How Pegasus O/AnyBot solves AI project pain points for enterprise clients

When enterprise clients come to us with AI projects, the goals vary, but the pain points are often the same. Long timelines, inconsistent outputs, a lack of control, and unclear handoffs between models and real-world use. That’s why we built Pegasus O/AnyBot, our internal AI framework. It provides our teams with a consistent foundation to move faster and solve problems without having to start from scratch every time. From natural language to predictive models, it helps us develop custom AI solutions that are focused, testable, and ready to scale. We’ve shared a quick breakdown of what it is, how it works, and where it fits into enterprise environments. Here’s the full post: https://ow.ly/Yaoj50WPLPX #AI #EnterpriseIT #SoftwareDevelopment #PegasusOne #MachineLearning #TechLeadership

RAG: Why It Matters in AI Right Now   AI’s biggest flaw? It still makes things up. That’s why everyone’s talking about RAG (Retrieval-Augmented Generation), the upgrade that makes AI smarter and more trustworthy. Retrieval-Augmented Generation (RAG) has become one of the hottest topics in AI because it tackles the biggest weakness of large language models, making things up. While AI models have gotten better at reasoning and writing, they don’t know everything and can hallucinate. RAG bridges that gap by giving models access to fresh, trusted information sources, so answers can be both fluent and grounded in fact. Instead of relying purely on what the AI was trained on, RAG adds a retrieval step. When you ask a question, the system searches a connected knowledge base and pulls back the most relevant snippets. The AI then uses these snippets as context when generating a response. In practice, that means the model is no longer answering from memory alone, it’s answering with live reference material at its side. Studies and industry benchmarks show that RAG can cut hallucinations dramatically. Depending on implementation, error rates often drop by 30–60% compared to using a language model alone. It’s not a silver bullet, bad sources still mean bad answers but RAG pushes LLMs much closer to being reliable tools for business, research and day-to-day productivity. I’ve created a tool to process large documents or bodies of text into smaller chunks with the required metadata. It’s available for free here - https://lnkd.in/ervJuyT7 #RAG #GenerativeAI #ArtificialIntelligence #LargeLanguageModels #DigitalTransformation #OpenSource #Innovation

✨ Embeddings: The Secret Language of AI ✨ We often talk about RAG, fine-tuning, or vector databases… but none of them would exist without one unsung hero: Embeddings. They are the hidden maps of meaning that let machines understand relationships between words, documents, and even images. Without embeddings, there would be no semantic search, no personalized recommendations, and no Retrieval-Augmented Generation. In my latest Substack article, I break down: 🔹 What embeddings are (in plain English) 🔹 Why they’re the backbone of RAG and vector databases 🔹 Real-world examples you use every day 🔹 The future of multimodal embeddings If you’ve ever wondered “how does AI actually understand meaning?”, this article is for you. #AI #MachineLearning #RAG #VectorDatabases #Embeddings #GenerativeAI #Kactii Kactii

Making the Constitution easier to explore with AI ⚖️✨ Here’s a quick demo of the AI Legal Assistant I’ve been building 🎥 With just a simple query, the system retrieves insights from over 400 pages of the Constitution of India in natural language. 💡 Powered by: 🔹BGE-large-en-v1.5 embeddings 🔹LLaMA 3.1 8B responses 🔹Vector similarity search for accurate retrieval 🔹LibSQL for fast, scalable queries ⚡ Key outcomes: 🔹Reliable chunk-to-embedding mapping 🔹Sub-second response time Try it out here 👉 https://lnkd.in/gF6pzRbB #AI #LLM #Embeddings #VectorSearch #AIModels #GenerativeAI #LegalTech #Demo #OpenSource

Sometimes smaller ideas make the biggest waves. The Hierarchical Reasoning Model (HRM) is a lean AI that’s outperformed some of the giants at reasoning tasks. It sparks an important question: is the future of AI about scale, or about thinking differently? Read my take here → https://lnkd.in/gJhH4X6P #HRM #ArtificialIntelligence #FutureOfWork #MachineLearning #AIResearch

