Prem Articles

No Code–Tool Calling on the Prem Platform

Transform conventional chatbots into sophisticated AI assistants with Prem’s No Code–Tool Calling. This solution seamlessly integrates with services such as Slack, Google Calendar, and GitHub, automating scheduling, messaging, and data updates while eliminating complex coding and OAuth challenges.

Prem Articles

Small Language Models (SLMs) for Efficient Edge Deployment

Small Language Models (SLMs) deployed on edge devices overcome cloud dependency by reducing latency, bandwidth, and privacy risks. Explores quantization, pruning, model optimization, and efficient inference for edge computing and energy efficiency.

LLMs

How to Succeed with Custom Reasoning Models?

Explore key strategies to successfully develop and optimize custom reasoning models. Learn how explicit reasoning structures, reinforcement learning techniques, advanced evaluation metrics, and optimized deployment enhance AI's logical inference and problem-solving capabilities.

LLMs

SLM vs LoRA LLM: Edge Deployment and Fine-Tuning Compared

A concise overview comparing the advantages and limitations of full fine-tuning Small Language Models (SLMs) versus LoRA-based fine-tuning of Large Language Models (LLMs). The article covers inference efficiency, quantization methods, robustness and deployment strategies on constrained hardware

series Featured

DeepSeek R1: Why Open Source Is the Future of Enterprise AI Development

Open-source vs. closed-source AI stands at a turning point: once dominated by proprietary systems, the field now sees open science and models like DeepSeek R1 driving forward. Leveraging new training efficiencies, these open solutions match or even surpass their closed-source counterparts.

Featured

PremAI Autonomous Fine-tuning System: Technical Architecture Documentation

The Prem AI Autonomous Fine-Tuning System optimizes Small Language Model (SLM) fine-tuning with automated data augmentation, distributed training, and LLM-based evaluation. It minimises manual effort through multi-agent orchestration, hierarchical task classification, and active learning loops.

News Featured

PREM and AWS Join Forces

PREM and AWS have joined forces to deliver an accelerated Generative AI developer experience on both AWS and the PREM platform. To kick off this milestone, PREM co-organized a GenAI Hackathon with AWS in Barcelona, where AWS's ProServe team collaborated with PREM's team to tackle complex

Prem Articles

Chatbots vs. AI Agents – Which is Right for Your Business?

Chatbots vs. AI agents: Which is the best choice for customer support? This article explores key differences, workflow automation, real-time AI responses, and decision-making criteria to help businesses implement the most efficient AI-powered solution for enhancing customer experience.

Prem Articles

Enterprise AI Trends for 2025: What's Next for Businesses?

In 2025, enterprises will accelerate Edge AI adoption, leverage multimodal AI for enhanced analytics, and implement Multi-Agent Systems for efficient automation, emphasizing sustainability, governance, explainability, and workforce readiness in AI deployment strategies.

SQL

PremSQL: Towards end-to-end Local First Text to SQL pipelines

PremSQL is a local-first, open-source Text-to-SQL solution that ensures data privacy and control by avoiding third-party models. With support for small language models, PremSQL simplifies natural language querying and provides autonomous, AI-driven data analysis.

SQL

Evaluating LLMs for text to SQL with Prem text2sql

Explore the latest Text-to-SQL advancements in our new blog. We evaluate top models like GPT-4o and Llama using the newly released text2sql package on BIRDBench. Learn about Execution Accuracy, Valid Efficiency Score, and how open-source models are challenging closed-source alternatives.

LLMs

Open Source Reasoning Models: Welcome Qwen QwQ and QvQ

Open-source reasoning models Qwen QwQ and QvQ represent a shift in AI from generation to structured reasoning. With transparency, multimodal capabilities, and fine-tuned performance, they set benchmarks in logical problem-solving, advancing industries like finance, healthcare, and education.

