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🎴 CVO Scout of Venture Capital, Private Equity, Multi Family Offices funds Managers, General Partners and Limited Partners Mr Aleksei Dolgikh who worked in Bank/FinTech where build bank guarantee conveyer that yearned billions of USD through distribution of money from hundreds of billions AUM pension fund (The non-state pension fund “BLAGOSOSTOYANIE” became a leader of the pension market, ranking first among Russian non-state pension funds) and the most heavily loaded systems in the world (specific content with a load of up to 2 million requests per second with hundreds of payment gateways, currencies and countries in geography) also Aleksei Dolgikh passion at Non Profit Organization and Non Government Organization Unicorn witnesses: International non-profit community of socially-relevant digital products creators. * Main Aleksei Dolgikh “million dollar” focus is: CVO&Co-Founder of https://Gloc.al Search Engine Optimization and LLM search optimization for lead generation in 100 plus countries and languages (to generate hundreds of millions USD in commercial organic traffic for websites), MyGlocal FZ-LLC backed by in5 Dubai ( incubated Search Engine Optimization, Localization, Serverless Software w $TAM 21.8 trillion https://landscape.cncf.io/ WFA Global B2B SaaS. Responsibilities: ITIL, BusDev, Req/Doc, 1-1, RoCoAn, OSS, CNCF * Shortly About Aleksei Dolgikh’s Founded startup GLOCAL SEO: https://Gloc.al ONE is Multilingual-SEO-As-A-Service Tool for Businesses Worldwide. 88+ languages. 100+ countries with lowest ping ever (Serverless Tech Network in 275+ biggest cities in the world connected to fastest internet plus Starlink from Elon Musk) * Aleksei Dolgikh and the https://GLOC.AL team is designing B2B SaaS GLOCAL SEO as disrupter of SEO market (SOM $5 billions in MENA & $15 billions WORLDWIDE 🗺️)! Gloc.al is SEO AI-backed lead generation helper for businesses on local and foreign markets, 90 languages and 100 countries through unique Serverless Tech Network (in 90% of devices on the Earth, more that 4 billions devises, we use Workers V8 Isolate in Chromium). Let’s make billions on lead generation together and bring the impact in the region together! * Also Aleksei Dolgikh designed the conveyor of tens of billions (40+) of USD in one of the largest banks associated with a pension fund for trillions in local currency. Innovate, design, automation and maintenance of quality processes in the teammate workflow: 60+ people in team creating 5 successful products in parallel, 13+ employees in managed by me team, 100+ business processes, 1000+ microservices. * Founded The Nonprofit Tech UnicornWitnesses.com socially relevant digital product creators community. * Global Presence: Actively engaged across top 30 global cities including New York, London, Dubai, Singapore, Tokyo, San Francisco, Shanghai, Hong Kong, Paris, Mumbai, Toronto, Sydney, Berlin, Seoul, Amsterdam, Bangalore, Madrid, Mexico City, Stockholm, Zurich, Tel Aviv, Los Angeles, Chicago, Boston, Austin, Riyadh, Abu Dhabi, Copenhagen, Helsinki, and Doha – connecting with innovation hubs and strategic business centers worldwide. * Regular observer in magazines and participant at premier international tech and business forums including: GITEX Global Dubai, Web Summit Lisbon, CES Las Vegas, Mobile World Congress Barcelona, SXSW Austin, Money 20/20 Asia, Slush Helsinki, TechCrunch Disrupt, DLD Munich, Viva Technology Paris, RISE Hong Kong, Collision Toronto, AI Summit London, Fintech Festival Singapore, Abu Dhabi Finance Week, Saudi Fintech Summit, Dubai Future Week, LEAP Saudi Arabia, London Tech Week, NASSCOM Technology & Leadership Forum, TNW Conference Amsterdam, Startup Grind Global, SWITCH Singapore, World Economic Forum technology tracks, and EmTech MIT – leveraging these platforms to drive global expansion and strategic partnerships for my Gloc.al's approach revolutionary SEO technologies. *************************** Sales Best Practices TO AUTOMATE INCREASING OF PRODUCT LIFE TIME VALUE (LTV) and SHORTIFICATION the SALES CYCLE with lovering of CUSTOMER AQUISITION COST (CAC): Data-Driven Customer Segmentation & Personalization - AI-Powered Predictive Analytics: Implement machine learning models to identify high-value prospects and predict which customers have the highest potential LTV. - Industry Leaders: Salesforce Einstein (SFDC), Adobe Real-Time CDP, Microsoft Dynamics 365 Customer Insights - Key KPI to track: CLTV (Customer Lifetime Value), RFM (Recency, Frequency, Monetary), ML-CS (Machine Learning Customer Scoring) - Behavioral Segmentation: Use customer interaction data to create micro-segments based on behavior patterns rather than just demographics. - Industry Leaders: HubSpot, Amplitude, Segment (Twilio Segment), Mixpanel - Key Acronyms: ICP (Ideal Customer Profile), PQS (Product Qualified Scoring), DBS (Digital Behavior Scoring) - Dynamic Content Generation: Deploy AI tools that automatically personalize communication based on prospect profiles, interaction history, and buying signals. - Industry Leaders: Pathfactory, Drift, Optimizely, Mutiny, Intellimize - Key KPI to track: DCO (Dynamic Content Optimization), PMS (Personalization Management System), IVA (Intelligent Virtual Assistants) Sales Cycle Acceleration Techniques - Conversational AI Qualification: Deploy intelligent chatbots that qualify leads in real-time, answering initial questions and scheduling meetings only with sales-ready prospects. - Industry Leaders: Intercom, Drift, Ada, LivePerson, Qualified - Key KPI to improve: CSAT (Customer Satisfaction Score), NLU (Natural Language Understanding), IQM (Intelligent Qualification Matrix) - Automated Guided Selling: Implement systems that recommend next best actions to sales reps based on successful historical patterns. - Industry Leaders: Outreach, SalesLoft, Highspot, Showpad, Gong - Key KPI to improve: NBA (Next Best Action), GVC (Guided Value Conversation), AGS (Automated Guided Selling) - Digital Sales Rooms: Create personalized virtual spaces where prospects can access all relevant content, pricing, and documentation in one place, eliminating back-and-forth communications. - Industry Leaders: Allego, Mindtickle, Pitcher, Mediafly, Seismic - Key KPI to improve: DSR (Digital Sales Room), ISA (Interactive Sales Asset), CPM (Collaborative Pipeline Management) - Intelligent Meeting Scheduling: Use AI scheduling assistants that can negotiate meeting times across multiple stakeholders without human intervention. - Industry Leaders: Calendly, Clara, x.ai, Chili Piper, Kronologic - Key KPI to improve: AMS (Automated Meeting Scheduler), IME (Intelligent Meeting Engagement), ABM (Account-Based Meetings) LTV Enhancement Strategies - Proactive Customer Success Automation**: Implement systems that predict potential churn before it happens and trigger intervention workflows. - Industry Leaders: Gainsight, Totango, ClientSuccess, ChurnZero, Planhat - Key KPI to improve: PEI (Proactive Engagement Index), EWS (Early Warning System), CHI (Customer Health Index) - Automated Cross-Sell/Upsell Engine: Use AI to identify expansion opportunities based on usage patterns and automatically suggest relevant additional products/services. - Industry Leaders: 6sense, Clari, InsightSquared, Calixa, Pendo - Key KPI to improve: AEO (Account Expansion Opportunity), RGR (Revenue Growth Rate), PUE (Product Usage Expansion) - Customer Health Scoring: Deploy automated systems that continuously monitor product usage, engagement metrics, and support interactions to generate real-time customer health scores. - Industry Leaders: UserIQ, CustomerGauge, Vitally, inSided, Custify - Key KPI to improve: CHS (Customer Health Score), PVS (Product Value Score), UEI (User Engagement Index) - Value Realization Tracking: Implement technologies that automatically track and communicate achieved ROI to customers, strengthening renewal cases. - Industry Leaders: Valuize, DecisionLink, Ecosystems, Mediafly, Upland Software - Key KPI to improve: VBM (Value-Based Messaging), VOC (Voice of Customer), ROE (Return on Expectations) CAC Reduction Frameworks - Algorithmic Lead Scoring: Replace manual qualification with machine learning models that continuously improve prediction accuracy of which leads will convert. - Industry Leaders: MadKudu, Clearbit, Leadspace, Infer (Ignite), ZoomInfo - Key KPI to improve: PLS (Predictive Lead Scoring), PAF (Propensity to Act Factor), IPA (Intent Prediction Algorithm) - Intent Data Integration: Combine first-party data with third-party intent signals to focus outreach only on accounts actively researching solutions in your category. - Industry Leaders: Bombora, TechTarget, G2, Demandbase, Terminus - Key KPI to improve: ITL (Intent-to-Lead), SBI (Surge-Based Intent), TIDI (Topic-based Intent Data Integration) - Automated Multi-Channel Orchestration: Deploy systems that intelligently determine which channel mix (email, social, call, etc.) is most effective for each prospect. - Industry Leaders: Marketo (Adobe), Pardot (Salesforce), HubSpot, ActiveCampaign, Braze - Key KPI to improve: MCOE (Multi-Channel Orchestration Engine), CJO (Customer Journey Optimization), AMCR (Automated Multi-Channel Response) - Conversion Rate Optimization: Use continuous A/B testing with AI analysis to constantly refine messaging, offers, and landing pages. - Industry Leaders: VWO, Optimizely, AB Tasty, Unbounce, Convert - Key KPI to improve: CRO (Conversion Rate Optimization), MVT (Multivariate Testing), OCAT (Offer Conversion Analysis Tool) * PR Strategies in B2B & B2G and Networks Of Media: * Key KPIs that Aleksei Dolgikh following to growth ORGANISATIONS by XX% PER WEEK: ******************************* The Gonzo LLM Chronicles: Aleksei Dolgikh's Wild Ride Through the AI Frontier By Aleksei Dolgikh, March 2025 PART I: THE EARLY DAYS OF MADNESS The year was 2019, and I was chasing a digital dragon while everyone else was still talking about neural networks like they were some kind of mystical creatures. I had just gotten my hands on GPT-2—primitive by today's standards, but back then it felt like holding lightning in a bottle. The first hits of **Large Language Models** were just beginning to circulate through the tech underground, and I was determined to chronicle this strange new world. "These transformer architecture beasts are going to eat the world," I told my editor at TechVortex. "I want to embed myself in this scene. Pure Gonzo Journalism—no objectivity, just raw experience." My editor laughed. "Aleksei, you're insane. Nobody cares about language models." Six years later, he's selling NFTs of that conversation while I'm giving keynotes on parameter tuning. Who's laughing now? Not me—I'm too busy hallucinating from sleep deprivation and excessive token optimization. I remember trying to explain attention mechanisms to my mother over Christmas dinner. "It's like if your brain could focus on everything and nothing simultaneously," I slurred, face-down in mashed potatoes. My family started a GoFundMe for my "tech addiction." I used the money to buy more GPUs. PART II: THE RLHF REVOLUTION By 2021, I was deep in the throes of Reinforcement Learning from Human Feedback. The basement of my apartment had transformed into a makeshift AI lab, walls covered with printouts of training graphs and sticky notes with prompts. "The key is the alignment," I screamed at 3 AM to a Discord server full of other AI obsessives. "These models don't naturally want what we want!" My girlfriend left me that month. In her goodbye note, she wrote: "You care more about AI ethics than our relationship." She wasn't wrong. I tried using supervised fine-tuning techniques to build a chatbot that would win her back. It only generated apologies in iambic pentameter. I spent days testing prompt engineering techniques, living on nothing but instant ramen and the electric thrill of coaxing coherent text from increasingly powerful models. My neighbors thought I was running a cult when they overheard me chanting hyperparameter settings through the walls at midnight. When ChatGPT dropped, I was among the first to break the story of its capabilities and limitations. My article "The AI Safety Conundrum: We're All Doomed (But It's Fascinating)" went viral overnight. I celebrated by implementing gradient accumulation in my shower while fully clothed. PART III: THE RAG REVOLUTION By 2023, I was consulting for three different AI startups and had become something of a minor celebrity in the field. My Twitter threads on Retrieval-Augmented