📢 Introducing Galileo AI, the newest member of your product team — reinventing how digital product teams create great digital experiences. In today’s digital-centric world, product teams are tasked with owning critical KPIs: conversion, adoption, retention, and more. When those KPIs don’t perform as expected, teams dig through massive amounts of data to figure out why. 🔬 🔎 Too often, though, those teams fail to find the root cause of a conversion or adoption problem because the important issues are buried in all their data. 🤯 😕 That’s where Galileo comes in: 🕵♂️ Galileo watches every user session in your app 🤔 Gains a human-like understanding of your app and its flows 🗣 Surfaces the most impactful user struggle and behavior patterns 🎯 Guides teams on where to invest for creating great user experiences 📺 Each issue comes with session replays to see exactly what went wrong Find out why Common App (yes, the site used by millions of students to apply to colleges) uses Galileo to identify critical issues impacting students, saying: “In the past, we might hear that something was wrong with our application submission flows, but figuring out the root cause and its impact was a time-consuming process, and we couldn’t be sure that the changes we made would move the needle. LogRocket’s AI capabilities easily allow teams across Common App to proactively identify the highest-impact issues and their causes in the areas of our app we care about the most.” Read the whole blog post here: https://lnkd.in/esn2Kb5W
LogRocket’s Post
More Relevant Posts
-
while deploying LLM's is still the major challenge. I believe generative AI should be more real-time focused. Currently GPT still holds most valid data until 2021 September. The more users reply on such systems to seek answers(another misnomer) the more they will be fed wrong answers. now for the the misnomer, Chat GPT was never meant to be a search engine or question answering machine. It was meant to help make tasks easy eg generating essays, resumes, travel plans, writing emails etc. But people mostly see it as the next Google which it is not. Generative AI focuses more on auto generation not auto thinking or abstract thinking for that matter
AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
Why aren’t we living in a Generative AI-powered utopia yet? LLMs are harder to implement and deploy than most businesses expect. It takes 6-12 months to come up to speed, develop, and release a customer-facing Generative AI app. It’ll take longer if the business’s data isn’t curated to support advanced model training. For internal use cases, it can take as little as 2-3 months to deliver reliable solutions. Products are much harder because they must be more reliable and deliver a superior user experience. Internal-facing LLM apps can get away with cutting corners that a customer won’t tolerate. Publicly available apps must also account for adversarial scenarios and intentional misuse. Mastering Generative AI for internal use cases is the easy part. If the business is planning to build for customer-facing products, add time to account for the differences. Ignore the hype about how easy it is to build LLM apps when it comes to customer-facing products. It’s a completely different process, and development doesn’t end once the product ships. There truly is no finish line. #generativeAI #aistrategy #productmanagement
-
-
Leveling up pragmatic Data Scientists | AI Strategy | AI Product | LinkedIn Learning Instructor
One of the most important lessons I learned in the last year? An AI app doesn't necessarily mean an AI product. I've seen models built for AI apps that are technically brilliant but lack product market fit. Technical skill improves a great product and optimizes it. But it must be built with the product in mind. Great AI products need more than just AI capabilities. They require market viability and user necessity. it's quite possible to build an AI model without a product. The right strategy in AI development begins with setting a solid foundation. This involves identifying a clear need, ensuring usability, and establishing monetization potential. Only then should you start with a simple, functional version. AI apps need to focus on functionality first. They should have simple, usable features that can develop into more advanced ones, increasing the product's value. Always start with functionality and build around it. This is the cornerstone of successful AI product development. Start with functionality. Build around it. #datalife360 #productmanagment #datascience #ai
-
It's sooo much fun to build things with exciting, cutting edge technology! But businesses need the whole life-cycle to be taken care of. Risk-management, safety, validation, operations. The list goes on. Just building it is the easy part. #ai #genai #business #lifecycle #operations #technology #productmanagement
AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
Why aren’t we living in a Generative AI-powered utopia yet? LLMs are harder to implement and deploy than most businesses expect. It takes 6-12 months to come up to speed, develop, and release a customer-facing Generative AI app. It’ll take longer if the business’s data isn’t curated to support advanced model training. For internal use cases, it can take as little as 2-3 months to deliver reliable solutions. Products are much harder because they must be more reliable and deliver a superior user experience. Internal-facing LLM apps can get away with cutting corners that a customer won’t tolerate. Publicly available apps must also account for adversarial scenarios and intentional misuse. Mastering Generative AI for internal use cases is the easy part. If the business is planning to build for customer-facing products, add time to account for the differences. Ignore the hype about how easy it is to build LLM apps when it comes to customer-facing products. It’s a completely different process, and development doesn’t end once the product ships. There truly is no finish line. #generativeAI #aistrategy #productmanagement
