PremAI

🚀 We're proud to announce our $14 million #seedfunding round. This milestone accelerates our mission to redefine generative AI. Investment Details: 📍 Lead Investors: David Maisel of Marvel Studios, Fan Zhang of Sequoia Capital China, and Alan Lipschitz of Incubeta. Their expertise fuels our strategic growth. 📍 Usage: Advancing our AI technology, team expansion, and market strategy. ☘ Thanks to our investors and team for your trust and support. Stay tuned for more updates and discover the details in our blog: https://lnkd.in/di39Q_5n Read our CEO Simone Giacomelli statement in this Business Insider article. https://lnkd.in/d_PhSCDm

🌟 PREM AI at AI Lab – Shaping the Future of AI Innovation We are honored to contribute to Session 2: AI Lab – Future of AI Innovation, part of AI in Action during unDavos Summit Week 2025. 📅 Date: January 21, 2025 ⏰ Time: 16:15–17:45 CET 📍 Location: Mountain Plaza Hotel 🔗 Register here: https://lu.ma/i6sbb2fx This session offers a unique platform to witness brief, high-impact presentations from leading AI companies, followed by in-depth roundtable discussions. Representing PREM AI, Judy Okten will highlight how our innovative solutions drive meaningful change and redefine what AI can achieve. Don’t miss this opportunity to engage with cutting-edge technologies and connect with visionaries shaping the AI landscape. #AIInAction #DavosWeek #AIInnovation #ShapingTheFuture

🚀 Thrilled to be part of this Journey, thanks to Endeavor Italy & Fondazione Sviluppo e Crescita CRT for this opportunity

💻 As the demand for real-time, privacy-centric, and low-latency applications grows, edge deployment of language models (#LLMs) emerges as a transformative shift in AI. ☁️ Moving beyond traditional #cloud-based systems, #edgecomputing brings AI closer to the #datasource, unlocking new possibilities for industries like healthcare, robotics, IoT, and autonomous systems. In our latest article, we went trough the challenges, innovations, and future directions of deploying LLMs at the edge. 🧑💻 From overcoming computational and memory constraints to leveraging advanced techniques like parameter-efficient #finetuning, model #quantization, and distributed #inference, this deep dive uncovers the technologies paving the way for scalable and efficient edge-based AI. 💡 Key Technical Insights: 🔎 Optimization Strategies: Techniques such as #LoRA, structured pruning, and speculative decoding for energy-efficient, resource-limited environments. 🤖 Collaborative Computing: Split learning and multi-hop inference to balance workloads between edge devices and servers. 📂 Architectural Innovations: AI-native #edgearchitectures and parameter-sharing #cachingframeworks to minimize latency and enhance scalability. Deep dive in our findings here: https://lnkd.in/dTPFp7Rk

#AIDeployment: The Technical and Practical Impact of #LLM Routing 🔎 The deployment of Large Language Models (LLMs) often presents a challenging trade-off between performance and cost. Advanced models deliver exceptional quality but demand significant #computational resources, while smaller models like #Llama2 offer cost efficiency at the expense of output quality. 📬 #LLMRouting addresses this dilemma by dynamically assigning tasks to the most appropriate model based on query complexity. This innovation can reduce costs by up to 75%, enabling businesses to scale their AI operations without compromising on quality. In our latest article, we examine the technical foundations of LLM routing, including: 🔑 Routing #algorithms: Deterministic, probabilistic, and hybrid approaches. 📂 Dynamic #thresholds: Adjusting for cost-performance optimization in real-time. 💻 Performance #benchmarks: Metrics such as #BART scores, cost efficiency, and recovered performance gaps. ✅ This article serves as a guide for technical leaders, developers, and AI practitioners seeking to maximize the efficiency of their AI infrastructure while minimizing operational costs. 📖 Read the full analysis here: https://lnkd.in/dNk9nzxU

