Here are the numbers since we launched the product last February:

• 154 users signed up (~20 are test/duplicate accounts and disposable emails)

• 120 unique projects generated

• 225,882 relevant signals found (insane)

• 625 reply drafts generated

• 6 customers (Around 20 are still trialing, so expecting a potential +1 or 2 customers)

• $313 MRR

First revenue in!

Validation Through Marketing

Despite being too early, I think 6 customers out of the gate (only 2 of them are acquaintances) is a validation signal. We’re also seeing consistent interest across the funnel, from top-of-funnel marketing to decent conversion rates.

Frankly, I was surprised by the early traction. Especially considering that we haven’t done any super-large-scale launches (more on this later) or any pre-selling/validation before the launch.

Here's a good time to challenge one of the most repeated pieces of startup advice out there. Validation consultants and lean startup textbooks will tell you: "Pre-sell and validate before you write a single line of code." It sounds sensible in theory. In practice, it never worked for me (see my struggles in the previous posts on this blog) I've since realized why and have a very strong opinion on this topic.

Cold outreach validation assumes strangers will invest their attention in an unproven idea from someone they've never heard of. When you have nothing to show (no product, proof, credibility), most people will not risk their time. You're asking them to do you a favor.

This is particularly pronounced in the tech space, where operators and decision-makers are drowning in pitches and overpaying for solutions they don’t use. Good luck pre-selling AI marketing tools to marketers.

I think that the 2010 lean startup textbook approach of “sell before you build” still works, but only in more traditional businesses (think analogue spaces that are digitizing) or in niches where you have large authority and connections, so your buyers can take a bet on you as a person, not on your startup idea.

Marketing Campaigns

We did a total of 4 mini campaigns (as we call them) to reach these numbers. 2 successful and 2 unsuccessful.

My approach to this launch was to leverage my LinkedIn profile and my SaaSrank newsletter (around 500 subscribers).

Campaign 1: 1st LinkedIn Engagement Post

This post was a fail. 1500 impressions. Most comments on it were from friends I levied to engage. We got around 5 signups from it.

It lacked the structure of a successful LI engagement post:

• It’s too long

• Too complex

• Sells the SaaS product (vs. a free agent / leads)

• Screenshot of the product (again, too complex to understand)

Campaign 2: SaaSrank newsletter announcement

I did create a similar message for my SaaSrank contact list. Explaining what the app does, why they should use it, and offering a discount code. Unfortunately, the result was similar to the LI post. People were not clicking with the message. While the open rate was decent (35%), it seems they didn’t understand the value of what Sensorhub does.

Campaign 3: 2nd LinkedIn Engagement Post

With the first round of campaigns barely moving the needle, discouragement was starting to creep in (didn’t want to scratch yet another startup idea). I took a Hail Mary approach. I went back to the drawing board on the LinkedIn engagement post. This time, applying what actually made high-performing LI engagement posts work:

• Made it suuuper short

• Presented the product as an “AI agent”

• I asked people if they wanted a ton (3,000 to be exact) free leads from Reddit, LinkedIn, etc.

• Didn’t mention the SaaS, didn’t show our UI, didn’t explain how it works behind the scenes

• Made it as simple as 1-2-3. This is what you do and it takes 5 minutes to get 3,000 leads.

• Shared the Reddit logo as an image (vs. product image)

Here’s the post:

Results:

~100 new signups.

Apparently, promoting scraping services violates their Terms of Service. The frustrating part is the timing. With the early momentum the post was generating, I'm confident it could have reached 20–30K impressions and 200+ signups. But that’s a cautionary note on the challenges of building a product in the lead gen space.

Campaign 4: LinkedIn Job Position Announcement

This Wednesday, I decided to officially announce that I’m starting Sensorhub and used LinkedIn, again. Job change posts perform extremely well on LinkedIn, and this one was no exception.

Results:

And ~20 new signups (still trialing).

Despite the rocky start, LinkedIn proved to be a surprisingly effective channel for early-stage traction. When you have no domain authority, no content, and no existing audience to speak of, organic social is one of the few levers you can pull. Two posts were enough to generate meaningful signals (pun intended).

The Feedback

What was even more encouraging was the feedback we received. Over 15 people have shared highly positive feedback for the tool, praising the quality of the data, and the volume of signals we find. Our feedback Slack channel is brimming with messages like this one:

That said, we need to do a better job of talking to people to gauge how they aim to use the tool and what their strategy is. We have very specific requirements for what we need to add or improve in short-term features (keyword filters and better responses), but I’m still not sure what the long-term direction for Sensorhub would be. I see two potential paths: an AEO tool (similar to how OGTool pivoted to AEO) or a social signals platform (expanding social networks and going beyond conversations with more signals). I don’t have the answer yet.

Why Are People Buying Sensorhub?

Sensorhub provides a quick path to value. 0 friction during sign up and 5-10 minutes to the first Aha moment. You put in your website, Sensorhub analyzes your business and finds the most conversations on social media.

Essentially, we sell data. If you need the data and find it valuable, you’ll buy a subscription. This, combined with the hype of AEO/Reddit visibility and targeting problem-aware people (vs. educating the market), is a concoction for quick traction. Of course, only if you have a great product. And we do.

Lubo spent a month improving signal relevance, and, with a hand on my heart, I can confidently say we are the gold standard for finding this type of data.

Of course, there are limitations and drawbacks to this model. The easy-come easy-go type of users. Because the product's value is extracted so quickly and there’s 0 onboarding investment, I expect people to churn at higher rates.

Sensorhub can surface the most relevant, high-intent conversations happening right now. But knowing you should engage and actually doing it consistently are two different things. You need to consistently follow these subreddits, provide authentic help, and be a valuable asset to the community. This takes time.

We have a few ideas for closing this gap. A managed service offering is one option, where we handle the engagement on behalf of customers, but that’s a services business, not a SaaS one. On the product side, we’ve already built great notification emails that create a re-engagement loop and pull users back into the app, but that’s not enough on its own. Making users successful (not just informed) is the next problem we need to solve, and this will take time, too.

What’s Next for Sensorhub?

• On the product front, we’re currently working on launching the most pressing features – signal filters by keyword(s) and time range.

• For marketing, I’m planning to slowly devise and roll-out long(er) term marketing strategies, including content marketing/SEO, use Sensorhub to build authority and visibility on Reddit, weekly webinars, and more. Still have to figure out the specifics on these initiatives.

• Collect more customer feedback.

All-in-all, it was a successful (soft) launch in my books, and there are still many uncertainties, but these quick wins are enough fuel to get us to the next stage.