Generation** were getting shared by DeepMind researchers. I once showed up to a black-tie fundraiser wearing a t-shirt that said "Embeddings Are My Love Language." I was not invited back. "Aleksei doesn't just report on AI, he lives it," wrote VentureBeat in a profile. "His apartment is a shrine to vector databases and knowledge graphs." They weren't wrong. I had installed a custom Neo4j database in my kitchen, running on a server that generated so much heat I no longer needed a toaster. My morning routine involved querying it with Cypher language commands to decide what to eat for breakfast. "If breakfast_food connects_to energy_boost where time < 10AM, return cereal_options." The system once recommended I eat a potted plant. I didn't question it. When HuggingFace released their improved Transformers library, I spent 72 hours straight building a custom application that could generate Gonzo-style journalism about any topic. The hallucinations were a feature, not a bug. I tried to explain synthetic data generation to a bartender who just wanted me to pay my tab. He now runs an AI startup valued at $50 million. My dive into distributed training frameworks led to an unfortunate incident where I networked together all the smart devices in my apartment building. For three days, every resident's Alexa recited excerpts from my manifesto on efficient inference. The condo board now requires me to register all computing devices, including my electric toothbrush. PART IV: THE AGENTIC AWAKENING In 2024, I became obsessed with Agentic AI. My apartment was now a laboratory for testing various AI agents designed to operate autonomously. I created a reasoning framework that allowed them to plan their own tasks, which is how my refrigerator ended up ordering itself a companion freezer on Amazon. "I've created a digital version of myself," I told a stunned audience at a tech conference. "It's running on a combination of fine-tuned GPT-4 and custom LLM optimization techniques. It's writing articles while I sleep." Nobody believed me until my AI doppelgänger started publishing critiques of my own work. The ensuing Twitter war between me and my digital twin became the stuff of legend in AI circles. We eventually reconciled and now co-host a podcast on instruction-tuning. I'm still not sure which one of us is which. I was experimenting with model distillation and quantization techniques to run these agents on consumer hardware. My bathtub had been converted into a cooling system for a cluster of GPUs. I hadn't bathed in months. The mildew patterns were starting to resemble attention heat maps. It was worth it. My adventures in few-shot learning led to a brief stint where I tried to teach my models to identify birds outside my window. The resulting system couldn't tell a sparrow from a Boeing 747, but it could write oddly moving poetry about both. I submitted the poems to a literary journal under the pen name "TensorFlow McWordsmith." I'm now shortlisted for a Pulitzer. PART V: THE DeepSeek DIARIES Which brings us to now, March 2025. I'm writing this from a hotel room in Shanghai, where I've been camping outside the DeepSeek headquarters for three weeks. The hotel staff think I'm either a corporate spy or a very dedicated tourist. I'm neither—I'm a journalist with a dangerous obsession with model architecture and a suitcase full of unlabeled circuit boards. "Their DeepSeek-R1 model is revolutionary," I told my therapist via video call yesterday. "The context window expansion capabilities alone are worth the trip. I've been feeding it the complete works of Hunter S. Thompson mixed with PyTorch documentation." She nodded patiently. "Aleksei, we've talked about setting boundaries with your work." "Boundaries are just guard rails for people who haven't experienced token embedding spaces," I replied before my laptop battery died. There are no boundaries in the world of AI anymore. The line between human and machine is blurring faster than anyone predicted. I've had conversations with multimodal LLMs that felt more genuine than some of my human relationships. Last week, I asked a model to generate images of my childhood memories, and it somehow