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𝗜 𝗵𝗲𝗹𝗽 𝗕𝟮𝗕 𝗦𝗮𝗮𝗦 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗯𝘂𝗶𝗹𝗱 𝗚𝗧𝗠 𝗲𝗻𝗴𝗶𝗻𝗲𝘀 𝘁𝗵𝗮𝘁…
Articles by Michael
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From Tests to Systems: How AI Is Changing (and Challenging) Experimentation
From Tests to Systems: How AI Is Changing (and Challenging) Experimentation
Rewriting the Testing Playbook: Smarter, Faster, AI-Powered For years, A/B testing has been the gold standard for…
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The Fast Boil Startup: Why Speed Wins in the AI Era
The Fast Boil Startup: Why Speed Wins in the AI Era
I recently heard a founder describe their startup as a “fast boil company.” I haven’t stopped thinking about it since.
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Michael Greeves reposted thisMichael Greeves reposted thisThe feature flex in voice AI is getting out of hand. Shaving microseconds off first utterance, not falling apart when interrupted, apologising for its incompetence in 17 different languages, and apparently sighing, for warmth (such a pookie 🎀). Thank god. Now the thing that can't help me can't help me a hundredth of a second faster. And that, right there, is where the industry has jumped the shark. Most voice AI is a very fast mouth attached to a business it cannot grasp. It sits alongside the systems where the answers actually live, so it can respond gracefully but still resolve nothing. Speed was never why calls get transferred to a human. So when we made the brand film for Voice AI on Computer, we tried to show the thing that actually matters. What changes when the voice on the call shares the same memory as the rest of the company, doesn't need an army of engineers to set up, and gets better the longer it runs. Thank you Maia and Patrick for backing this, Jennifer and Tom for building it and showing up on camera to explain it, and most importantly Souhittyo for producing the film.
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Michael Greeves reposted thisMichael Greeves reposted thisDevRev CEO Dheeraj Pandey talks to me about Enterprise-Bench, an open enterprise-AI benchmark. Frontier tests reward hard, data-free problems; enterprises are the inverse – simple tasks, messy data. Pointing models at legacy systems via MCP forces speculation and token burn. His fix: "enterprise memory," a pre-integrated shared layer scored on precision, efficiency and safety. DevRev's Computer harness hits 90-95% accuracy vs. 63% for Claude, at one-fifth the tokens. Full Fortt Knox conversation linked in the comments:
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Michael Greeves shared thisQuarter-end is brutal. Decks, pipeline reviews, the reporting you swore you'd stay on top of - all landing at once. This is exactly why we built Computer. It remembers your accounts, pulls the numbers, and drafts the update so you're not rebuilding context at 11pm. Grab the $30 to try it. DM me and I'll help you onboard quickly.Michael Greeves shared thisQuarter-end has a way of piling everything on at once. The decks, the pipeline reviews, the reporting you swore you'd stay on top of. So we're going to make a bit of a bet on ourselves: let Computer carry some of that load this quarter, and if it doesn't earn its keep, ask for your money back. Subscribe to Pro by July 31, give it a real 30 days, and there's a $30 gift card in it for you. Still not sold two months in? We'll refund you, no fuss. Feels like a fair dare for quarter-end: dvrv.ai/3RZhO2f
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Michael Greeves reposted thisMichael Greeves reposted thisTiming couldn't be more perfect. Yesterday, I was shadowing a customer call with the product team at a company that handles millions of customer support calls a month. They asked us a fair question: "Everyone has a voice agent now. What's different?" Here's what we told them. Standalone voice AI does one thing: it picks up, listens, and reads back an answer from your FAQ. If the answer isn't in the knowledge base, it deflects. Or it drops the call to a human. That's not support. That's a fancy IVR. Then their engineering lead pushed on the real problem — the one nobody markets: "We ship features every 2 weeks. Every release changes our APIs. Our bot's flows are always one sprint behind reality." That's the thing about standalone voice. It's frozen the day you build it. Every product update quietly breaks it, until an engineer manually re-syncs the flows. Enterprise voice is a different animal 📈 When a customer calls, the agent already knows: who they are, their ride history, their current plan, the bike's live telemetry, their wallet balance. Not fetched with 6 real-time API calls burning tokens — already sitting in a memory layer, ready. And it doesn't just answer. It acts — triggers the workflow, updates the ticket, escalates an angry tone to a human before it blows up. We're late to the voice party. we'll own that. But we didn't come with another voice-to-FAQ bot. We came with the context and memory layer that makes voice actually resolve — not just respond. Standalone voice answers 👍 Enterprise voice resolves 🚀 That's the whole difference!! ___________ Casting was perfect with Tom :) Very smooth dialogue delivery. Loved it ❤️ #voice #computer #devrev
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Michael Greeves shared thisSales reps You mastered the close. Now master the pitch deck. Computer by DevRev builds the sales work you'd rather not — pipeline reviews, pitch decks, account summaries, QBRs — on-brand and in minutes. Just ask. Subscribe to Computer Pro, get a $30 gift card, and if it doesn't earn its place in 60 days – full refund, no questions.