Stay tuned for more updates!

Let me explain.

Sensorhub is Almost Ready

We’ve been heads-down building Sensorhub (previously called Subsignal, more on the rebrand in a sec).

The tool started as a Reddit-only social monitoring MVP. Now it works across LinkedIn and X too. We’ve essentially 3x’d the platform coverage.

We also redesigned the entire interface. The original version had this... let’s call it “vibe code aesthetic.” You know the look when something works but screams “a developer made this in a weekend.” We’ve moved to something more professional that doesn’t make you question whether your credit card info is safe. Super pumped to show you the full version soon.

The marketing site is done: sensorhub.ai. You can’t sign up yet, but you can see where we’re headed. Official launch is the week starting January 19th.

Why Sensorhub instead of Subsignal? A competitor launched with the same name. That’s the space right now. Тhere are 2-3 new vibe-coded tools launching every week in social listening/AEO. It’s wild. That’s also partly why we pushed for the redesign. When everyone’s shipping MVPs that look like MVPs, looking professional becomes a differentiator.

The SEO Ebook That Went Viral

On the SaaSrank side, something actually clicked.

I wrote an ebook on SaaS SEO, basically a distillation of everything I learned scaling Encharge to $2M through organic. Published it on LinkedIn, expecting the usual engagement.

It went semi-viral. Almost 600 email subscribers from a single piece of content.

They’re SEO and marketing folks at SaaS companies, exactly who I want to launch Sensorhub to. Keeping it all in the same ecosystem.

Sales: The Lesson I Keep Re-Learning

Got 12 inbound calls for the SEO service from SaaSrank. Still haven’t converted anyone.

Part of it was timing. December is the “let’s catch up in Q1” month. Everyone’s either on vacation or pretending to be.

But part of it was a rookie mistake I keep making: not scheduling the follow-up call on the spot. I’d end calls with “I’ll send you more info” instead of pulling up the calendar right there. Classic rookie mistake.

Some of these leads look promising, though. We’ll see what Q1 brings.

New Mentorships Starting

I also have a couple of new SaaS mentorships kicking off this month through kalo.me/mentorship. Always energizing to work with founders who are a few steps behind where I was with Encharge. You remember all the mistakes more vividly when you see someone about to make them.

January Goals

1. Launch Sensorhub officially — Week of January 19th.

2. Close first SaaSrank client — I have the leads from December. Time to convert.

Simple list. Execution is the hard part.

Will keep this super short, as I’m feeling exhausted from this week’s work.

First, we launched an MVP of the idea that I shared in my previous post.

Subsignal is live, and we have our first users testing it.

What It Does

Subsignal is a social listening tool that does deep analysis of your business (ICP, UVP, competitors, positioning) and finds relevant conversations where you can get mentions and citations.

Currently Reddit-only. LinkedIn is next.

The difference from tools like Brand24 or Mention: we’re not doing enterprise brand monitoring across thousands of mentions. We find the conversations where your product can genuinely help someone. Then we help you engage while the discussion is still active.

This ties directly into AEO (Answer Engine Optimization). With AI search growing, you need to show up where LLMs pull context: Reddit threads, LinkedIn posts, review sites.

85% of brand discovery on LLMs comes from third-party sources.

Subsignal helps you get there.

If you want a walkthrough or have some feedback, do not be shy: hey@kalo.me

In Parallel: SaaSrank

I also launched my SEO services business, SaaSrank.

Announced it with an ebook on LinkedIn: ”How I Built and Sold a $2M SaaS With SEO”

Took longer than expected to write this beast, as it’s 9,300 words long.

The post has generated:

• 1,000+ comments

• 800+ new LinkedIn followers.

• 20K impressions+

• and 200+ email subscribers.

• 2 lead demo calls for the service

Those subscribers are SEO/AEO people. In theory, perfect audience to launch Subsignal to when we’re ready. In practice, I’m not sure how many of them are qualified, buying-capable leads. So at this stage, it’s more of a psychological boost and potentially vanity numbers. But I’d take that over crickets and 0 results.

I sent the eBook to the first ~100 people manually (huge pain in the butt), then automated the process with LeadShark – a great little tool that gets the job done and automates the whole lead magnet engagement process on LinkedIn.

Why 2 Businesses?

Both businesses are in the same ecosystem – SEO/AEO. SaaSrank generates revenue while I build. Subsignal scratches the product itch. Knowledge transfers between both. Client conversations inform product decisions.

What’s Next

Flesh out the MVP based on user feedback. Then start promoting more aggressively.

The journey continues!

Have a great weekend,

Kalo

Follow me:

Website | Youtube | LinkedIn | X

This week? I’m testing something different. And by “testing,” I mean I’m not even sure if this is a real business yet. But at least I’m moving.

The New Idea: Increase Brand Visibility Across Social Media Conversations

The inspiration came from Replymer – a tool that’s apparently doing ~$4k MRR in its first month. Their pitch: monitor social conversations and jump in with helpful replies (written by human VAs) that position you as an authority, not a salesperson (at least in theory).

I tested Replymer briefly, and the human-generated responses felt inauthentic, at times bordering on spammy. That said, I see real value in what they’re doing. Having manually tried to find and engage in relevant conversations on LinkedIn myself, I know how incredibly time-consuming and tough it is to surface opportunities where you can add genuine value.

The overall concept clicked immediately. Instead of creating content and hoping it reaches the right people, you find conversations where your ICP is already asking questions. Then you join with genuinely helpful responses that showcase your expertise.

The idea:

An AI social listening tool that tracks relevant conversations across LinkedIn, Reddit, and X, analyzes which opportunities are actually worth engaging with, and pre-writes authentic, human-sounding replies using your voice.

This helps you:

• Get in front of high-intent leads that show social selling signals

• Increase your visibility in answer search engines and LLMs, as social citations are critical for ranking

• Improves your presence and engagement on social media.

Why Is This Valuable

The technical challenge isn’t tracking simple keyword mentions; that’s table stakes in 2025 (unless you get banned by the social network, but that’s another story). It’s the filtering and relevance matching.

Tools like Brand24 and Mention exist, but they’re built for enterprise brand reputation monitoring. They’re expensive, track a limited number of keywords, and use archaic scraping methods that just match keywords. Their goal is to help large brands track sentiment across hundreds or thousands of monthly mentions, when you essentially want to track your brand name.