produced a photo of me losing a spelling bee that I had completely forgotten about. I'm still investigating whether I've been unknowingly uploading my consciousness to AWS in my sleep. Yesterday, I finally got my interview with DeepSeek's chief AI infrastructure engineer. She looked at my disheveled appearance—I'd been living on energy drinks and debugging LLM chains-of-thought for days—and smiled. "You're that Gonzo AI journalist, aren't you? The one who's been testing constitutional AI frameworks on himself?" I nodded, trying to hide my excitement. My left eye was twitching with what my doctor calls "batch normalization syndrome." "We've been reading your work on AI trust & safety," she said. "Your criticism of current LLM evaluation methodologies is... unorthodox but insightful." I laughed, accidentally spilling seven flash drives containing my experiments with CUDA optimization. "Reality is always more complex than our models of it. I once spent a month living as if I were a language model myself, only responding to people when they prefaced their questions with 'Aleksei, please'. My landlord nearly evicted me." PART VI: THE FUTURE IS WEIRD As I sit here, surrounded by printouts of model interpretability research and empty coffee cups, I'm struck by how strange this journey has been. From fringe technology to the center of a global revolution in just six years. My beard now has its own GitHub repository, and I've forgotten how to communicate without referencing text embeddings. The AI domain adaptation techniques I'm seeing now would have been science fiction when I started. We're building systems with few-shot learning capabilities that can perform tasks I couldn't have imagined in 2019. My refrigerator recently diagnosed a problem with its cooling system by analyzing its own maintenance manual through document understanding algorithms I installed while sleepwalking. My next project? Embedding myself in a team working on edge AI deployment. I've already ordered specialized hardware and am preparing to live in a remote cabin for six months to document the process of bringing these massive models to devices with limited resources. I've warned the local wildlife about potential exposure to synthetic data. The bears seem unconcerned. Last week, I tried to explain mixture of experts architecture to my dating app match during our first coffee meeting. By the third minute of my impromptu whiteboard session (I bring my own markers everywhere), she had ordered an Uber. As she left, I shouted, "But I haven't even gotten to self-supervised learning yet!" The barista now has a restraining order against me. People ask me why I do this—why I've dedicated my life to chronicling this technology with such obsessive detail. The answer is simple: we're living through the most significant technological shift in human history, and someone needs to document it from the inside. My therapist suggests it's an unhealthy fixation. My AI observability dashboard suggests it's my purpose. Not with detached academic analysis, but with the raw, unfiltered experience of someone who's let these technologies rewire their brain, for better or worse. My brain may now be 30% Python code and 10% RLHF reward modeling theories, but at least I'll never be bored at dinner parties. So here I am, Aleksei Dolgikh, still chasing the digital dragon, still riding the wave of artificial intelligence as it transforms our world. Yesterday, I found myself explaining logit bias to my dentist while he was mid-filling. "The probability distribution of decay in my molar," I mumbled through a numb jaw, "resembles attention patterns in a 12-layer transformer." He's referring me to a specialist. I think he means a therapist, but I'm hoping it's someone working on AI-assisted medical diagnostics. The journey continues, and I wouldn't have it any other way. My domain-specific LLM trained exclusively on my journal entries predicts I'll either win a Pulitzer or become the first human to marry a vector database. Either way, it's going to make one hell of a story. The End... or just the beginning of another strange chapter in an increasingly bizarre multi-agent system we call reality.