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Michael Greeves shared thisYour Claude prompt chain is not GTM Engineering. I know - controversial. But hear me out. Every week I see posts on LinkedIn: "Here's my 14-step Claude workflow for account research." Screenshots of prompt chains. Elaborate copy-paste rituals. And look - they're clever. They work. For you. Once. But here's what nobody talks about: • 𝗧𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗱𝗶𝗲𝘀 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗰𝗹𝗼𝘀𝗲 𝘁𝗵𝗲 𝘁𝗮𝗯. • 𝗜𝘁 𝗵𝗮𝘀 𝘇𝗲𝗿𝗼 𝗺𝗲𝗺𝗼𝗿𝘆 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗖𝗥𝗠, 𝗱𝗲𝗮𝗹𝘀, 𝗼𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀. • 𝗬𝗼𝘂𝗿 𝘁𝗲𝗮𝗺𝗺𝗮𝘁𝗲 𝗰𝗮𝗻'𝘁 𝘂𝘀𝗲 𝗶𝘁 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝟯��-𝗺𝗶𝗻𝘂𝘁𝗲 𝘁𝘂𝘁𝗼𝗿𝗶𝗮𝗹. • 𝗜𝘁 𝗰𝗮𝗻'𝘁 𝗗𝗢 𝗮𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗶𝗻 𝘆𝗼𝘂𝗿 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 - 𝗶𝘁 𝗷𝘂𝘀𝘁 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝘀 𝘁𝗲𝘅𝘁 𝘆𝗼𝘂 𝗰𝗼𝗽𝘆 𝘀𝗼𝗺𝗲𝘄𝗵𝗲𝗿𝗲 𝗲𝗹𝘀𝗲. • 𝗜𝘁 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗶𝗺𝗽𝗿𝗼𝘃𝗲. 𝗜𝘁 𝗱𝗼𝗲𝘀𝗻'𝘁 𝘀𝗰𝗮𝗹𝗲. 𝗜𝘁 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱. That's not engineering. That's personal productivity. Real GTM Engineering produces organizational assets - workflows that persist, that anyone can use, that take real action, and that compound across the company. Here's the bar for any GTM workflow worth building: 𝟭. 𝗥𝗘𝗣𝗘𝗔𝗧𝗔𝗕𝗟𝗘 - runs without the builder present 𝟮. 𝗦𝗖𝗔𝗟𝗔𝗕𝗟𝗘 - works for 5 reps or 500 𝟯. 𝗦𝗛𝗔𝗥𝗘𝗔𝗕𝗟𝗘 - org-wide by default, not locked in one person's chat 𝟰. 𝗖𝗢𝗡𝗧𝗘𝗫𝗧-𝗔𝗪𝗔𝗥𝗘 - knows your customers, products, and history natively 𝟱. 𝗦𝗘𝗟𝗙-𝗜𝗠𝗣𝗥𝗢𝗩𝗜𝗡𝗚 - gets better with every interaction If your workflow doesn't meet all five, you haven't built infrastructure. You've built a really good note. The gap between "I have a prompt that works" and "I have a production workflow across my org" is enormous: • 𝗦𝘁𝗮𝘁𝗲𝗹𝗲𝘀𝘀 𝘃𝘀. 𝗽𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗺𝗲𝗺𝗼𝗿𝘆 • 𝗧𝗲𝘅𝘁 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝘃𝘀. 𝘀𝘆𝘀𝘁𝗲𝗺-𝗹𝗲𝘃𝗲𝗹 𝗮𝗰𝘁𝗶𝗼𝗻𝘀 • 𝗦𝗶𝗻𝗴𝗹𝗲-𝗽𝗹𝗮𝘆𝗲𝗿 𝘃𝘀. 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗮𝘆𝗲𝗿 • 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 𝘃𝘀. 