Hi Preston, I took a look at this and here's my comments:
- Wrong ICP, "early stage founders" is not a buyer. They don't have enough email spam problems. Later-stage founders who do already have an EA.
- You're assuming the pain. People generally don't have problem with email scheduling. And if someone were truly spending 6+ hours a week on it, they would have already delegated.
- The messaging framing hurts you. The moment Persona is compared to a human EA, you lose on judgment, context, and trust.
What could work though,
- Narrow to a pathological edge case. One role, one moment, one acute pain.
Examples: solo VCs without EAs, boutique bankers mid-deal, lawyers coordinating multi-party calls under time pressure, founders actively fundraising without support.
- Reframe away from scheduling. Don’t sell logistics or efficiency. Sell control. This is a way more powerful messaging instead.
- Stop outbound. Insert parasitically into their workflow. Live where the pain already exists. Gmail plugins, CRM edges, investor update workflows. Be an appendage, not a destination.
- Accept that this may not be a standalone company. It may only survive as a feature inside a larger assistant or email client.
I know it's a bitter fruit but think about this and digest it. This isn’t about tactics. The problem doesn’t hurt enough for the people you’re targeting, at the moment you’re targeting them.
Thank you for your response. I think the ICP needs some refinement. I've found it pretty hard to prospect because it is so broad.
> People generally don't have problem with email scheduling
I think this stems from the fact that there is no existing tool that solves this problem. Clearly, tools like Calendly haven't solved it for execs or people with complex scheduling flows. A lot of it is caused by the fact that people tend to just tolerate scheduling because they don't know a better way, leading them to waste more time than they expect.
> The moment Persona is compared to a human EA, you lose on judgment, context, and trust.
I agree with this. I'm trying to steer away from this actually, because a lot of companies sell an AI EA, even though their product doesn't even do anything related to EA tasks.
I think the landing page needs more work, copy wise. I'm certainly moving towards the control thing. The features are in place, so I need to tweak our marketing.
> Accept that this may not be a standalone company
Totally. Scheduling alone cannot be a standalone company. That's just the first pain point I'm attacking. I have many more features that are in progress that move it more into it's own category. Think of it more as a email delegation layer for AI agents.
I go deep on SEO/AEO (including technical SEO + LLM optimization), funnel optimization, marketing automation, and analytics infrastructure. I write code to solve problems—landing pages, event tracking, conversion APIs, dashboards, whatever moves the needle.
Looking for: Growth roles at early-stage SaaS (pre-seed to Series A) where I can own the full funnel and build systems from scratch.
ok been quietly working on this thing for the past 5 months, and the reports finally hit the quality bar i was aiming for.
long story short: i built a system that scrapes a company’s site, profiles, press, and anything else it can find online, then runs it through a multi-model prompt engine i wrote from scratch (multiple LLMs chained together with custom logic). the workflow is split into three parts: crawl, process, synthesize.
output comes in markdown. all signal, no bs. useful for GTM, growth, competitor analysis, product gaps, strategic blindspots, that kind of thing.
i’ve been stress testing it on real companies. just pushed 2 sample reports to GitHub if you wanna peek:
not selling it as a product (yet). backend still in dev. just doing reports manually for now while testing edge cases/validating if anyone wants this at all lol.
note: it’s not fully automated. i use the system to generate raw analysis, then apply human input to reframe, expand, or clean up weak insights. think of it like pairing with an AI researcher who works fast but lacks nuance/context, you still need a strategist to shape the output. there's tons more feature but at the core this is it.
happy to run one or two more this week if anyone wants a teardown. not free though (token cost’s brutal lol). DM if interested.
Agreed. A lot of SEA countries been handling it pretty well(with the exception of Indonesia). I'm from Malaysia and even though there's been a huge political storm recently, we still have the highest recovery rate among all fo the countries that are suffering.
This is funny, if you actually google the keyword X-Keyscore, you'll find job opportunities that matches the criteria of intelligence gathering. And if you look closely, you'll find out it's a company called Raytheon that's awarded the contract to execute these works. And they have main offices in these crucial locations, eg; Fort Meade, Australia etc. And that they do all these kind of intelligence works.
And ironically, Raytheon's scientist was the dude who invented microwave.
What happens next looks like a 21st-century data assembly line. At the NSA’s headquarters in Fort Meade, Maryland, a program called Xkeyscore processes all intercepted electronic signals before sending them to different “production lines” that deal with specific issues. Here, we find another array of code names.
Pinwale is the main NSA database for recorded signals intercepts, the authors report. Within it, there are various keyword compartments, which the NSA calls “selectors.” Metadata (things like the “To” and “From” field on an e-mail) is stored in a database called Marina. It generally stays there for five years. In a database called Maui there is “finished reporting,” the transcripts and analysis of calls. (Metadata never goes here, the authors found.)
As all this is happening, there are dozens of other NSA signals activity lines, called SIGADS, processing data. There’s Anchory, an all-source database for communications intelligence; Homebase, which lets NSA analysts coordinate their searches based on priorities set by the Director of National Intelligence; Airgap, which deals with missions that are a priority for the Department of Defense; Wrangler, an electronic intelligence line; Tinman, which handles air warning and surveillance; and more.
Lest you get confused by this swirl of code names and acronyms, keep this image in mind of the NSA as a data-analysis factory. Based on my own reporting, the agency is collecting so much information every day that without a regimented, factory-like system, analysts would never have the chance to look at it all. Indeed, they don’t analyze much of it. Computers handle a chunk, but a lot of information remains stored for future analysis.
