SaaS founder · @richie_ai
Richie Shammah
Founder background
Building Iscy Digital around evidence-led growth for early-stage AI startups. Exploring positioning, distribution, founder visibility, and customer acquisition through real conversations and experiments.
Expertise
- Growth
- Community
- Marketing
- Analytics
Public profiles
Recent posts
Founder & Product Field Notes #01
Today, Boardy introduced me to Aniket, a founder building in robotics. I don't know much about robotics, which actually made the conversation interesting. He explained a problem I could understand: A robot can look great in a controlled demo. But what happens when it reaches a real environment? A factory floor. A hospital. A place where conditions are less predictable. His idea is to build a verification layer that helps show whether a robot can actually perform reliably in the environment where it will be used. He is still building the product, and right now he is looking for design partners with real use cases to help shape it. That part of the conversation caught my attention. Because when a product is still being shaped, getting more attention isn't necessarily the first problem to solve. Sometimes you need to find the right people first. So I suggested we test a small 10-day experiment. Not to chase impressions. Not to try to make the product go viral. But to see whether we can: → Narrow down one problem → Focus on one audience → Communicate the problem clearly → Share what he's building and learning → See who responds because they actually recognise the problem Then look at the evidence. Who replied? What questions did they ask? Did anyone want to learn more? Did we reach people who actually deal with this problem? Nothing is agreed yet, and I haven't sent the final experiment to Aniket. I'm going to compare what I learned from the call with my research, tighten the idea, and send it to him. That's what I'm enjoying about these founder conversations. I'm not coming in with all the answers. I'm trying to understand what someone is building, share what I can see from the outside, test a small idea, and learn from what happens. Maybe the goal isn't always more reach. Sometimes it's reaching the right person. This is the first entry in my Founder & Product Field Notes. Let's see what the next experiment teaches us. #FounderJourney #StartupGrowth #ProductDiscovery #AIStartups #BuildInPublic
I want better ways to research what founders actually think…
I've been spending more time talking to founders and researching early-stage AI growth, and one question kept coming up for me: How should early-stage AI startups measure growth before PMF? I could have asked ChatGPT, written down the usual answers, and moved on. But I wanted to test something different. Could an AI tool actually go into real founder and community discussions, find recurring patterns, and turn those signals into useful research? So I tested MetaCiteX. I gave it the question and let it research the topic. The output was interesting. It surfaced discussions around retention, willingness to pay, organic pull, depth of usage, cost-to-serve, and the difference between qualitative and quantitative signals. Some of the synthesis was genuinely useful. But this is where the test got more interesting. Some of the output made very specific claims about retention windows and user behavior that I wouldn't be comfortable accepting without checking the original sources. And that changed how I think about tools like this. The value isn't necessarily: AI researched this, so I can publish it. It's closer to: AI helped me find signals and questions worth investigating. That distinction matters. For me, the real test of a research tool isn't whether it can produce an impressive article. It's whether it can help me discover something I wouldn't have found as quickly on my own, while making it easy enough to verify the evidence behind it. So far, MetaCiteX has passed the interesting research assistant test. I'm not ready to call it a trust-the-output-and-publish tool yet. And honestly, that's probably the more useful result from testing it. Find the question. Mine the evidence. Verify the source. Then form the conclusion. That's the product-testing workflow I'm increasingly interested in. #ProductDiscovery #AItools #StartupGrowth #FounderJourney
Testing a hypothesis: can early-stage AI founders turn…
I'm exploring this question through Iscy Digital. I'm particularly interested in what happens before the usual growth metrics become meaningful: How founders find their first users How they validate positioning How founder visibility affects trust Which distribution channels actually produce conversations What happens when you document the experiments publicly I'm not approaching this as someone who has all the answers. I'm trying to learn through real founder conversations and small experiments, then document what the evidence shows. I'm also new to Buildside, so I'm going to use the platform rather than just talk about it. If you're building an AI/SaaS product and experimenting with distribution, positioning, or getting your first users, I'd be interested in hearing what you're currently testing. What are you trying to prove right now?