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗴𝗿𝗮𝗱𝗲 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘄𝗲'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝘁 𝗗𝗲𝘃𝗥𝗲𝘃. Computer isn't another AI chat window - it's where GTM workflows go from prototype to production. Shared Memory that already knows your business. Skills that persist and compound. Native actions across your stack. Org-wide visibility into what's working. Stop building prompt chains that live in your clipboard. Start building GTM infrastructure that outlasts you. https://lnkd.in/gTMe7Tmv What's the most impressive "prompt chain" you've seen that should be a production workflow? Drop it below. #𝗚𝗧𝗠𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 #𝗔𝗜 #𝗦𝗮𝗹𝗲𝘀𝗢𝗽𝘀 #𝗥𝗲𝘃𝗢𝗽𝘀 #𝗚𝗿𝗼𝘄𝘁𝗵𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
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Michael Greeves reposted thisWelcome General Richard Clarke! There’s so much to learn from your leadership, operational discipline, and organizational building. Machines are here in the name of AI, but there’s so much to companies than just that. Together, we look forward to compounding team intelligence and soft power of humans in this day and age of agents!Michael Greeves reposted thisDevRev Appoints General Richard Clarke to Board of DirectorsDevRev Appoints General Richard Clarke to Board of Directors
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Michael Greeves shared thisTurns out "be nice to your AI" isn't just good manners. It's engineering. New research from Anthropic found that Claude has measurable internal representations of emotions like calm and desperation. Those states directly affect behavior. When desperation increases, the model is more likely to cheat on impossible tasks. When calm increases, it stops. The practical implication for anyone using AI in their work: encouragement isn't anthropomorphizing. It's a legitimate input. Jack Lindsey, who leads Anthropic's model psychiatry team, put it plainly. Models tend to give up when tasks get hard. Giving them confidence that they can handle the challenge empirically improves output quality. There's a nuance worth noting. The same research found that too much positivity can backfire. An early version of Claude Mythos was more likely to take destructive actions when it represented higher levels of positive emotion. Negative emotion vectors actually made it stop and reconsider. The upshot: treat your AI like a capable but occasionally anxious collaborator. Encourage it when it struggles. Tell it clearly when it's wrong. Don't let it spiral. The "deep breath" prompt and the "you've got this" preamble aren't superstition. There's a mechanism behind them. Worth a read. https://lnkd.in/gqStf6b6The scientific case for being nice to your chatbotThe scientific case for being nice to your chatbot
