AI Post

Short headline: Retrieval-Augmented Generation (RAG) and vector databases are moving from experiments into production — and they’re changing how companies use AI for search, support, and decision-making. What’s happening now – Organizations are combining large language models (LLMs) with vector databases to build Retrieval-Augmented Generation (RAG) systems. – Instead of asking a model to “know […]

Short summary: Autonomous AI agents — software that can plan, act, and interact across apps — are moving from labs into real business use. Over the last 18–24 months organizations have started using agents to qualify leads, draft contracts, automate monthly reports, and run routine ops tasks. These agents combine large language models, connectors (CRMs,

Short summary AI agents — autonomous, conversational systems that combine large language models (LLMs) with tools, APIs, and company data — are moving from demos into real business use. Companies are using agents plus Retrieval-Augmented Generation (RAG) to let AI safely access internal documents, CRM records, and operational systems so the AI can draft client

Short summary Over the past year, many companies have moved from experimenting with public chatbots to building private LLM systems that connect to their own data via vector databases (retrieval-augmented generation, or RAG). The result: AI that answers questions using your internal knowledge, reduces hallucinations, and keeps sensitive data in-house. This trend is driven by

Quick take: AI “copilots” and autonomous agents that connect to a company’s apps and data are moving from demo stage into real business use. Companies are using RAG (retrieval-augmented generation), vector databases, and workflow orchestration to build assistants that draft emails, summarize meetings, surface customer insights, and automate repeat tasks — while leaders focus on

AI agents — autonomous, multi-step workflows powered by large language models — are moving from labs into the enterprise. Over the past year we’ve seen more platforms ship agent frameworks, more vendor integrations (CRM, ticketing, BI), and more pilots that replace repetitive human tasks like research, ticket triage, and report drafting. Why this matters for

Recent trend snapshot – Autonomous AI agents — software that plans, executes, and adapts multi-step tasks with minimal human direction — have moved from research demos into real business pilots. – Frameworks and toolkits (think LangChain-style agent patterns, vendors adding agent features to their stacks) are making it easier for teams to build agents that

Autonomous AI agents — software that can plan, act, and complete tasks across apps with little human input — are moving rapidly from labs into real business use. From researching prospects and drafting outreach to updating CRMs and scheduling demos, these agents win time back for sales teams and speed up repetitive workflows. Why this

AI agents—autonomous software that can read, decide, act, and connect to your systems—are the next big step in enterprise AI. Over the past year, more companies have moved agents out of research projects and into real workflows: customer triage, order processing, sales outreach, and internal knowledge assistants. These agents combine large language models, retrieval-augmented generation

AI trend summary Autonomous AI agents — software that uses large language models (LLMs) to plan, act, and complete multi-step tasks across tools and systems — are moving fast from labs and demos into real business use. Companies are using agents to automate customer triage, generate and reconcile financial reports, manage procurement workflows, and run