What does it mean to "speak" #AI? One of the most helpful features of today's AI is the ability to ask a question or assign a task in plain language and get a response back. But speaking to AI is not the same as speaking to a human colleague. Getting the most from AI at work requires an understanding of what it is... and what is not. Here's an article about what AI fluency in the workplace: 📞𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: refining your prompts, knowing how to "tune the knobs"  ✔️𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻: testing outputs and matching tools to tasks. 🔃 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: refreshing playbooks as capabilities evolve. 🤖𝗛𝘂𝗺𝗮𝗻-𝗔𝗜 𝗦𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘀: pairing machine scale with human judgment and accountability. More here:  https://lnkd.in/gFhpmQzm https://lnkd.in/gFhpmQzm

RAG and MCP aren't necessarily competitors—they solve different problems. RAG makes AI smarter by giving it better access to information. MCP makes AI more useful by letting it take action on that information. In fact, many advanced AI systems use both approaches together. They use RAG to ensure accurate information retrieval and MCP to enable real-world actions based on that information. The choice between them depends on what you're trying to achieve: Do you want an AI that knows more, or an AI that can do more? Or better yet, why not have both?

When we talk about AI today, three powerful concepts stand out: 🔹 LLM (Large Language Models) – Powerful brains trained on massive amounts of data. They can understand and generate human-like text, code, or even reasoning. 🔹 RAG (Retrieval-Augmented Generation) – LLMs are smart, but they sometimes “hallucinate.” RAG connects them with external knowledge sources, so the answers are not only smart but also factually accurate. 🔹 Knowledge Graphs – A map of facts and relationships (who, what, how things connect). When paired with LLMs, they bring structure, context to AI outputs. 👉 Together, these three are shaping the future of intelligent applications—from search engines to enterprise AI assistants. ✨ On a personal note: I’m currently learning and implementing AI, and every day I explore new foundational concepts, read, and experiment. A big thanks to my friend Anup Kasat, with whom I brainstorm on RAG — our discussions really help us grow our knowledge by sharing and learning together. 💡 In my upcoming posts, I’ll try to break down these three building blocks in simple words, based on my own learning journey. Stay tuned! #AI #LLM #RAG #KnowledgeGraph #ArtificialIntelligence #FutureOfWork #LearningJourney #Collaboration

Unlocking Factual AI: Why RAG is a Game-Changer Ever ask a Generative AI a question and receive a confident, yet completely fabricated answer? This "hallucination" challenge is a significant hurdle for enterprise AI adoption. But what if we could ground these powerful models in verifiable, up-to-date information? Enter **Retrieval Augmented Generation (RAG)** – a revolutionary approach combining the best of retrieval systems with the generative power of Large Language Models (LLMs). Instead of solely relying on their pre-trained knowledge, RAG systems first *retrieve* relevant, accurate data from an external, trusted source (like your company's documents, databases, or the latest research). This retrieved context is then fed to the LLM, enabling it to generate responses that are not only coherent but also factually grounded and specific to the provided information. This isn't just about reducing errors; it's about increasing trustworthiness, making AI more useful for critical applications, and allowing LLMs to access real-time, proprietary data they were never trained on. Think improved customer support, more accurate research assistants, and data-driven decision making. How are you integrating RAG into your AI strategy, or what opportunities do you see it creating for your industry? Share your insights below! #AI #RAG #GenerativeAI #LLMs #ArtificialIntelligence #TechInnovation #MachineLearning #EnterpriseAI

AI is helping teams move faster than ever - from natural language to code to automated workflows. But speed only creates value if you’re heading in the right direction. Following on from our last blog on natural language to code, this post looks at why planning makes that speed work - leading to AI projects that are better, faster and smarter. https://lnkd.in/e_p2z3EU #ai #productdelivery #digitalproducts #productmanagement #softwaredevelopment