Ron Mitchell

AI news snapshot: Companies are increasingly pairing private, fine‑tuned large language models (LLMs) with Retrieval‑Augmented Generation (RAG) and vector databases to answer business questions from their own documents. Instead of relying solely on a general internet-trained model, firms feed their internal knowledge—contracts, product specs, support tickets, SOPs—into a searchable vector index. The LLM retrieves relevant […]

The trend: Autonomous AI agents and enterprise copilots are moving from demos into real business processes. Companies are combining large language models, retrieval-augmented generation (RAG), and workflow orchestration to build “agents” that can read documents, run systems, summarize results, and take follow-up actions — often across multiple tools. Why it matters for business leaders –

Quick summary – Businesses are adopting vector databases (Pinecone, Milvus, Qdrant, Weaviate) plus Retrieval‑Augmented Generation (RAG) to power next‑gen search, knowledge management, and AI agents. – Instead of relying on static keyword search or fine‑tuning heavy models, companies store embeddings (numerical representations of text, docs, and other data) in vector DBs and use LLMs to

Autonomous AI agents — software that can act, plan, and complete tasks with minimal human direction — are moving fast from research demos into real business use. Over the last year, major AI platforms and toolkits have added agent frameworks and integrations that make it easier to automate workflows end-to-end. That shift matters for leaders

Big idea: Autonomous AI agents — sometimes called “copilots” or agent platforms — are moving from demos into real business use. Companies are using agents that can read emails, pull CRM data, run reports, and start routine processes without constant human prompting. Vendors and open-source projects (from enterprise copilots to agent frameworks) are making these

Quick summary Companies are increasingly using Retrieval-Augmented Generation (RAG) — pairing large language models with vector databases — to build AI agents that answer questions using a company’s own documents, product data, and workflows. Instead of relying only on a generic model, RAG finds the most relevant internal content, feeds it to the model, and

Quick summary Enterprises are increasingly building private large language model (LLM) assistants using retrieval-augmented generation (RAG) and vector databases. Instead of asking a generic public chatbot, employees can query a secure, company-specific AI that searches internal documents, pulls in the most relevant facts, and answers with sources. This trend is being driven by falling costs

Big idea in plain terms: Enterprise “AI copilots” — custom AI assistants built on large language models and connected to a company’s own data — are moving from concept to day-to-day reality. Teams in sales, support, HR, and operations are using copilots to draft proposals, summarize customer histories, automate routine workflows, and surface insights from

Quick summary AI agents — autonomous, LLM-driven systems that can carry out multi-step tasks, talk to apps, and make decisions — moved from labs into real business use in 2024. Companies are using agents to run outreach, triage customer issues, build reports, reconcile finances, and automate repeatable ops work. The shift is less about replacing

AI agents (LLMs that act, call tools, and make decisions) are now pairing with Robotic Process Automation (RPA) to automate complex business workflows end-to-end. Major platform vendors have pushed this shift by embedding copilots into automation stacks and giving models safe access to apps, documents, and APIs. The result: bots that don’t just click buttons