RAG

From RAG to TAG: Document-Centric RAG to Table Augmented Generation

By |February 25, 2026|Tags: , |

Key Takeaways *Drawbacks of document-centric RAG: Most of the most valuable business insights reside in structured data sources; your relational databases and tables. If you’re focusing solely on document-centric RAG, you may be missing out on the goldmine in your own transactional systems. * Building a TAG solution in-house [...]

Beyond the Hype: Why ChatGPT 5.2 Needs a Semantic Layer to Survive the Enterprise

By |January 22, 2026|Tags: , , |

Key Takeaways ChatGPT 5.2 offers 70% success rates on various complex tasks compared to global white collar workers, but still struggles with business data. The MCP Limitation: MCP transmits raw data without transmitting understanding, leading to plausible but incorrect answers. The Danger of Close Answers: Without a semantic layer, [...]

The Agentic AI Blueprint is a Trap: Why We Spent 2 Years Building eRAG, So You Don’t Have To

By |January 15, 2026|Tags: , |

Key Takeaways * The "11 Steps of Agentic AI System Design" is deceptive, suggesting a simple ‘Digital Employee’ built from basic components like a Vector DB and an LLM router. * eRAG's Federated Query Engine unifies data by abstracting complex storage (Oracle, PostgreSQL, MS-SQL) into a single view. * [...]

Ensuring Robust Data Security and Compliance

By |January 5, 2026|Tags: , , |

Key Takeaways * Extreme Data Isolation: eRAG ensures complete data isolation between customers through isolated deployments, dedicated Kubernetes namespaces, and separate, enterprise-grade LLM vendor accounts. * No Customer Data for Training: GigaSpaces commits to not using any machine learning library or training any models on customer data. * Security [...]

Conversational Databases: Your Database Now Speaks English

By |December 29, 2025|Tags: , |

Key Takeaways *  Data Access Democratized: Conversational databases let users query data in natural language, removing the need for SQL knowledge * The Critical Semantic Layer: Acts as a translator, transforming complex data structures into familiar business language and uses a dynamic knowledge graph to accurately interpret user intent [...]

Beyond Automation: How AI Is Quietly Rewriting the Way You Think, Decide, and Compete

By |December 22, 2025|Tags: , , |

Key takeaways * The Critical Component is Meaning: For AI to be effective for businesses, it must be supported by a system that exposes structured data with meaning, including field definitions, relationships, constraints, and business rules, enabling it to produce trustworthy insights. * To Start, focus on Individual Decisions: [...]

How to Supersede Business Planning with GenAI, Operational Data & Crowd Wisdom

By |December 15, 2025|Tags: , |

Key Takeaways * RAG is Insufficient for Modern Enterprises: Standard RAG fails with complex business data due to blindness to structured data, latency at scale, and lack of sufficient governance. * The Semantic Layer is Key: This central data layer enables LLMs to think like a business, by mapping [...]

2026 Enterprise AI Trends: What’s Next After the Demo Era

By |December 10, 2025|Tags: , , |

Key Takeaways| * AgentOps and Runtime Governance: Operational maturity, led by AgentOps (lifecycle management) and runtime governance enforcement, not just documentation, is a core buying criterion. * Orchestration is a Core Layer: Enterprises running multiple models need orchestration as an essential architecture layer for risk management and routing tasks [...]

From TAG to ATAI: The Rise of Agentic Table-Augmented Intelligence

By |November 11, 2025|Tags: , , , |

Key Takeaways * ATAI builds upon TAG's foundation, which innovates by applying Retrieval Augmented Generation (RAG) on metadata (database schemas, relationships, lineage) and enhancing it with GraphRAG to create a semantic network of relational structures. * With ATAI, AI becomes an active collaborator that understands intent, context, and consequence, [...]

Bridging the Gap: How RLHF, RAG and Instruction Fine-Tuning Shape the Future of Aligned AI

By |May 16, 2025|Tags: , |

Key takeaways 1. Combining Techniques for Truly Aligned AI Using pretraining, instruction tuning, RAG and RLHF together enables developers to build AI systems that are capable, trustworthy, and aligned with human needs 2. Benefits of RLHF Aligns AI outputs with human values, improves coherence and relevance, mitigates biases performs [...]

Building the Future of Intelligent Systems: The Synergy of AI Databases, Design Patterns, and Reasoning Engines

By |May 5, 2025|Tags: , |

What’s under the hood of an intelligent system? In this blog we’ll explore AI Databases, AI Design Patterns, and AI Reasoning Engines, the essential pillars of these systems, and enable the development of intelligent systems that are both powerful and adaptable. Unlike traditional databases, AI databases handle the vast [...]

Will AI Replace You?

By |April 24, 2025|Tags: , |

The question of whether AI will replace professionals is a pressing concern in today's rapidly evolving technological landscape. Recent research from Anthropic has delved into this issue, analyzing millions of interactions with Claude AI to assess which professions are most vulnerable to being replaced by AI. For many professionals, [...]

How Natural Language is Transforming Data Discovery

By |April 20, 2025|Tags: , |

“Reports that say that something hasn't happened are always interesting to me, because as we know, there are known knowns; there are things we know we know.” — Donald Rumsfeld, former Secretary of Defense, USA It’s not every day that a political soundbite ends up reshaping how we think [...]

Computer Using Agents – CUA Demystified

By |April 14, 2025|Tags: , |

As AI continues to revolutionize industries, one fascinating advancement is the ability of agents to interact with and learn from what they see on-screen. Until now, agents have relied primarily on backend APIs or documentation. This new generation of agents - Computer Using Agents (CUAs) — operate much like [...]

Enhancing AI Reasoning and Transparency: Exploring Chain of Thought Prompting and Explainable AI

By |April 7, 2025|Tags: , |

As AI systems become more sophisticated and integrate into critical decision-making processes, the need for these systems to be not only intelligent, but also transparent and understandable has become paramount. The ability to comprehend the reasoning behind an AI's output fosters trust, enables accountability, and ultimately leads to more [...]

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