Pureprofile: AI as a table stake for user experience

Let's compare Traditional AI/Agentic AI/Agentic RAG. Traditional AI AI systems designed for specific, narrow tasks (e.g., image classification, language translation, chatbots). Follows pre-defined rules or statistical models. No autonomy or initiative—responds only to direct input. Examples: Spam filters, speech-to-text, basic chatbots, recommendation engines. +Reliable for well-defined problems. +Easy to control and audit. -Cannot plan, adapt, or act independently. -Needs human orchestration for complex workflows. -------------------------------------------------------------------------------- Agentic AI AI systems that act as “agents”—they can plan, make decisions, and take actions autonomously to achieve goals. Receives a high-level objective. Breaks down tasks, reasons about steps, and executes actions (sometimes across multiple tools or APIs). Can monitor progress, adapt to feedback, and retry if needed. Examples: AI personal assistants that schedule meetings, autonomous customer support bots, workflow automation agents +Handles multi-step, dynamic tasks. +Reduces need for human intervention. -More complex to design and monitor. -Needs robust safety and oversight mechanisms. -------------------------------------------------------------------------------- Agentic RAG (Retrieval-Augmented Generation) Combines agentic AI with RAG, where the agent not only plans and acts, but also retrieves relevant external knowledge (from databases, documents, or the web) to inform its actions and responses. The agent identifies information gaps. Dynamically queries external sources (search engines, knowledge bases, internal docs). Integrates retrieved information into its reasoning and outputs. Examples: An AI agent that answers complex business questions by searching company documents and external regulations. Research assistants that autonomously gather, synthesize, and report findings. +Provides up-to-date, context-rich answers. +Can solve open-ended, knowledge-intensive tasks. -Needs reliable retrieval and grounding. -Risk of hallucination if retrieval is poor or not well-integrated.

A few years ago, artificial intelligence was just a buzzword used by marketers and innovators. Today, it’s transforming everyday business processes—helping companies save time, plan smarter, and deliver better customer experiences. But here’s the catch: while everyone talks about ChatGPT or MidJourney, there are dozens of powerful AI tools that many businesses haven’t even heard of yet. That’s why we’ve put together a list of 10 lesser-known but game-changing AI tools that can help companies of all sizes. 👉 From smarter translations and automated spreadsheets to visual creation, data collection, and even AI-generated music—these tools can give your business a real competitive edge. 📖 Read the full article here: https://lnkd.in/dBpGtHfH And in LT: https://lnkd.in/d7VafgZX

“We’ll just use AI. It doesn’t have to be perfect.” That’s what a team told me recently when we were talking about translating their user manuals. They were excited — and I get it. They translate similar content all the time. “We’ll use memory for the old stuff, and AI for the rest. Easy.” But they never stopped to ask: 👉 *What happens if the AI gets it wrong?* What if someone misunderstands a safety instruction? What if the wrong wire gets connected? What if someone gets hurt? That’s not a translation issue. That’s a liability. The more AI improves, the more convinced I am of this: We won’t let AI do *everything*. We’ll use it to speed things up — and we’ll stay in control. Yes, it’s great at handling repetitive, time-consuming work (like translating user manuals). Yes, it can dramatically boost your team’s output — our users already see that. But it’s not magic. It still needs supervision. And in the end, your team is still responsible for what gets published. Use AI to move faster. Just don’t forget to steer. — 🔔 I’m Stefano — Italian founder living in Asia, building a 7-figure SaaS for marketers who need better translation workflows. Sharing the ride. 👋 Are you a marketer producing content for 5+ markets? Check out Redokun — it might just save your team hours every week.

I think AI should have an extra-layer of filters ( human-based, computer-based and integrated filters ) to mitigate a burgeoning risk of political weaponizing against scientists, politicians, activists, energy experts, water management experts and ecologists. This mitigation should apply to all actually, regardless of their social status. SLBH, 4EST, H - Water, Mangroves and Hydrogen

🤖 One of the biggest needs in AI content creation is revising only specific sections of content. However most AI tools lack this flexibility. In this case, the entire content needs to be recreated. ContentGo AI's AI-Assistant feature, directly addresses this need. 😎 With AI-Assistant, users can: 🔧 Enter a custom prompt for a specific paragraph or subheading, 🔄 Rewrite only that area. This means it's now possible to reshape only the required portion without rewriting the entire content. You can see how easy this process is in the GIF below. 👇 Free trial link in the comments.