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The Evolution of #AIagents: Hype or Breakthrough? 🔑 The AI agent ecosystem is experiencing rapid growth, merging cutting-edge autonomy with adaptability to redefine how industries operate. 🔗 From enhancing decision-making to streamlining complex workflows, these systems hold immense promise—but do they deliver? Our latest deep dive explores: 💯 Technical Underpinnings: How #NLP, #ML, and #reinforcementlearning power these agents. 📚 Real-World Applications: From financial insights to healthcare innovations, discover their transformative potential. 📑 Challenges & Opportunities: Addressing scalability, ethical considerations, and integration hurdles. 📬 Market Dynamics: Is the projected $151.8B #market by 2033 sustainable—or inflated by hype Dive into the full article: https://lnkd.in/dT_V3K68

📚 MicroSaaS is a lightweight, niche-focused approach to #SaaS that’s changing the tech landscape. The game-changer? #ArtificialIntelligence. In this highly technical exploration, we break down: 🤖 AI’s Role in #MicroSaaS: Automating code, enhancing user personalization, and enabling hyper-scalability with minimal resources. 📚 Step-by-Step Guide: From niche identification to MVP development and advanced #AIintegrations, learn how to build robust solutions faster. 💻 Real-World Code Examples: See how tools like PREM-AI automate #RESTAPI creation, integrate with Shopify APIs, and scale applications seamlessly. 🔮 Future Trends: AI-native #workflows, serverless architectures, and hyper-personalization redefining the MicroSaaS ecosystem. Whether you’re a solo developer or leading a small team, this post will show you how AI unlocks the full potential of MicroSaaS.💡 👉 Read the full breakdown here: https://lnkd.in/dgSrzA3M

✅ As Large Language Models (#LLMs) redefine AI capabilities across domains, their evaluation is becoming increasingly critical. 🤖 From performance #benchmarking to ethical and safety assessments, robust evaluation methodologies are essential to ensure LLMs meet the demands of real-world applications while addressing trust and accountability. Our latest article provides a comprehensive analysis of: 📌 Key Objectives of LLM Evaluation: Performance benchmarking, alignment with human values, and risk mitigation in sensitive applications. 📊 Evaluation Dimensions and Strategies: Covering static benchmarks like #GLUE, dynamic frameworks such as #PandaLM, and adversarial/out-of-distribution (#OOD) testing. 🌟 Emerging Trends: Adaptive evaluation methods, ethical and safety-centric testing, and domain-specific benchmarks like #PubMedQA and #LSAT. 🌱 Future Challenges and Opportunities: Addressing scalability, environmental sustainability, and developing standardized evaluation protocols for multimodal and context-specific applications. This technical overview highlights the evolution of evaluation methodologies and underscores their role in building reliable, inclusive, and high-performing LLMs.

📚 #LLMs, being non-deterministic, challenge traditional monitoring approaches, making observability essential for understanding and optimizing their behavior in production. 📑 From tracking key metrics like latency and resource utilization to unraveling the nuances of input-output pairs through structured logging and tracing workflows, observability provides a comprehensive lens into LLM performance. It's not just about knowing what went wrong—it's about discovering why and ensuring robust, cost-effective, and user-centric operations. 🔑 Highlights of LLM Observability: 🔗 Embracing the #MELT Framework: Metrics, Events, Logs, and Traces form the backbone of effective observability practices. 🔗 Extended #Pillars tailored for LLMs, including prompts, feedback, evaluations, and retrieval analysis, ensure quality and relevance at scale. 🔗 Core Tools like #LangSmith, #SigNoz, and #Helicone empower teams to monitor, optimize, and refine LLM systems efficiently. 🔗 Proactive monitoring, iterative refinement, and ethical safeguards are shaping the future of observability. Dive into the full analysis and explore the practices, tools, and trends redefining #LLMobservability. 💡 Let’s build smarter, more transparent systems together: https://lnkd.in/d6NPBu6S