What’s missing is a targeted solution that finds opportunities that are actually viable and relevant for smaller companies. Conversations where engaging would provide value to the community and drive high-intent traffic back to you. This type of tracking is based on long-tail keywords and queries, which require lateral analysis of the results rather than a basic keyword search. A LLM layer is needed to analyze the context of mentions and relevance on a deeper level.

We’re currently validating whether we can actually solve the technical side of this. Can we effectively scrape and analyze conversations? Can we identify genuine opportunities versus spam traps? That’s the hard part.

The Validation Attempts

On the sales front, I did two batches of outreach, plus reached out to a previous client. Results range from mixed to bad.

The Previous Client: Not interested. They’re focused on more critical marketing activities, such as creating their own social media content. Fair enough.

Batch 1: SaaStock Connections (~10 people):

Target: Founders, sales people, marketers I’d met at the conference.

Message:

“I’ve been testing a new idea to help businesses get in front of high-intent leads. Not with ads or cold outreach, but by joining real conversations already happening on LinkedIn, Reddit, and X. We track relevant posts in your space and write thoughtful replies that build visibility and trust. Curious to get your take. Want me to share a couple of examples of how it could work for [Company]?”

Results: 0 responses. Ouch.

Batch 2: Social Media Experts (~10 people):

The 2nd batch was reached out with a much softer, feedback-oriented message:

“I’ve been testing a new idea to help businesses get in front of high-intent leads. Not with ads or cold outreach, but by joining real conversations already happening on LinkedIn, Reddit, and X. Would love to get your feedback as a social media expert. Let me know if it’s ok to share more?”

Results: 2 interesting conversations (see screenshots below) with feedback focused on the quality and authenticity of the voice:

^ Fun fact: This person shared that AI-written content generated better commercial results than he did. They mentioned AI could be trained on this, with some initial control over responses needed. The balance is between having perfect “you” answers versus just getting results, even if the tone isn’t 100% dialed in.

The second person shared the idea and would want to approve responses initially until they were comfortable that their voice was captured.

Nobody wants an AI embarrassing them in public forums.

What I’m Learning

I have two moderately positive responses from social media experts, zero responses from my (potential) target customer profile (founders/sales/marketing leaders), and one clear “no” from someone who should, in theory, want this.

The concept doesn’t require them to have perfect data hygiene and established workflows first. It’s addressing a problem people seem to understand, even if they’re not sure about the solution.

Overall, it’s too early to draw conclusions.

Next Week’s Plan

The goal is to validate the technical side: Can we actually find relevant conversations in time for them to be useful? If someone posts “What tool do you use for X?” on Reddit, can we surface that within hours, not days?

If so, I’ll run another outreach batch. This time with specific examples. “Here are 3 conversations from this week where your product could have been mentioned naturally. We can help you find and respond to these.” Provide value first, sell second.

I also have another potential idea for faster validation.

On a side note, I’ve started exploring freelance work in SEO and content marketing. The next startup will take time to validate and build, and with rising expenses eating into my runway, I need to extend my financial cushion while I figure this out. This is the startup way.

The path from exit to restart remains non-linear, but the rollercoaster ride is always exciting.

See you next week!

This week’s update isn’t about a pivot or a breakthrough. It’s about what happens when you do everything “right” (or at least what you believe is right) and still get crickets.

But here’s the thing: invalidation is still validation.

The LinkedIn Messaging Experiment

I tested a couple of different outreach approaches:

Round 1: The Salesy Approach Mentioned Tarsis, shared the link, explained the AI Slack agent concept. Got silence.

Do you use Slack for day-to-day ops?

I’m working on Tarsis.app to help SaaS consolidate scattered sales/product data with Slack-native agents that unify CRM, product, and billing data so GTM teams can act from Slack and get answers in channels. Wondering if this problem seems relevant?

Round 2: The Problem-First Approach Focused on the pain point of context-switching between tools. Without mentioning the app. Asked if they experienced this problem. Got more silence.

Quick question, do you or your clients use Slack much for your day-to-day ops?

I’m exploring an idea around helping SaaS teams get a clearer picture of their customers inside Slack — connecting product, CRM, and billing data so people can ask questions or take action right in channels. Essentially consolidating sales/product/marketing data so you can chat with it and automate tasks without leaving Slack.

Just trying to see if that kind of problem resonates with you?

And another variant of this message:

Hey {{firstName}}, quick one. Do you run sales/product comms in Slack?

I’m working on something new for GTM teams that live in Slack

Consolidating sales/product/marketing data so you can chat with it and automate tasks without leaving Slack. It’s something we built in-house for one of our customers and currently productizing for other SaaS.

Does this sound relevant?

The Numbers Don’t Lie

The Outreach:

• ~100 LinkedIn DMs sent

• 60 to GTM professionals (my supposed ideal customers). Separated into 2 outreach batches.

• 30+ to heads of sales and heads of product. As a 3rd outreach batch. The hypothesis was that I should target middle management, because individual contributors don’t care about process optimization and efficiency (which Tarsis is about)

• The rest to my semi-close network

The Results:

• 5-6 negative responses

• 1 piece of valuable feedback

• 1 positive response

• Most people didn’t bother responding

Essentially: low response rate and just 1 positive reply.

Feedback:

It’s valuable to get at least some perspective and legitimate feedback. As Slack could feel overwhelming, and bringing in more data into it, could make communication even harder.

The BetaList Experiment

Thinking maybe LinkedIn was just the wrong channel, I decided to test demand through BetaList. Paid $300 for a featured spot. Here’s what happened:

• 32 users clicked through from BetaList. Which was a robbery, as essentially I paid $10 per click.

• 90 total people visited the site

• 157 pageviews

• 0 demo calls booked

• 0 follow-up conversations

The average time on site was decent (even quite surprising for Beta list, but not sure if the data in Google Analytics was not skewed), but nobody was biting. The landing page that I’d spent a week perfecting after SaaStock? It wasn’t converting curious visitors into interested prospects.

My Conclusions

The AI Slack agent for GTM teams sounded good in theory. It solved a problem I’d observed. It leveraged trendy technology. It had a clear ICP. But it was missing that crucial element: an urgent hair-on-fire problem.

Before you can query your data in Slack, you need to have your processes built out in your CRM, your product analytics, your marketing automation, etc. You need clean data, established workflows, and teams that are already collaborating around that data. A Slack agent is an optimization play. It makes something that’s already working work better.

The frustrating part is that I genuinely believe teams will adopt these kinds of solutions. Once a team member sees it working in your Slack channels, once the pain of context-switching becomes acute enough, they’ll bite and adopt it company-wide. But it’s not 1st line of urgency or importance. At least this is my hypothesis.