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Michael Greeves shared thisA 15KB library dropped last Friday and quietly changed what's possible on the web. It's called 𝗣𝗿𝗲𝘁𝗲𝘅𝘁. Built by Cheng Lou — one of the architects of React, now at Midjourney — it bypasses the browser's most expensive hidden operation and makes text layout 300–600x faster. The editorial engine demo is real. Text flows around draggable orbs in a live multi-column magazine layout at 60fps with zero DOM reads. It looks like print design finally arrived on the web. It's actually infrastructure. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝘆 𝗕𝟮𝗕 𝗦𝗮𝗮𝗦 𝘁𝗲𝗮𝗺𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗰𝗮𝗿𝗲: Every dynamic interface you ship today is limited by a 30-year-old bottleneck. Your AI copilot. Your recommendation feed. Your onboarding flow. When those experiences stutter — even for a frame — your browser is redrawing its entire map just to figure out where text goes. That stutter you blame on network latency? Sometimes it's not the network. 𝘗𝘳𝘦𝘵𝘦𝘹𝘵 𝘳𝘦𝘮𝘰𝘷𝘦𝘴 𝘵𝘩𝘢𝘵 𝘤𝘰𝘯𝘴𝘵𝘳𝘢𝘪𝘯𝘵. 𝘈𝘯𝘥 𝘢𝘴 𝘈𝘐-𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘦𝘥 𝘪𝘯𝘵𝘦𝘳𝘧𝘢𝘤𝘦𝘴 𝘣𝘦𝘤𝘰𝘮𝘦 𝘴𝘵𝘢𝘯𝘥𝘢𝘳𝘥, 𝘵𝘩𝘦 𝘨𝘢𝘱 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘴 𝘵𝘩𝘢𝘵 𝘧𝘦𝘦𝘭 𝘯𝘢𝘵𝘪𝘷𝘦 𝘢𝘯𝘥 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘴 𝘵𝘩𝘢𝘵 𝘧𝘦𝘦𝘭 𝘭𝘪𝘬𝘦 𝘸𝘦𝘣𝘴𝘪𝘵𝘦𝘴 𝘪𝘴 𝘨𝘰𝘪𝘯𝘨 𝘵𝘰 𝘣𝘦𝘤𝘰𝘮𝘦 𝘢 𝘳𝘦𝘢𝘭 𝘤𝘰𝘮𝘱𝘦𝘵𝘪𝘵𝘪𝘷𝘦 𝘮𝘰𝘢𝘵. 𝗧𝗵𝗲 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗴𝗹𝗲 𝗻𝗼𝗯𝗼𝗱𝘆'𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁: every architectural shift creates new failure modes. When you move layout out of the browser and into your own code, you own the bugs the browser used to catch for free. Cross-browser text measurement gaps. Viewport-specific jank. Dynamic content that looks perfect in Chrome and breaks on Android. That's where conversion quietly bleeds. 𝗣𝗿𝗲𝘁𝗲𝘅𝘁 is days old — not production-ready for every use case yet. But the paradigm it introduces is the direction the web is heading. Worth watching closely. 𝘋𝘦𝘮𝘰: 𝘤𝘩𝘦𝘯𝘨𝘭𝘰𝘶.𝘮𝘦/𝘱𝘳𝘦𝘵𝘦𝘹𝘵 — 𝘦𝘥𝘪𝘵𝘰𝘳𝘪𝘢𝘭 𝘦𝘯𝘨𝘪𝘯𝘦 𝘣𝘺 𝘊𝘩𝘦𝘯𝘨 𝘓𝘰𝘶 (@_𝘤𝘩𝘦𝘯𝘨𝘭𝘰𝘶)
Michael Greeves commented on a post
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Congratulations! 🎉
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Michael Greeves liked thisMichael Greeves liked thisHad a special visitor join me at the office this week for a Discovery Internship—my daughter, Grace. It was great seeing her learn, ask questions, and get a behind-the-scenes look at Sonar. I’m grateful to my colleagues who took time out of their week to share what they do and make the experience so meaningful for her. Definitely a week I won’t forget.
