What's really happening 👇 The core idea of AI is Leverage, It gives you Leverage- to do more faster, with fewer resources:- ~Automate : Routine tasks ~Brainstrom : Ideas & content ~Generate : Image & videos ~Summarize : Data & Documents Automation gives scale AI adds intelligence together they create leverage not replacement. ~Ai isn't replacing people. It's actually replacing takss. ~Just like calculators didn't replace mathematicians they made them faster. ~People who know how to use AI will replace those who don't. Do you agree? different opinions are most welcomed 👇 Harsh Singh

The Problem- 1.)A support Agent easily spends 2-4 hours answering the same refund policies & the shipping queries 2.) For many businesses, 50–80% of support emails are repetitive FAQs. 3.)Businesses take 4–24 hours to reply to customer emails while a customer wants it as soon as possible. 4.)When your business grows from 80 to 400 customer emails a day you have to hire more people to keep up with the task & provide the same accurate, answer every single time. Now, The solution: For a business getting 80+ support emails per day- 1.) 40-60 emails are repetitive FAQs and policy related queries which can be handled automatically which can save 3-5 hours of manual work every single day, which is 21 -35 hours a week. 2.)Because of the Automation response time can drop from 4-24 hours to under a minutes which is huge. 3.)Support quality will be more consistent, with the same policy-based answer every time and as the business grows you can avoid hiring a new person. Now let me walk you through a demo of this Agent 👇 in the video below. Now if you want it to use it or test it comment "Support" and i will send it to you. Harsh Singh

What started as a demo is now almost production ready, let me walk you through it: I built this for a Imaginary Tech company called 'Gadgets & More Electronics' (Yeah I know name is not sounding like name) First of all the workflows, There are actually Two workflows: 1.The first one is for document ingestion. Where I have converted the FAQs & The policy pdf into chunks & uploaded them into Pinecone vector database. 2.The second one is our Support Gmail agent. Now let's talk about it what it does:- ~It checks for new emails every 15 minutes, takes only unread emails ,newer than 10 minutes. ~Then it detects whether the emails are automated or not, if it is Automated then workflow will stop but if it is not automated it will continue ahead. ~Then it extracts the customer's name from the emails to make it personalized to them. ~Then it will classify the query or emails whether it is Support relevant or not if not the workflow will stop right there but if it is then the workflow will proceed ahead. ~Then comes our support Agent which has our Pinecone vector database as a tool where our policy and FAQs are stored, it knows how to handle each topic, Personalization ,Tone and brand voice ,Output formats, rules, Confidence scores based on the retrieval of data. ~Then there is Agent response checker which will see whether The agent actually found the policy or not? because sometimes you know how Agent make up and hallucinate. ~If the policy found it will create a draft so that a human can review and send it and if it does not found the policy it will create a fallback draft. This is the entire workflow, will drop demo videos today. let me know whether we can add some new features,or add some more guardrails......Just let me know i am welcoming all thoughts and perspective. Harsh Singh

These are the layers where players from the Biggest to the smallest are betting, AI is a 5 layer Cake (As said by Jensen Huang) :- 1. Energy (Base Layer) -To run these Entire Ai Data Centers there is a massive amount of power required. -This is where Big players are investing, In India Reliance Industries , Adani Group are doing it. 2.Chips -The Power is converted into compute using specialized Processing hardware ( like the chips designed by NVIDIA). 3.Infrastructure -The Hyperscale cloud services and network systems that tie massive amounts of hardware together. -This allows data centers to operate seamlessly as global computing platforms. 4.Models - This Includes the LLMs and foundation models developed by organizations(like OpenAI, Anthropic , Google) that process unstructured data. -These are where the Technical people exists includes fine tuning and training of LLMs. 5.Applications (Top Layer) -This is the layer where we can integrate and customize the AI into specific industries to deliver real world values. -To make them more money, save their time, cut their costs. P.s - If you want to transform your Business Using AI then your AI Transformation Partner Harsh Singh is waiting for you.( Sorry for a bit of self promotion 😀)

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