-
-
Anybody can develop AI solution but most can’t run it in prod. That’s why MLOps is so much important and automation is the key
AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
Why aren’t we living in a Generative AI-powered utopia yet? LLMs are harder to implement and deploy than most businesses expect. It takes 6-12 months to come up to speed, develop, and release a customer-facing Generative AI app. It’ll take longer if the business’s data isn’t curated to support advanced model training. For internal use cases, it can take as little as 2-3 months to deliver reliable solutions. Products are much harder because they must be more reliable and deliver a superior user experience. Internal-facing LLM apps can get away with cutting corners that a customer won’t tolerate. Publicly available apps must also account for adversarial scenarios and intentional misuse. Mastering Generative AI for internal use cases is the easy part. If the business is planning to build for customer-facing products, add time to account for the differences. Ignore the hype about how easy it is to build LLM apps when it comes to customer-facing products. It’s a completely different process, and development doesn’t end once the product ships. There truly is no finish line. #generativeAI #aistrategy #productmanagement
-
-
CEO and Founder at MantisNLP | Helping organizations deploy Generative AI | Creating value with cutting edge NLP
This 👇 Even though the infrastructural challenges of deploying #largelanguagemodels are being overcome at a rate of knots, deploying Generative AI applications is still very hard. As Vin Vashishta says, maybe you can cut corners with an internal audience, but forget it if you plan to deploy to the public. There's already been several high profile cases where public facing deployments have needed to be rolled back, e.g. the National Eating Disorders Association (https://lnkd.in/dTMMup3U) And in the end, it is like any other #machinelearning deployment. The work does not stop, you need to account for things changing over time, and that's all the more complex if you are relying on proprietary APIs that don't factor in your application in their deployment schedule!
AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
Why aren’t we living in a Generative AI-powered utopia yet? LLMs are harder to implement and deploy than most businesses expect. It takes 6-12 months to come up to speed, develop, and release a customer-facing Generative AI app. It’ll take longer if the business’s data isn’t curated to support advanced model training. For internal use cases, it can take as little as 2-3 months to deliver reliable solutions. Products are much harder because they must be more reliable and deliver a superior user experience. Internal-facing LLM apps can get away with cutting corners that a customer won’t tolerate. Publicly available apps must also account for adversarial scenarios and intentional misuse. Mastering Generative AI for internal use cases is the easy part. If the business is planning to build for customer-facing products, add time to account for the differences. Ignore the hype about how easy it is to build LLM apps when it comes to customer-facing products. It’s a completely different process, and development doesn’t end once the product ships. There truly is no finish line. #generativeAI #aistrategy #productmanagement
-
-
This should be talked about much more. It happens with all AI solutions: those without experience in their development and deployment don't see the complexity. With LLMs it's even more widespread: when one sees that they 'talk like a person', one expects to just tell if what to do.
AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
Why aren’t we living in a Generative AI-powered utopia yet? LLMs are harder to implement and deploy than most businesses expect. It takes 6-12 months to come up to speed, develop, and release a customer-facing Generative AI app. It’ll take longer if the business’s data isn’t curated to support advanced model training. For internal use cases, it can take as little as 2-3 months to deliver reliable solutions. Products are much harder because they must be more reliable and deliver a superior user experience. Internal-facing LLM apps can get away with cutting corners that a customer won’t tolerate. Publicly available apps must also account for adversarial scenarios and intentional misuse. Mastering Generative AI for internal use cases is the easy part. If the business is planning to build for customer-facing products, add time to account for the differences. Ignore the hype about how easy it is to build LLM apps when it comes to customer-facing products. It’s a completely different process, and development doesn’t end once the product ships. There truly is no finish line. #generativeAI #aistrategy #productmanagement
-
-
Software Delivery Executive, skilled in collaborating with cross functional, cross-cultural teams to execute strategic digital initiatives through program management disciplines.
#primary - data strategy in place FIRST to support the business objectives that #ai will support… #dataarchitecture Nugget: It takes 6-12 months to come up to speed, develop, and release a customer-facing Generative AI app. It’ll take longer if the business’s data isn’t curated to support advanced model training.
AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
Why aren’t we living in a Generative AI-powered utopia yet? LLMs are harder to implement and deploy than most businesses expect. It takes 6-12 months to come up to speed, develop, and release a customer-facing Generative AI app. It’ll take longer if the business’s data isn’t curated to support advanced model training. For internal use cases, it can take as little as 2-3 months to deliver reliable solutions. Products are much harder because they must be more reliable and deliver a superior user experience. Internal-facing LLM apps can get away with cutting corners that a customer won’t tolerate. Publicly available apps must also account for adversarial scenarios and intentional misuse. Mastering Generative AI for internal use cases is the easy part. If the business is planning to build for customer-facing products, add time to account for the differences. Ignore the hype about how easy it is to build LLM apps when it comes to customer-facing products. It’s a completely different process, and development doesn’t end once the product ships. There truly is no finish line. #generativeAI #aistrategy #productmanagement
-
-
Not only LLM but traditional ML/DS work. A big problem with every client I have. Models drift and sometimes they are very volatile. My solution to them as always IBM Watson Studio IBM Watson Openscale. Deployment and Governance made easy. For LLM Watsonx.governanance.
AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
Why aren’t we living in a Generative AI-powered utopia yet? LLMs are harder to implement and deploy than most businesses expect. It takes 6-12 months to come up to speed, develop, and release a customer-facing Generative AI app. It’ll take longer if the business’s data isn’t curated to support advanced model training. For internal use cases, it can take as little as 2-3 months to deliver reliable solutions. Products are much harder because they must be more reliable and deliver a superior user experience. Internal-facing LLM apps can get away with cutting corners that a customer won’t tolerate. Publicly available apps must also account for adversarial scenarios and intentional misuse. Mastering Generative AI for internal use cases is the easy part. If the business is planning to build for customer-facing products, add time to account for the differences. Ignore the hype about how easy it is to build LLM apps when it comes to customer-facing products. It’s a completely different process, and development doesn’t end once the product ships. There truly is no finish line. #generativeAI #aistrategy #productmanagement
-
-
Building A.I. Agents for Amazon Sellers | E-Commerce & Amazon Entrepreneur | Fund Operator
Here is how I cut my product development time down from hours to minutes using AI. Steal my process. How do you make a better product? -Find the niche leaders -Note down all their features -Sift through hundreds of reviews -Jot down common complaints and wishes -Pull out the drawing board -Wrack your brain trying to solve customer pain points -Organize your scratch pad into a coherent product development report I’ve been doing this process for 10 years and it always takes hours of energy over several days. Not anymore - thanks to AI. I built an AI Agent that does all this work for me and you can use it too. Input ASINs, click a button, and in a couple of minutes, you have a product development report ready to go. When I tested it on a product I sold for years and it solved customer complaints in a way I had never thought of, I knew it was a winner! You can use this AI Agent with a free trial on app.yourecomagent.com. Comment a search term and I will share some examples.
-
-
Building personal brands for founders and startups | I make people famous and businesses profitable | Branding & Content Marketing Expert | Top Voice | want to grow your Personal Brand? checkout my services below
Are you still spending hours in crafting surveys, quizzes, and forms from scratch? No need to worry anymore! forms.app is here to revolutionize the way you create online surveys and forms. What is forms.app? forms.app is an innovative form builder tool that empowers you to effortlessly create surveys, quizzes, and forms online. Whether you're a business professional, educator, or simply someone who values user-friendly tools, you should check out forms.app to streamline your survey and form creation process. The Power of AI Integration:- Imagine being able to describe the type of survey you want, and AI takes care of the rest! Its intelligent AI technology generates new forms and surveys tailored to your specific needs. Say goodbye to the hassle of manual form creation! 📌 Key Features: ✅ Intuitive User Interface: This tool is designed with simplicity in mind, ensuring a smooth user experience. ✅ AI-Powered Form Generation: Describe your survey, and watch as AI creates it for you! ✅ Versatility: forms.app can be used for a wide range of purposes, from market research to educational assessments. ✅ Data Analysis: Easily collect and analyze responses to gain valuable insights. ✅ Mobile-Friendly: Create and access your forms from anywhere, on any device. Whether you're looking to engage your audience, gather valuable feedback, or streamline data collection. Try forms.app today and experience the future of online data collection. #formsapp #surveycreation #ai #datacollection #innovation
VP, Marketing at LogRocket
7moI'm so excited for this! I've talked to a bunch of customers already who are getting great results. There's too much AI out there that just optimizes existing workflows. This actually uses AI to do something you couldn't do before and get you the info you need to deliver better experiences for your users.