This doesn’t mean the idea was invalid. It means I personally can’t validate it right now. With no early adopter excited to build this with me, no specific use case that’s urgent enough to drive adoption, and no small win to latch onto. Psychologically pushing forward with 0 signals is brutal.

Moving Forward

Tarsis is officially on ice. The landing page will stay up for a bit, but I’m not pursuing it further.

Next week, I’ll share the new direction I’m exploring. It’s based on something interesting.

Thanks for following along on this journey. The path from exit to restart isn’t linear, but at least it’s never boring.

In my previous updates, I shared how I come up with startup ideas and do early customer development. I talked about the frameworks, the competitive analysis, the discovery calls. It all sounded organized, methodical, strategic.

This week? Not so much.

Weeks 4 through 6/7 have been a humbling lesson in what happens when reality doesn’t match your plans. I had goals. I had timelines. I had a landing page I wanted to launch before SaaStock (the conference). Instead, I got overwhelmed, context-switched, and watched my outreach efforts fall flat this week.

But here’s the thing: I’m still showing up and doing the updates.

The Pivot That Never Happened (On Time)

A couple of weeks ago, I was at SaaStock with a goal: to validate a new idea for a customer intelligence platform that connects with your data so you can chat with it.

I built a landing page for the idea. It looked good. But before I could show it to anyone at the conference, I got early feedback that invalidated the whole thing. The response was lukewarm: “Looks cool”. The issue was that it didn’t show specific examples and use cases.

After talking to a startup that has implemented this solution in-house in Slack, I decided to pivot.

The new idea was similar but narrower: instead of a broad customer intelligence platform, why not focus specifically on AI agents for Slack. Position it as a tool that connects to your sales, product, and marketing data, and lets your team ask it questions directly from Slack without context switching.

My goal was to launch this new landing page before SaaStock, show it to people at the conference, have real conversations, and get validation signals.

Life got in the way. Tasks piled up. The landing page didn’t get built in time. I went to SaaStock empty-handed, which frustrated me because I knew I wasn’t maximizing the opportunity. I did manage to get one potential lead out of it, but I left a lot on the table.

I beat myself up over it for a while.

But I came back from the conference, spent about a week properly building the landing page, and started validation this week. You can check the latest landing page at Tarsis.app:

Here’s what I learned: sometimes delays happen. What matters is what you do next.

The Validation Plan

Before I started reaching out, I created a structured validation plan with Phase 1: Discovery (small-scale validation through oubtound) and Phase 2: Pre-launch marketing. Check the plan here.

And quickly did an ICP persona based on the little info/interest pull I have available so far. I needed this to narrow down the outreach target:

The core questions I needed answered in Phase 1 were simple:

• Is this a real problem for GTM teams?

• Are people willing to spend time working with me on this?

• Will they provide feedback and potentially become early users?

I decided on a phased approach to outreach:

• LinkedIn outreach to my network (30 people, including the “hot lead” from SaaStock)

• Direct outreach to my close network (people who know me, including past customers)

• If all else fails, inbound marketing through micro-campaigns and content

The messaging I crafted was fairly soft: discovery-focused rather than salesy. I mentioned I was working on something new for GTM teams, shared the link to the landing page, and explained the core idea.

The Results Were...

I reached out to 30 people on LinkedIn.

I got essentially zero responses.

One person said they don’t see it as a problem. The rest either didn’t see my message or didn’t respond.

This is the part where you question everything. Is the idea bad? Is my messaging off? Are people just too busy on LinkedIn? Is there “sales blindness” where people automatically filter out anything that looks like an attempt to sell them something?

Probably all of the above?

Thing is: poor response rates don’t necessarily mean the idea is bad. They might just mean the channel is wrong.

LinkedIn is saturated with outreach. Everyone is pitching something. People are overwhelmed and defensive. The signal-to-noise ratio is terrible.

Cold outreach in general is extremely saturated right now.

But that doesn’t mean the problem doesn’t exist or that people don’t need the solution. It might just mean I need a different approach.

The Path Forward

I’m going to try a few different things:

• Less salesy messaging: My next batch of outreach won’t mention the app or the domain name.

• Tap my close network again: Past customers and people who actually know me might be more receptive.

• Shift to inbound if necessary: If outbound doesn’t work, I’ll need to try micro-campaigns, content, or other channels that pull people toward me rather than pushing my message to them.

The goal is simple: get a few conversations in the next couple of weeks. Not hundreds. Not thousands. Just conversations with people who might have the problem I’m solving.

The Thing About Building in Public

I’m sharing this because it’s real. This is what actually happens when you’re validating an idea. You don’t get overwhelming enthusiasm. You get silence. You get one person saying “I don’t see the problem.” You get overwhelmed by your other commitments. You build something you thought would take three days in seven days.

And then you keep going.

The previous idea (customer intelligence platform) didn’t work. The messaging approach didn’t work. The cold outreach didn’t work. But the journey itself is working. Every failure is giving me clearer signals about what might actually resonate.

Key Takeaways

If you’re doing your own validation right now, here’s what I’m learning:

✅ Don’t assume the channel is the solution: If your outreach isn’t working, it might not be the idea. It might be that LinkedIn, email, or cold outreach in general isn’t the right channel for your audience, or maybe you are not targeting the right ICP.

✅ Narrow your messaging over time: My first messages were too focused on the product. My next ones will be more focused on the problem and the conversation.

✅ Accept that validation is messy: There’s no clean path from idea to validation to traction. There’s a lot of iteration, a lot of failure, and a lot of learning in the gaps.

✅ Keep moving even when momentum stalls: I didn’t get my landing page up in time. I got poor responses to my outreach. But I’m still moving forward, adjusting, and trying again.

✅ Show up when you don’t feel like showing up

What’s Next

I’m launching another batch of outreach this week, this time with less focus on selling and more focus on understanding. I’ll update you on whether this approach moves the needle.

If you’re interested in the idea, you can check out the landing page at Tarsis.app. I’m building this in real-time, so feedback — even negative feedback — is incredibly valuable.

Thanks for following along on this messy, beautiful journey of building a startup. See you in the next one.

In my last post, I covered how I research startup ideas through frameworks and competitive analysis. Now comes the part everyone talks about but few do well: actually talking to people.

I’m currently in week 6 (ish) of building my new startup, but I’m a couple weeks behind on these posts. Today’s piece covers weeks 2-3: customer development and the mistakes I made while validating my initial ideas through conversations.