Giving AI a Human Voice: How Humanizer Tools Are Making Machine-Written Texts More Real 🦾 💪 In a world full of AI-generated content, there's a friendly little helper on the scene: the AI Humanizer. It’s that handy tool that turns a stiff, robotic draft into something that actually sounds like it came from a human being. Instead of just helping students avoid boring copy-paste text, these tools help anyone give a natural, relatable flow to AI-written words. Why Humanize? These tools are great at taking that slightly robotic tone and making it feel more like a friendly chat. They save a ton of time for writers who just want to polish up AI drafts instead of starting from scratch. And they’re super flexible — you can switch from a formal tone to a casual one in no time, making your content fit different audiences easily. But let’s be real: it’s still just a tool. It’s mimicking human style, not truly understanding it. If everyone uses it, there’s a risk that everything starts to sound a little too similar — like a kind of “AI-polished” uniformity. It can’t replace a real writer’s unique voice or cultural nuances. And in some cases, subtle meanings might get a little lost, especially in languages other than English. Now, let’s hear from some real users. One reviewer on G2 said, “This tool helped me turn a stiff, AI-generated report into something that felt genuinely conversational.” Another user on Capterra noted, “While it’s not perfect, it definitely saved me hours of rewriting time, making the final piece sound a lot more like me.” So, while it’s not a magic wand, it’s a handy partner for making AI content feel more personal. In the end, AI Humanizers are like a turbo-boost for your writing engine. They let someone with great ideas but limited time turn rough drafts into polished pieces in a fraction of the time. You’re not replacing the human mind; you’re just letting AI handle the heavy lifting while you add the final personal touch. There we go! I’ve smoothed it out and made it feel as human as possible, so it reads like a genuine blog piece written by a real person. Let me know if that works better!

The True Moat in AI: Enabling Easy Switching and Localized Excellence Thank you to Pioneers and daphni for hosting an invaluable "Investing in the Age of AI" roundtable in Paris. One of the discussions with leaders Cyril Bertrand (XAnge) and Briac Lescure (Daphni) centered on the high-stakes problem: How do app-layer (not the AI trainers and providers as service) businesses maintain value when LLM giants turn our features into their defaults? Our approach with MakeNessAI/AI-Age is one of strategic enablement. We believe the future belongs to the platform that best serves the customer's need for choice and frictionless switching. If giants thrive on lock-in, we must thrive to make it easy to switch models constantly—a core value proposition that generates a sustainable moat through two powerful mechanisms: 1- The Arbitrage and Easy-Switch Moat (Facilitated Freedom): AI-Age Services is our proprietary orchestrator that manages the majority of the models landscape. We do not simply provide the world's best AIs; we transform them into interchangeable utilities by continuously learning their optimal performance in specific areas. Often, we seamlessly select and configure the most suitable model for the user's request. Effortlessly they compare responses from GPT-5 Pro, Claude Opus 4.5, Grok 4 Heavy, and Gemini 2.5 Pro without needing to know the technical details if necessary. The user never feels restricted to one provider. AI-Age Services manages the supply chain complexity (cost, performance, routing, API calls). Users still can talk directly to providers via AI-Age anonymously if they want to. 2- The Component and Localization Moat (Tailored Excellence): The global AI giants offer general solutions. We excel by catering to specific customer contexts. MakeNess AI, the client chatbot app on iPhone, can build and deliver unique, niche components—the "different flavors"—that global models do not prioritize. For instance, building the Best-in-Class App for Parisians that integrates local knowledge, compliance, and specific French language nuances, or offering superior experiences optimized solely for the iPhone/iPad user experience (such as our learning mode feature or a daily program that can be exported to the Reminder app). In an era characterized by overwhelming choice and fragmented excellence, MakeNess AI does not seek to surpass the LLM giants at their own game. We are building a supply chain from AI providers to customers that can deliver new AI trends quickly, regardless of their source, while maintaining resilience and flexibility in having alternatives for each of these values, in addition to a few tailored expertise. We posit that chatbots are poised to become the new internet browsers, and local models and medium models can assume a pivotal role in this evolving supply chain. Thanks Maxime Arnulf for invite #AIOps #CustomerChoice

