Clay’s Post

Is there an Ai tool for Gmail/Workspaces? What a crazy useful feature it would be to have an AI chat that's aware of my business email. Examples: Show me emails where someone showed a sentiment to buy that I didn't follow-up with / what company did I correspond with most last month, and last year? With whom did my correspondence sharply drop last quarter? That kind of aggregated and specific data would be amazing...

Introducing the fastest way to build and desing email templates, the 10x Email Builder. Leave a comment if you want to try it. #email #ai #ai #TechEvolution #TechLife #SoftwareDevelopment

☀ Thought of the day ☀ I often say that we need an AI transformation to become a fully AI-driven company. However, getting started is not easy, and leaders search for quick wins. Well, where do we find these quick wins? 💭 One of the easiest places to introduce AI solutions is where decisions are already automated. Here we can simply swap out a human-defined rule with one by a machine-learning model. Example 1: E-commerce Product Recommendations🛍️ Before: An online store might use simple algorithms that suggest products based on broad categories. After: The recommendation engine recommends products with an ML model trained on individual user behavior, purchase histories, browsing patterns, and time spent on specific products. Example 2: Email Spam Filters📧 Before: Emails are filtered based on blacklisted words or known spam sender addresses. After: The spam filter makes decisions based on an ML model that uses context, subject, text, etc. #lowhangingfruits #aitransformation

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𝗡𝗲𝘄 '𝗧𝗮𝗿𝗴𝗲𝘁 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿' 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗔𝗱𝗱𝗲𝗱 𝘁𝗼 𝗔𝗜 𝗪𝗿𝗶𝘁𝗲𝗿'𝘀 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗣𝗿𝗼𝗳𝗶𝗹𝗲 𝗦𝗰𝗿𝗲𝗲𝗻 Please note that now you can add your target customer to your company profile in the AI Writer. If you leave this blank, it will not mention target customer at all in your prompts. However, if you add your target customer here it will automatically specify target customer in all modules, and in just the right place. We found that this can really tailor the output of your tasks. If you don't have a target customer, then leave this field blank.

It may not be visible today, but the have's and have not's is lurking stealthy...those who have 'done the right things' and those who have hacked their data over the years for a short-term profit.... AI reveals the choices made...as AI requires GREAT data -- not good enough - but GREAT data quality, structure and governance. The choices made on data quality every day has a long term impact on the bottom line.... and it will make it obvious who the winners and losers are.

A good automation is like a piece of art 🎨 Today, for the halfway point of the #30DayAutomationChallenge, I delved into an automation for a client monitoring media mentions online and having AI summarise and populate a table with fields. This is such a fun automation that can scale further in so many different directions! Plus it saves them hours of, what would otherwise be, manual searching and data entry! #Automations #Media #Make #AI

I’m excited to announce that I completed another AI project where we fine-tuned an LLM on a celebrity’s content and integrated it with a voice clone ⬇️ In this project, we brought this AI project from 0 to 1 by preparing the data, experimenting with different prompts, and testing different technologies and integrations until we could recreate clips of a celebrity giving their takes on current events. One learning from this project was that double-blind testing was an effective way to assess performance improvements between a fine-tuned model and a baseline model. For example, you can show clients different clips and see if they can identify which ones are from the fine-tuned model and which ones are not. If they can reliably detect content generated from the fine-tuned model, we know that fine-tuning yielded a tangible performance improvement. In such tests, it’s important to keep as many variables constant as possible to not skew results. You have to make sure the prompts passed to both are similar and that you are using the same model in all tests to ensure apples-to-apples comparisons. Link to the case study below 👇 https://lnkd.in/gs45DrSb

There are 2 main problems with cold email at scale.. 1. Email blasts that pretend to be personal but were obviously sent to 1000s of people 2. Manually writing personalized emails takes a TON of time and effort The solution is AI. 🤖 See how we personalize emails at scale with this real example. And if you're interested in seeing what we could come up with for you, book a call and we'll give you a free strategy and example just like this one. https://lnkd.in/eubj_xvV

How did I clean and enrich a list of thousands of leads for a client this morning – without sacrificing time with my family? I didn’t wake up at 4:30 or offshore the task. I used AI. Look…there’s a tendency on LI to claim AI is a silver bullet OR that it’s pure snake oil. The reality is that it’s great for some things…but not so much for others. A lot of repetitive boring tasks can be outsourced to AI. For example – processing data that doesn’t require deep industry knowledge (like lead lists) is a bad way to spend your team’s time. Turn your team’s attention to higher value tasks…like writing targeted copy that resonates with your prospects (AI is bad at this). Shoot me a DM if you want to see the script I use to automate this task. Happy to share!