Let me be upfront: these early interviews were messy. I made a lot of mistakes. But that’s exactly why I’m writing this. To share what didn’t work and what I’m doing differently now.

The Discovery Approach That Failed

When I started talking to potential customers, I did what most frameworks tell you to do: I picked a niche (salespeople) and asked general discovery questions.

The idea was start broad. Ask open-ended questions about their workflow, pain points, and day-to-day operations. Then narrow down to a specific problem (hopefully)

It didn’t work.

I conducted 10-15 interviews across a few different niches, primarily focusing on salespeople as my target audience.

Here’s what went wrong:

Problem 1: It’s Too Vague

When you ask someone “What do you do on a daily basis?” or “Tell me more about your workflow,” they feel interrogated. They think you’re interviewing them for a job. People don’t enjoy that feeling.

More importantly, they weren’t opening up. Without showing you understand their space or have expertise in it, they resented giving you their time. Why would they spend 30 minutes with a stranger asking vague questions?

Problem 2: You’re Not Showing Value

If you show up as someone knowledgeable in their space, they’re more receptive. If you show up as someone just fishing for ideas, they close off.

The people I was talking to (particularly in sales and marketing) are tech-savvy professionals saturated with solutions. One person told me directly: “We pay for too many tools and don’t use half of them.”

These people weren’t desperate to solve a problem. They were drowning in tools.

The Results Were Useless

After 10-15 interviews with this approach, I had no meaningful insights. No clear problems. No real validation signals. Just a pile of vague conversations that went nowhere.

The people I was targeting were overloaded with tools and solutions. They weren’t actively searching for a new one. And without me positioning myself as someone worth listening to, they had no reason to invest time.

The Approach That I’m Testing Now

Instead of starting with a general audience and narrowing down, I decided to:

1. Come up with a specific hypothesis for a problem

2. Come up with a specific hypothesis for a solution

3. Find the audience and ICP based on that hypothesis

This is a bit counterintuitive if you follow most lean startup advice. Most experts will tell you: talk to people first, figure out the problem second, then build the solution.

I think starting with a hypothesis is more productive.

Here’s why: when you have a specific idea, you can position yourself as someone knowledgeable. You can share your experience. You can relate to their challenges. You become someone worth talking to. And be more specific.

2 Types of Interviews

I split my customer development into two buckets:

1. Expert Interviews

An expert is someone with status and knowledge in the space. Someone who’s been there, done that.

My goal: Get a shortcut to answers. Understand the space at a high level.

Why it matters: If I’m researching B2B SaaS sales, I don’t need to interview 20 salespeople. I can talk to someone who’s run a sales-led SaaS product for 5+ years and who understands the landscape.

Example: If you talk to me about marketing automation (my space as someone who sold Encharge), I can give you an objective view of the entire market because I’ve run a product in that space and exited. I understand the players, the problems, the trends.

2. Buyer Interviews

A buyer is the actual ICP—the person who would potentially buy your product.

My goal: Understand their workflow, their challenges, their buying behavior.

Why it matters: This is where you validate if the problem is real and if your solution could actually work.

How I Structure Expert Interviews

With experts, here’s my approach:

Step 1: Share Your Experience & Context

I open with something like:

“I’ve run a B2B SaaS product with a sales motion similar to what you’re doing, self-serve plus sales-led. I’m trying to help companies like yours solve specific problems.”

This immediately positions me as someone who isn’t wasting their time. I have skin in the game. I understand their world.

Step 2: Share Your Problem

I then share the specific problem I’m trying to solve:

“I struggled with lead qualification, getting trials to convert into demo calls, and closing those demos into paying customers.”

Why? So they can relate to me. So they know I’m not a random person asking random questions.

Step 3: Share Your Value

I mention my experience working with similar companies. The processes I’ve helped them implement. The results I’ve seen. This further reinforces that I’m a credible person having this conversation.

Step 4: Share Your Agenda

“My main goal is to help companies like yours solve these specific problems. I want to understand what tactics and strategies have worked best for high-performing salespeople.”

This frames the conversation as collaborative, not extractive.

Step 5: Ask Informed Questions

Now you can ask questions like:

“What would be the tactics and strategies that have worked best for high-performing sales reps in your space?”

You’re asking the expert about their ICP. You’re trying to understand your buyer through their lens.

Result: Experts are relieved when they hear you have experience. They feel like you’re worth talking to. The conversation flows naturally.

How I Structure Buyer Interviews

With actual potential customers, I take a different approach:

Focus on the Past and Present, Not the Future

People are terrible at predicting the future. They’re great at describing what they’ve already done.

So I focus on real experiences:

• What are the KPIs you track? (How do they measure success?)

• How do you budget for software? (What’s their buying process?)

• Who’s the economic buyer in your company? (Who approves the budget?)

• Have you tried tools like this before? If not, why?

Understand Their Role Deeply

• How do you run a demo? How do you close?

• What’s your typical deal size?

• What’s your sales stack right now?

• What’s your customer profile?

Understand the Market

• How much time does follow-up take your team?

• Have you paid for tools to speed up sales before?

• What’s the urgency around solving this?

My Honest Note: I rarely ask all these questions during a call. Conversations naturally flow. These questions are more like compass points, I use them to guide where the conversation goes.

Implicit Feedback (a.k.a Reading Between the Lines)

Here’s the critical insight I learned: Most people will not give you explicit answers.

They won’t say: “This is the problem I have and I desperately need a solution.”

Instead, you have to read between the lines. Listen for implicit feedback.

Example: The Digital Sales Room Idea That I Invalidated

Let me give you a concrete example.

I had an idea for an AI-powered Digital Sales Room (DSR) tool. Here’s my hypothesis:

The Problem: Sales reps manually create digital sales rooms (personalized micro-sites they send to leads after calls). They have to drag and drop templates, PDFs, playbooks, task lists — all manually.

My Solution: What if we used AI to automate this? One-click DSR generation?

It sounded solid. But when I talked to potential customers, I realized it wouldn’t work.

How I Found Out (Without Explicit Feedback)

I asked salespeople about their DSR workflow. Here’s what I heard implicitly:

• “We only do DSRs for very high-value, enterprise deals.”

• “We don’t do that many of them.”

• “I generally hate doing DSRs.”

• “The potential buyers don’t even look at the DSRs we send.”

• “We have templates for everything. We just drag and drop. It takes 15 minutes, max.”

What This Meant:

Digital sales rooms aren’t used at scale. They’re used for a handful of enterprise deals. The process doesn’t take hours. There’s no pain point that needs automation.

