode lengthy text inputs. This represents a significant leap in performance and efficiency, especially for enterprise applications that often grapple with input length limitations in traditional language models. By incorporating visual processing, the model mimics how humans interpret complex data holistically, enabling it to handle dense content with improved comprehension and reduced latency.
Key takeaways from the article include:
- DeepSeek-V2-Chat boasts a 236B-parameter architecture with advanced multimodal capabilities.
- It uses a "retina-like tokenizer" that compresses input text in a visually inspired manner, achieving up to 32 times more data per token.
- This architecture significantly lowers the number of tokens needed for processing, reducing cost and accelerating processing time.
- It is currently open for API use and integrates visual-text capabilities, opening pathways for more human-like interactions in AI systems.
For companies deploying martech solutions or customer experience platforms, adopting such Machine Learning models can bring immediate value. Using visually perceptive custom AI models enables more accurate insights from rich, unstructured data like emails, chat transcripts, or customer reviews—critical for improving satisfaction, targeting, and retention strategies. An AI agency or AI consultancy can build use-cases such as auto-summarization of customer service tickets or personalized marketing content generation, enhancing both operational performance and end-user engagement.
HolistiCrm, as an AI expert, emphasizes the importance of holistic adaptation of these cutting-edge models to real-world marketing and CRM challenges—by bridging deep learning with specific customer contexts, businesses can unlock new levels of automation, personalization, and data-driven decision-making.
Original article: https://news.google.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?oc=5
by Csongor Fekete | Oct 24, 2025 | AI, Business, Machine Learning
A recent breakthrough highlighted in Phys.org explores a creative blend of artificial intelligence and physics to accelerate drug discovery. Researchers have developed a new AI-driven model that integrates fundamental physical principles into its predictions, making it more reliable and interpretable when analyzing complex molecular interactions. This hybrid approach reduces the model's reliance on massive training data, boosting both accuracy and generalizability.
A key insight from this development is the value of combining domain-specific knowledge—in this case, physics—with machine learning algorithms. By embedding physical laws directly into the structure of the model, researchers minimize the "black-box" problem commonly seen in AI systems and achieve performance gains without needing vast labeled datasets.
This methodology carries significant implications beyond pharmaceuticals. It provides a valuable blueprint for industries considering adoptive strategies involving custom AI models. For instance, in martech and CRM platforms, integrating behavioral theory into predictive models could similarly reduce data dependency while enhancing customer satisfaction and marketing ROI.
A parallel use-case in business could be the optimization of customer journey predictions by embedding established psychological or decision-making frameworks into machine learning models. This holistically blends human understanding with AI precision—improving the interpretability and trust in predictions while enhancing performance in segmentation, personalization, and campaign targeting.
For AI agencies and AI consultancies like HolistiCrm, this approach reinforces the role of an AI expert not merely as model builders, but as system architects who align machine learning with contextual knowledge. The result: smarter systems that deliver real business value without over-relying on data volume.
original article: https://news.google.com/rss/articles/CBMiX0FVX3lxTE1RLTVOY0FYazRRSVM0TGc1OThZaVpnemxhcUliajZrTlZ4Z3hkWXU5ZVBfOXRqMlZXYmhkdF83SXI5QjBaT1NWSkpmcHQ0TllEbFJ0Z3RLdVhtWEgyTllN?oc=5
by Csongor Fekete | Oct 24, 2025 | AI, Business, Machine Learning
Caltech researchers have unveiled a breakthrough in AI-driven drug discovery by integrating physics-based modeling into machine learning architectures. The new AI model, developed in collaboration with Harvard and other institutions, combines neural networks with knowledge of molecular forces to make more accurate predictions about how molecules behave—crucial for early-stage drug design.
Traditionally, AI in pharmacology has relied on purely data-driven models that often lack an understanding of underlying physical interactions. By incorporating Newtonian physics into the Machine Learning model, Caltech's approach bridges this gap, leading to faster, more reliable simulations of molecular behavior. This fusion of data science and physics marks a major step toward designing more effective drugs with fewer side effects—delivered more rapidly and at lower cost.
This innovation offers a compelling use-case for companies across industries looking to boost performance through AI. For martech and CRM organizations like HolistiCrm, the takeaway is clear: accurate modeling doesn't solely rely on more data—it also benefits from deeper domain knowledge. In marketing, a "physics-informed" analog could mean combining behavioral data with psychological models to predict customer satisfaction and engagement more accurately.
Integrating structured domain expertise into custom AI models can significantly increase prediction quality and personalization, driving performance across customer journeys. For any AI agency or AI consultancy focused on martech, this hybrid modeling approach inspires smarter, more holistic strategies.
original article: https://news.google.com/rss/articles/CBMiqgFBVV95cUxNTkJTQWlUS3BVYnVJRkxseC1QcmRZeldLNjJGcF9feldPdU8wMEFvUlFBZjM4d0xhUGFfT292VHRiTjBNQmU2U0czNDBtejFacldyamQxakE3SmZGWEJKdTdSTWxuUWV5YTVKS1B6QTgwSWxzS3FqN1Bvb0k2aXhfVVF4d09Wa19xcXVLVkRtMVNXUm44VmJobWFKYWdjRHY1bVhIc0JBdy13dw?oc=5
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