Massimo Bensi Tech Pills

Business automation is an essential part of modern businesses, and it does not have to be hard! Several automation tools exist, such as Zapier , Make ,  n8n  that allow automations between systems, services, databases, you name it! I recently created and published a n8n   workflow to deliver Google News to yourGmail inbox . This template serves business leaders and marketers and anyone who wants to track industry developments without wasting their weekly time on endless feed browsing.   Overview The workflow fetches daily GoogleNews digest based on your selected topics, before sending them to your Gmail inbox for verification, allowing you to select appropriate stories to share. NOTE: The workflow operates through community nodes which function exclusively on n8n self-hosted platforms. If you want to know how to easily run a self-hosted n8n instance it on your infrastructure, I will be happy to help you in a free call .   Why this matters Th...

When we talk about Retrieval-Augmented Generation (RAG) , we usually focus on accuracy: pulling the right documents, grounding responses, and ensuring users get reliable answers. But in enterprise settings, accuracy is only half the story. Guardrails —the policies and mechanisms that govern how AI systems handle inputs and outputs—are what make the difference between a safe, private system and a liability. If you’ve ever chatted with an AI that refuses to give you certain answers, or rephrases toxic language, you’ve seen guardrails in action. In enterprise RAG , these guardrails aren’t just nice-to-haves—they’re essential for data privacy, compliance, and confidentiality . In my previous post, Enterprise RAG: Turning Company Knowledge into an AI Assistant , I described how enterprises can safely expose their knowledge base through RAG. Guardrails are the next step: they ensure the AI assistant never leaks sensitive information, avoids compliance violations, and stays aligned with organ...

Artificial intelligence keeps advancing at a rapid pace yet most business leaders agree that AI systems function independently while being inflexible and cost-prohibitive to integrate within operational workflows. The Model Context Protocol (MCP) presents itself as a developing open standard which addresses this problem. MCP operates as an essential translation system which enables AI models to exchange information with the tools and data and workflow systems organizations currently use. Through its standard interface MCP enables models to communicate uniformly with enterprise systems thus eliminating the need for unique connectors and customized integrations. Why MCP Matters for Business Leaders Executives require only the business outcomes of MCP instead of technical implementation details under the hood. Faster Integration -  The process of integrating AI pilots into company operations currently requires extensive time periods and substantial financial investments from businesse...

Modern executives encounter a standard business problem because their organizations maintain vast amounts of data that employees and customers find difficult to obtain at the right time. Search tools are clunky, documents live in silos, and critical knowledge often gets buried. Enterprise Retrieval-Augmented Generation (RAG) represents the solution to this problem. Business organizations consider this AI architecture among the most vital because it revolutionizes their ability to access and utilize corporate knowledge. What is RAG? The RAG system unites two advanced technologies through its operations: Large Language Models (LLMs) including GPT demonstrate exceptional skills for processing and creating natural language content. Enterprise data retrieval functions as a system that retrieves information from authorized company sources. RAG systems retrieve the most suitable documents from your organization before the model generates an answer. The system generates answers based on you...

Once you setup the service in Azure and add all your APIs, you should make sure you have a good Backup strategy in place, in case things go wrong. If you do regularly backup the service, then you can also use those backups for a couple of purposes: you can for example deploy more than one instance and put a Traffic Manager in front of them, in order to handle geographical distribution . But you can also restore a specific backup to a different Subscription , and create a separate environment, as per the DTAP street . Azure API Managemen t provides a REST API that you can use to automate things; unfortunately that works for most features and activities, but not for backing up and restoring the whole service. For this you need to use the good "old" ARM , which also provides a REST API with specififc API Management operations. By reading the article above, you might think that this is as easy as getting a token , backing up and restoring the service... Well, ...

Here's a diagram to show what are the main components involved in an Azure AppService deployment and configuration, between the actual application and the Azure Active Directory setup. For those not familiar with Azure, it can get confusing between the concepts of Directory , Tenant , Subscription , and all the sub-components that need to be properly configured when deploying to Azure . Hopefully this high-level diagram can clarify a bit more. A few notes: you can create several Tenants under a single Directory . you can use Azure Active Directory in any Tenant (not necessarily the one where the application is deployed). you can use the basic Azure Active Directory authentication and authorization features for free (without a Subscription attached to it), but as soon as you need advanced features (let's say MFA ), then you need to buy one of the AAD Premium plans. You do need a Subscription for deployment of apps in this setup, a SSL Certificate is ...