An AI-powered solution to automatically generate DSRs? There’s no market for it because the problem. Manual work isn’t actually a problem. It’s a non-issue.

I learned this not because someone said “This won’t work.” I learned it by listening to what they did and didn’t say.

TLDR;

The shift from general discovery to hypothesis-driven customer development changed everything:

1. I positioned myself as credible. Not as a random person asking questions, but as someone with experience.

2. I focused on real experiences. Past and present. Not hypothetical futures.

3. I learned to listen between the lines. Implicit feedback is more valuable than explicit answers.

4. I invalidated ideas faster. With signal from just 3-5 conversations, I could confidently move on.

5. I wasted less time. Instead of 10-15 vague discovery calls, I could validate or invalidate an idea in 4-5 focused calls.

The Results

I’ve used this approach to invalidate most of my initial ideas. The DSR idea is gone. My next idea—a customer intelligence platform—didn’t pass muster either.

But I also found one idea that’s generating real traction: AI agents for Slack.

This was a pivot from the customer intelligence idea, and early conversations are showing more promise. I’m building a landing page and am currently reaching out to people to validate.

The Key Takeaways

If you’re doing customer development, avoid what I did wrong:

❌ Don’t use vague discovery questions. Position yourself as credible.

❌ Don’t ask about the future. Ask about the past and present.

❌ Don’t wait for explicit feedback. Learn to read between the lines.

❌ Don’t do 15 vague calls. Do 4-5 focused calls instead.

✅ Do come with a hypothesis. It makes you more credible.

✅ Do share your experience. Show you understand their world.

✅ Do focus on real experiences. Past and present, not hypothetical futures.

✅ Do listen for what they don’t say. That’s often more valuable.

What’s Next

In my next post, I’m planning to dive deeper into my validation sprints and pivots. I’ll walk through:

• The pivot to AI agents for Slack

• Whether I actually landed that pilot customer

Thanks for reading. See you in the next one.

If you’re finding this series helpful, consider subscribing. I’m documenting everything as I build this startup in real-time

In my last post, I talked about coming up with startup ideas. Today, I’m diving into how I actually research and validate those ideas before talking to customers.

I’m currently in week 5 of building my new startup (codename: Project25 or P25), but these posts are a bit behind. Today’s piece covers weeks 1-2: the research phase.

Next week, I’ll share how I approach customer development and talking to people.

Setting Up Your Research Infrastructure

The first thing I do when starting to research an idea is create a project to organize everything. I use ClickUp, but Notion or any similar tool works just as well.

Here’s how I structure it:

Project Organization

I run small validation sprints (anywhere from a few days to two weeks) for each specific idea. For each sprint, I create:

1. Task Management

• Specific action items (research tasks, customer calls, feedback collection)

• Clear next steps and deadlines

• Progress tracking

2. Customer Feedback Table

A CRM-style table tracking every cust. dev. conversation with columns for:

• ICP Status: Are they ideal customer profile?

• Type: I bucket people into two groups and I aim to reach out to one of them: experts or potential customer.

• Position & Company: Their role and organization

• Business Type: Company size, industry

• Contact Info: LinkedIn, email

• Problems Identified: Key pain points they mentioned

• Notes & Recordings: Links to call recordings. I use Fathom for recording calls and Granola for taking notes.

This essentially becomes a lightweight CRM for customer development.

3. Competitors’ Table

• Pricing model

• Description (I use AI to scrape: “Check website and explain what the tool does in 1-2 short lines. Try to highlight something that makes this tool unique”)

• Direct vs. indirect competitor status

• Marketing data (just to gauge size)

• USPs: What makes them unique

• Weaknesses: Where they fall short

I also do UX/UI video walkthroughs where I record myself using competitor software for the first time, sharing my raw reactions and notes.

4. Customer Development Questions

I keep a list of questions based on The Mom Test framework (more on this in the next post). I rarely delve deeply into these topics during actual calls. Conversations usually take unexpected directions. But having them helps me know what to listen for and read between the lines.

Pro tip: Ask ChatGPT to “write customer development questions that follow the Mom Test structure” for your specific idea.

5. Research Notes

This is where I conduct in-depth research, brainstorm ideas with ChatGPT, gather landscape insights, and analyze the market.

Why This Structure Works

The beauty of tools like ClickUp and Notion is their AI capabilities. You can query all your collected feedback and research. I don’t use it much since I remember most of my research, but for bigger projects, it’s invaluable.

For each new idea sprint, I just duplicate this project structure and start fresh.

What I Look for Before Researching an Idea

Before I invest time in deep research, I check if an idea meets these 10 criteria:

1. Recurring Subscription Business

Software as a Service (SaaS) only. Recurring revenue is essential.

2. B2B Preferred

B2C unit economics are much more challenging, and I don’t have extensive experience in that area. B2B is where I focus.

3. Product-Founder Fit

This is crucial. Would I be excited to work on this for the next 5+ years, even if it’s unprofitable for the first 6-12 months?

If the answer isn’t a genuine “yes”, if I’m only excited about making money but not the actual problem, I move on.

4. Fragmented Market

Are there behemoth competitors dominating the space?

With my previous startup, Encharge (a marketing automation tool), we failed to meet this mark. We competed against Salesforce, Marketo, and HubSpot. Big mistake.

You don’t want to compete with the Clays, Zapiers, and Figmas of the world. Once you have an established winner, they rarely change.

Now I look for spaces without a clear, dominant winner. Examples:

• Cold email tools (outside of Clay’s lead gen orchestration space)

• Digital sales rooms (DSRs)

• Facebook ads tools, etc.

When a huge player has been in a space for 5-10 years (which is dog years in startup time), they’re a dinosaur. You can carve out a small piece, but you can’t disrupt them. It happens occasionally, but it’s rare.

5. Not a Micro Solution

I want to build substantial products. I’ve done the micro-solution thing with Breezemail (a simple email categorization tool we sold for $5,000 on Acquire.com). It wasn’t engaging. I want to work on bigger ideas.

6. Small Pain to Validate

What’s the barrier to entry for validating and building an MVP?

With Chargey, the barrier was extremely high—we were competing with HubSpot’s billions. I now prefer ideas that are easier to validate.

7. More Than an AI Wrapper

With ChatGPT and Claude adding more features constantly, will this be more than a simple AI wrapper? Or will it get commoditized?

8. Trend and Timing

Is the timing good? At minimum, I need to avoid building against a trend.