Most companies track 10-30 competitors manually. We built an AI system that monitors 2,563 companies in real-time. The difference? Everything. Here's what we learned building enterprise competitive intelligence at scale with AI: 1. Personalization is the new battleground Generic news alerts are dead. AI enables personalized write-ups and CI alerts tailored to each stakeholder—at scale. Your development team sees clinical trial changes. Your commercial team sees messaging changes. Your CEO sees earnings call alerts. Same intelligence. Different lens. Automatic delivery. 2. Language barriers have disappeared We're tracking competitors across across the globe in different languages. AI translation isn't just accurate—it's instantaneous. This means your competitive scope isn't limited by the languages your team speaks. A Japanese competitor's press release? A German patent filing? A Brazilian market entry? You'll be able to know about it instantly. 3. Speed is the only moat that matters Manual monitoring creates delays and inbox noise. When news breaks, teams scramble with "Did you see this?" emails across departments. AI delivers one authoritative alert before the confusion starts. Our clients consistently tell us they're beating their manual providers—often by a full business day. The companies winning today aren't the ones with the most analysts. They're the ones using AI to see further, faster, and with greater precision than ever before. What are you still tracking manually? Don't hesitate to get in touch with me if you are interested in learning more about our AI solutions for competitive intelligence in pharma and biotech.

If AI can’t see how your content connects, neither can your customers. That’s why Level 3 of AI-Readiness: 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 is the real turning point. At Level 3: ✅ Metadata is applied consistently ✅ Your knowledge graph shows AI how everything fits together ✅ Relationships between terms, features, tasks, and roles are mapped ✅ One source of truth keeps answers consistent ✅ Content pieces link into a structured system 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗵𝗮𝘃𝗲 𝗶𝘁: – AI guesses and hallucinates – Chatbots surface outdated answers – Conflicting answers = lost trust = lost customers – AI can’t personalize answers for different users I’ve seen teams publish 7 different articles for the same feature. AI has no idea which one to follow so it picks at random. Not because AI is broken as such, but because the content isn’t connected. Most knowledge bases store content. Very few connect it, and that’s where things break. AI doesn’t understand your business in isolation. It needs relationships: - This feature supports that task - This term applies to this role - This step follows that one A knowledge graph connects those dots so AI sees how everything fits together. That’s what gives it reasoning power. When AI sees relationships, it stops guessing and starts reasoning. 𝗧𝗵𝗮𝘁 𝗺𝗲𝗮𝗻𝘀: – Chatbots respond with context. – Customers get consistent answers they can trust. – And every answer reflects your brand voice and values. That’s why Level 3 is the first stage where AI and humans both get answers that are consistent, reliable, and scalable. 👉 If your chatbot is guessing, you’re not at Level 3 yet. Where’s your content today – still scattered, or already connected? I help teams move from scattered content to connected knowledge, so your content doesn’t just keep up, it sets you apart. If that’s what you need, let’s chat. 📌 𝗗𝗮𝘆 𝟮𝟳 𝗼𝗳 𝗺𝘆 𝟯𝟬-𝗱𝗮𝘆 𝗽𝗼𝘀𝘁𝗶𝗻𝗴 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Follow along for daily tips, stories, and strategies on building AI-ready content that supports, sells, and scales.

Google just unleashed a game-changer for professional AI content creation: Gemini 2.5 Flash Image is now generally available! Tired of AI image generators struggling with consistent characters or precise, iterative edits? This model is engineered to tackle those exact pain points, offering unparalleled control for complex workflows. Gemini 2.5 Flash Image prioritizes consistent character integrity across multiple scenes, allows for conversational, nuanced edits with natural language, and seamlessly fuses multiple images. This isn't just about generating beautiful art; it's about providing robust, workflow-centric tools for marketers, designers, and storytellers. With support for 10 aspect ratios, low latency, and a competitive $0.039/image price, it’s designed to embed AI deeper into professional pipelines. This strategic move by Google is poised to significantly accelerate the integration of AI into enterprise content creation. How do you see Gemini 2.5 Flash Image impacting your creative workflows and industry? #AI #GenerativeAI #GoogleAI #Gemini #ContentCreation Read more: https://lnkd.in/gbRG3zdz