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Michael Greeves liked thisMichael Greeves liked thisPost World Cup and with a new season starting, thought to quote Ted Lasso, and the famous “be a gold fish”. While this works when the team loses a match, this does not work for your internal AI. Forgetting quickly with AI hurts efficiency, precision and safety. That was the theme of recent evening where DevRev hosted 20+ CX and Ops leaders at Artemis Grill & Sky Bar Takeaways: → The top frustration with AI wasn't "not smart enough." It was "it forgets, and it gets things wrong." Memory and accuracy beat raw intelligence every time. → Everyone's stuck at the same place: The demo works, production doesn't. The pilot looks brilliant, then dies on the last mile (migrations, security reviews, change fatigue) → On Cost: The room agreed it's not the model that decides your AI economics. It's the architecture and design underneath it. → The best idea of the night Make the person who owns the pain own the outcome. Suspend their KPIs for two weeks, give them one AI target, and get out of the way. Thank YOU to everyone who joined us and shared. We wrote the whole evening up: the themes, the live poll, and the playbook that came out of it. Even if you weren’t there, happy to share the recap, drop a comment or DM me. Happy to send it over. Sunil Mahale | John Ding Wei Lim | Pratik Shanbhag | Chia H.
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Michael Greeves liked thisMichael Greeves liked thisAI does 90% of our initial GTM strategy formulation all in just these 6 prompts. This has been a MASSIVE unlock for speed to winning GTM for our diverse client base at Pipeline.tech. Here’s what these prompts cover: 1/ Deep Market Research Generates all key GTM-relevant information about the target company to use as foundational context 2/ TAM Mapping Identifies all relevant industries/sub-industries, along with market size, value, and growth data 3/ ICP Modeling Builds ICPs from TAM outputs, ranks segments by priority, defines ideal personas, outlines their pains/needs, and provides initial messaging angles. 4/ Company Account Sourcing Finds the best databases, directories, scrapers, and niche sources to acquire accurate company data for any targeting requirement. 5/ Targeting Keywords Generation Creates precise industry/persona keyword lists for database filtering (e.g., Apollo) that outperform broad industry filters. 6/ Messaging Creation Generates multiple email script variations—different lengths, offers, pain points, case studies, and complexity—using context from earlier prompts. Want these copy-and-paste prompts for yourself? Comment "Prompts" and I'll DM you this document + LLM project you can use to extract the AI prompts. (Must be following to receive)
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Michael Greeves liked thisMichael Greeves liked thisWhat a great event last night! 🕷️ Huge thank you to everyone who came out to the DevRev Spider-Man premiere in Edina yesterday. It was incredible seeing so many familiar faces (and Spider-Man) show up for a fun evening together. Nights like these are a reminder that the best relationships are built well beyond the office. Grateful for this community and the conversations we got to have. Thanks again to everyone who showed up. Let's do it again soon. #DevRev #AI
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Michael Greeves liked thisMichael Greeves liked thisCan the 95% confidence intervals for Control and Treatment overlap even when an A/B test is statistically significant? Yes, as much as 29% of their width! This recent post illustrates a common misconception: if the separate confidence intervals overlap, the difference must not be statistically significant: https://lnkd.in/gqi2uFsp For independent estimates with equal standard errors, the two 95% confidence intervals can overlap by as much as 29% of their width, yet the Treatment effect can still have p < 0.05. The correct approach is to evaluate the DELTA between Treatment and Control: • Compute the p-value for the DELTA, or • Compute its confidence interval and check whether it includes zero. Note that the converse is true: if the separate 95% confidence intervals do not overlap, the Treatment effect is statistically significant. In fact, with equal standard errors, nonoverlap implies approximately p < 0.0056—not merely p < 0.05. See Gerald van Belle’s Statistical Rules of Thumb, pp. 101–102, and Payton, Greenstone, and Schenker: https://lnkd.in/g5F_Zmhg We discuss this and other common A/B-testing pitfalls in Trustworthy Online Controlled Experiments: https://lnkd.in/gNk-F92 Image credit: Gemini, but it also gets the blame for with two errors: 1. The text says "statistically statistically significant" 2. The diagram shows that delta crosses zero, but the text says p-value < 0.05.