Good example: Three days ago, Shopify announced an official partnership with ChatGPT for direct purchases in the chat. That’s riding a wave.

Bad example (an anti-trend): Building traditional SEO tools right now with Google’s AI Overviews disrupting search. That’s an anti-trend.

I don’t need to ride a massive wave, but I can’t build against one. I don’t want to enter a space where people have FOMO and are looking in a different direction.

9. Not Enterprise

I have no enterprise connections. I won’t be the right person to sell to enterprises.

10. Single Point Solution (That Matters)

Not a micro-solution, but a focused solution to a real problem.

Research Frameworks

Once an idea passes my 10-point checklist, I use three frameworks.

Important: Don’t put the cart before the horse. Get some initial validation or interest before diving deep into frameworks.

These frameworks should take 20-30 minutes each, not a week of contemplation.

1. The Lean(er) Canvas – Lean Canvas Simplified

Created by Ash Maurya, the Lean Canvas focuses on:

• Who is your customer?

• How do you identify early adopters?

• How do they solve the problem today?

• What are the top 3 problems?

It’s focused entirely on the segment, audience, and problem—not marketing channels or business models. Check out Ash Maurya’s videos for more details.

Example from my Digital Sales Room idea:

• Customer: Account Executives, SDRs (secondary segment)

• Top 3 Problems: [My initial hypothesis, which changed after research]

• Existing Alternatives: How they currently solve it

• Unique Value Proposition: One-liner on my solution

The 5PM Framework

From Rob Walling at MicroConf, this covers:

• Problem: What problem exists?

• Purchaser: Who is the ICP?

• Pricing model: How will I charge?

• Market: What’s the TAM?

• Product-founder fit: Am I excited about this?

• Pain of validation: How hard is it to validate?

Example questions I answer:

For the problem:

• How important is it?

• How urgent is it?

• Are people actively searching for solutions?

I use Google Trends to check if the keyword is growing. Important tip: Compare your specific keyword to adjacent or broader terms to gauge relative demand.

For my DSR idea, I compared “digital sales rooms” to “sales enablement” and “sales asset management.” Digital sales rooms was tiny in comparison, not necessarily a red flag about market size, but something to keep in mind as it indicates a smaller total addressable market (TAM).

Pro tip: After thinking through the framework yourself, you can ask ChatGPT: “I have this basic idea [explain it]. Fill out the 5PM framework for idea research.” It does a great job.

The STRIKE Framework (My Own)

This is my combination of Lean Canvas and 5PM, plus my own additions:

S - Specific Job to Be Done

• What’s the clear JTBD for my customers?

• What’s the JTBD for my customers’ customers?

Example for DSR: “As a SaaS sales leader, I want a personalized space that adapts to trial usage so I can shorten sales cycles and smoothly onboard them with internal consensus.”

T - True Problem

• Does the product solve a real problem?

• Is it widespread across the target market?

• Is it expensive or urgent enough?

R - Reachable Audience

• Is there a clear audience?

• Are their pain points well understood?

• Are they reachable and willing to adopt?

I - Investment Required

• How much time, money, and effort to get to MVP?

• Is there automation potential?

• Does it solve a repeatable, annoying task people do daily or weekly?

K - Key Advantages

• Do we have unique skills, resources, or moats?

• What competitive advantages can we leverage?

E - Evaluation of Market

• What’s the competitive landscape?

• Are there behemoths or is it fragmented?

Most questions in this framework can’t be answered with certainty at this stage—but they help you form hypotheses.

Deep Research with ChatGPT

I’ve found ChatGPT to be an incredible assistant for getting a landscape overview. I spend tons of time going back and forth with it about ideas.

Setting Up ChatGPT for Research

1. Create a Separate Project for Each Idea

I create a separate ChatGPT project for each validation sprint. This provides ChatGPT with context and saves me from having to rewrite background information every time.

2. Use a Research Brief Template

There are two approaches:

Approach A: Full Freedom

Keep it super brief: “I’m doing deep research on [this idea].”

Let ChatGPT use its own judgment about what information to give you and what approach to take. This often surfaces information you didn’t know you didn’t know.

Approach B: Guided Research

I use this approach from Torsten Walbaum.

Use a detailed template covering:

• Strategic Goal: Why are you doing this research? What does success look like?

• Product Concept & Scope: Explain your idea, target use case, USP

• Target Market: Who’s this for?

• Competitive Priorities: Rank what’s most important (technical capabilities, business model, go-to-market, customer adoption, etc.)

• Hypothesis & Pain Points: What do you suspect is missing? What pain points have you heard?

• Report Preferences: Quick landscape overview, deep dive, or investigative report? What format?

Pro tip: Ask ChatGPT to start with “key insights and recommendations before going into details. Use overview tables or visuals instead of text blocks where appropriate.”

You can use this prompt:

📝 Deep Research Intake: [Your idea]

1. Strategic Goals

Why are you doing this research? (e.g., validate market entry, test hypotheses, attract investors, competitive mapping)
What would success look like from this report?

2. Product Concept & Scope

One-liner vision of your product (plain-language description).
Target use cases (e.g., knowledge retrieval for sales teams, internal documentation search, customer support knowledge base).
Key differentiators you’re considering (e.g., AI-driven summarization, real-time SaaS integrations, security focus).

3. Target Market

Customer segment (SMB, mid-market, enterprise).
Industries of focus (general vs. SaaS-only vs. vertical-specific).
Geographic focus (North America, EU, APAC, global).

4. Competitive Priorities

Rank the following in order of importance for your research:

Technical capabilities (AI/LLM, semantic search, integrations).
Business model & pricing.
Go-to-market strategy (PLG vs. enterprise sales).
Customer base & adoption.
Security/compliance posture.
Other (please specify).

5. Hypotheses & Pain Points


What do you suspect is missing in today’s tools?
What pain points have you heard from potential users or customers?

6. Report Preferences

Depth: (quick landscape, deep dive, or investor-grade).
Format: (competitive matrix, SWOT, emerging trends, gap analysis, TAM/SAM/SOM sizing).

Deliverable style: (executive summary, long-form report, presentation-ready slides).

Example from my current sprint (enterprise search and knowledge management for SaaS):

Strategic Goal: “Validate if entering the market with a B2B SaaS-specific enterprise search tool that provides actionable insights would create appetite. Success = tangible quantifiable data indicating market potential, unique positioning angles, and better understanding of competitors.”

I then give it the full product concept, priorities, and my hypothesis.