Fine-tuning Embeddings for Domain-Specific NLP

Fine-tuning embeddings are crucial for enhancing domain-specific NLP applications. General models may fall short in specialised fields like healthcare or law. By fine-tuning, models improve accuracy, relevance, and understanding of specific terminologies, ensuring better performance in niche tasks.

News

2024 AI Wrapped: Innovations, Challenges, and What’s Next for PremAI

2024 marked a transformative year in AI, with breakthroughs from OpenAI, Anthropic, and Meta, the rise of open-source models like Llama 3.1, and the emergence of advanced reasoning systems such as DeepSeek R1. Discover how innovations, challenges, and PremAI’s contributions.

LLMs

Open Source Audio Models: Text-to-Speech and Speech-to-Text

Open-source frameworks like BASE TTS, ESPnet, and FunASR are transforming Text-to-Speech and Speech-to-Text technologies in 2025. With advancements in scalability, natural prosody, and low-resource deployment, these tools make high-quality speech AI accessible and customizable globally.

Prem Articles

Open Source Agentic Frameworks: LangGraph vs CrewAI & More

Open-source agentic frameworks like LangGraph, SmolAgents, CrewAI, PhiData, and Composio enable multi-agent AI systems with scalable, modular architectures. Key features include graph-based workflows, retrieval-augmented generation, hierarchical planning, and collaborative task allocation.

LLMs

Fine-Tuning & Small Language Models

The era of GPT-4's dominance is ending as diverse, specialized language models take the stage. Companies now favor both open-source and proprietary models tailored to specific tasks, moving away from one-size-fits-all solutions. Small Language Models (SLMs) offer efficiency for niche needs.

LLMs

Multilingual LLMs: Progress, Challenges, and Future Directions

Multilingual LLMs face challenges like cross-lingual knowledge barriers, data imbalances, and performance disparities in low-resource languages. Key advancements include multilingual fine-tuning, retrieval-augmented generation (RAG), and adaptive architectures.

LLMs

Balancing LLM Costs and Performance: A Guide to Smart Deployment

Balancing LLM costs and performance requires strategies like dynamic model routing, hybrid deployment, and fine-tuning smaller models for specific tasks. Techniques such as token optimisation, caching, and leveraging open-source models help reduce expenses while maintaining efficiency.

LLMs

Are Agentic Frameworks an Overkill?

Agentic frameworks offer advanced adaptability and automation but come with high complexity and cost. This article explores their benefits, limitations, and practical alternatives to help you decide whether they are the right solution for your AI and automation needs.

LLMs

Edge Deployment of Language Models: Are They Ready?

Edge deployment of LLMs promises low latency, privacy, and real-time insights. This article explores the challenges, cutting-edge solutions, and future opportunities to make edge-based AI a reality across industries like healthcare, robotics, and IoT

LLM Routing: AI Costs Optimisation Without Sacrificing Quality

LLM routing revolutionizes AI deployment by dynamically assigning tasks to the best models, reducing costs by up to 75% while maintaining high-quality outputs. Explore its role in customer support, content creation, and developer tools, and learn how it optimizes efficiency across industries.

LLMs

Is the current AI agents ecosystem again a Hype?

The AI agent ecosystem is rapidly evolving, blending autonomy and adaptability to transform industries. This analysis delves into their development, real-world applications, and challenges, highlighting advancements in decision-making, efficiency, and integration.

LLMs

What is a MicroSaaS and How to Get Started with Making One Using AI

AI is transforming MicroSaaS by automating development, enabling personalization, and optimizing scaling. Tools like PREM-AI help developers create scalable, niche-focused solutions faster and with less effort. The future includes AI-native workflows and hyper-personalized features

LLMs

LLMs Evaluation: Benchmarks, Challenges, and Future Trends

The evaluation of Large Language Models (LLMs) focuses on benchmarks, scalability, ethical challenges, and multimodal testing. Dynamic frameworks and emerging trends drive robust, adaptive AI performance, ensuring safer, efficient deployment in sensitive fields like healthcare, finance, and law.