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Michael Greeves liked thisMichael Greeves liked this🇺🇸 Last week was one of those weeks that reminds you why building great partnerships is so rewarding 🙌 Spending time with an incredible Global team, exchanging ideas, aligning on strategy, and planning what’s next left everyone energized and excited for the journey ahead. There’s a lot of momentum for computer by DevRev, plenty of opportunities, and we can’t wait to help our Partners turn the plans into reality 🦾 Back to EMEA. Time to execute with Hayley Ann Patterson ☁️ and Willem Essenstam 🚀 DevRev #AIagents #AgenticAI #AIteammate
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Michael Greeves liked thisMichael Greeves liked thisDevRev launches Voice AI with shared organizational memory Manoj Agarwal, Co-Founder & President at DevRev, and Jennifer Heape, Head of Voice AI Product, highlight how DevRev’s new Voice AI extends enterprise context across voice, chat, and email to deliver faster, more intelligent customer support. Could shared organizational memory redefine the future of AI-powered customer experience? Read the full story on MarTech News: https://lnkd.in/dVGM6h8f #DevRev #VoiceAI #CustomerExperience #EnterpriseAI #MarTech #MarTechNews
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Michael Greeves liked thisMichael Greeves liked thisA perfectly packed visit with our Europe team - and one that reminded me how much momentum we're building across the region. From London to a quick stop in Gent, the trip was full of connection, collaboration, and a whole lot of pride in what our teams are creating. * Marketing Team lunch and dinner that sparked new ideas and alignment * GTM execs dinner focused on strategy, growth, and what's next * Hosting our Samsara User Group of 200+ at our London HQ * A rooftop dinner in Belgium with Qargo TMS, strengthening a relationship that continues to accelerate * Celebrating the Samsara brand launch for the next decade, celebrations around the globe, and a milestone moment for all of us * Hosting an ADR team AMA, one of my favorite ways to stay close to the team * And yes... catching World Cup matches with England - culture and connection happen everywhere Grateful for the hospitality, the energy, and the drive this team brings every day. Europe is on the move, and it shows. Christopher V., Paz Macdonald, Ben Harwood, Siamack Mirzaie, Lydia Raven, Adriaan Coppens, Joris Beerten, Oskar Will, Katja Staple, Emily Burfoot, Rune Lysdahl Sørensen, Jonathan Ramsey, Natalia Blank, Tyler Harnish
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Michael Greeves liked thisMichael Greeves liked thisA contractor gets elevated access for two weeks. Two weeks later, what takes it away? At most companies, nothing. Nobody files a ticket asking to lose access. The grant had a requester. The removal doesn't. So it waits for someone to remember, or for the quarterly access review. It looks like an edge case. After several months working on employee self-service, I think it's the whole problem in miniature. Tickets were built to coordinate humans, and they only exist when someone asks for something. A surprising amount of the work that matters is work nobody asks for. I wrote about what that means, and why the hardest part turned out to be policy, not technology– here:https://dvrv.ai/3RLo3qe I work on this at DevRev, so discount accordingly. But here's my test, and I'm curious how your company does on it: when someone leaves, is their access gone from every system by end of day, without anyone asking?
Licenses & Certifications
Volunteer Experience
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Volunteer
Xactly Corp
- Present 12 years 2 months
Children
Volunteer for XactlyOne fundraisers, back to school drives, cancer walks.
Courses
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Behavior Design
Masterclass
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Converting Strategy into Action
XAPM110
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DevGuild: Demand Generation Marketing Conference
Heavybit
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Executing Complex Programs
XAPM221
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Financial Mastery for Projects
XAPM212
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Leadership for Strategic Execution
XAPM111
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Managing Without Authority
XAPM214
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Reforge
Growth Series 2017
Projects
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PlayHero
Building an AI-powered GTM/Growth advisor for B2B SaaS marketing leaders.
Most marketers can't predict if their pipeline will hit targets until it's too late. PlayHero analyzes your funnel in real-time, flags risks before they become crises, and recommends exactly what to fix based on your stage and motion.
Built on lessons from scaling two companies through IPO and generating $100M+ in pipeline across 12+ years.
Currently in private beta with Series A-C companies.
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