The Research Process

1. ChatGPT gives you a proposed research plan

2. Review and fine-tune the plan (Gemini is even better perfect for planning. You can get the plan there and paste it in ChatGPT)

3. Once satisfied with the plan, give ChatGPT the green light to do the research.

The output is incredibly detailed and helpful. Here’s the actual conversation link again.

Reddit Problem/Idea Research

There are now dozens of tools for Reddit research:

• Gummy Search

• Big Ideas Database, etc.

• I tried them and had mixed results.

What These Tools Do Offer

Gummy Search example

1. Choose your audience (e.g., “salespeople”)

2. Select relevant subreddits (e.g., r/sales with 500K members)

3. Browse categories:

   • Hot discussions

   • Top content

   • Advice requests

   • Pain and anger

   • Solution requests

   • Opportunities

Themes section shows patterns like: “Users are expressing frustration due to struggles finding new roles, disliking current jobs, and facing challenges with product-market fit.”

You can click through to see actual posts and comments.

BigIdeasDB is more of a list of ideas and pain points:

Here’s another way to search Reddit for problems. A Google search query, credit to StarterStory:

“{Market to explore}” (

   site:reddit.com 

   inurl:comments|inurl:thread 

   | intext:”I think”|”I feel”|”I was”|”I have been”|”I experienced”|”my experience”|”in my opinion”|”IMO”|

   “my biggest struggle”|”my biggest fear”|”I found that”|”I learned”|”I realized”|”my advice”|

   “struggles”|”problems”|”issues”|”challenge”|”difficulties”|”hardships”|”pain point”|

   “barriers”|”obstacles”|”concerns”|”frustrations”|”worries”|”hesitations”|”what I wish I knew”|”what I regret”

)

The Problems I Found

1. Lack of Statistical Significance

Tools say “here’s a specific problem people have,” but when you dig in, it’s just 1-2 people mentioning it. There’s no data on how widespread the problem actually is.

2. Vague Problems

People think they have a problem, but they haven’t actually looked for a solution. The problems are often theoretical.

3. AI Over-Optimization

These tools’ goal is to find problems, so they find problems where there might be none. You need to be really careful.

My Verdict

I went through 100-200 ideas on Big Ideas Database and various Gummy Search threads. I found some interesting insights, but most problems were too vague and didn’t resonate with me.

Alternative approach: This old-ish guide on manual Reddit research (before the Reddit research tools existed) might be better. It teaches you to:

• Identify relevant subreddits

• Look for problems, motivations, and actions

• Find what recommendations people ask for

• Spot recurring complaints

• Understand what would make achieving their goals easier

By all means, try these tools, they’re cheap. But manage your expectations.

G2 Review Analysis

Once you have the landscape overview from ChatGPT and have filled in your frameworks, it’s time for deeper competitor research.

The most productive way I’ve found: scrape and analyze competitor reviews.

The Process

1. Scrape Reviews

I use Outscraper (about $5 for 1,000 reviews). It has an easy-to-use built-in G2 review scraper that handles CAPTCHAs, proxies, etc.:

• Paste in the product link

• Download a CSV with all reviews, ratings, user info, etc.

2. Analyze with ChatGPT

Upload the CSV to ChatGPT and use this prompt structure to do a grounded analysis and find patterns:

**Objective**: Grounded analysis to find patterns

**Context**: This is a [category] product

**Key Aspects to Extract**:
- Common pros and cons
- Themes and patterns
- Sentiment analysis
- Comparison points
- Suggestions
- Structure: bullet points, summary tables
- Tone of audience

**Optional**:
- Categorize feedback by company size
- Break down by industry

**CRITICAL INSTRUCTIONS**:
- Be as specific as possible
- Be objective, not overly positive
- Show specific examples to illustrate points
- Explain your reasoning
- Don’t latch onto one quote as a trend—show statistical significance

Ensure that you include the critical instructions: without them, ChatGPT will be overly optimistic. It will see one person say “I had a UI issue with Trumpet” and conclude “People have problems with UI, make yours better!”

That’s false. You need objective, grounded analysis that shows how many people mentioned each issue and how significant it really is.

Putting It All Together

This is a lot of information, I know. Here’s the order I recommend:

1. ✅ Quick validation check: Does the idea pass my 10 criteria?

2. ✅ Initial frameworks: Spend 20-30 minutes on Lean Canvas, 5PM, or STRIKE

3. ✅ Deep ChatGPT research: Get landscape overview and competitive analysis

4. ✅ Optional Reddit research: If your audience is active there

5. ✅ G2 review analysis: Deep dive into competitor weaknesses

Remember: Don’t do deep research before you have at least some initial validation or pull. These frameworks help you think through ideas quickly, they’re not meant to keep you in research mode for weeks.

What’s Next

I didn’t have space here to be super specific about individual ideas I’m validating, but if you want me to walk through a specific example in detail, let me know in the comments or DM me.

Next week: Customer development. How I approach talking to people, what mistakes I’ve made, and what I’m doing differently this time around to validate ideas through conversations.

Thanks for reading. See you in the next one.

If you’re finding this series helpful, consider subscribing. I’m documenting everything as I build this startup in real-time.

TL;DR
I created an "idea dump" of 20+ startup concepts + researched competitors. Most ideas looked promising on paper but fell apart during validation. Here's my raw, unfiltered process for evaluating startup ideas and what I learned about turning concepts into reality.

Hey friends, I'm Kalo. I sold my SaaS business Encharge last year and now I'm starting from square one again. This is the first practical post in a series where I'm sharing everything behind the scenes as I build my next startup in public.

The Time Machine: My First Idea Dump

Let me take you back a couple of years, even before selling Encharge. Like most creators, I began with what I call the "startup idea dump".

I found my first one from ~2023. It contains 21-22 different ideas, mostly AI-based tools. Fascinating to see how things have evolved since then.

Some of these ideas became massive businesses. One was Glean.com, now ranked among the top 10 highest-valued AI startups with a $7+ billion valuation.

Others have literally ceased to exist (URLs no longer work), such as collato.com. Many features I sketched out have been launched by major platforms. You snooze you lose mantra is multiplied by 100x in the AI age.

The Ideas That Didn't Age Well

AI Meeting Notes Assistant: Extremely crowded even back then, now dominated by excellent players like Fathom, which I currently use for all my sales and customer development calls.

All-in-One AI Product-Led Sales Platform: This was just trying to out-feature competitors with a massive scope - definitely not a good idea for a bootstrap founder.

PQL Qualifier (Automated Lead Scoring): The market is well-served by tools like Mad Kudu. For small startups, simple qualification criteria are usually sufficient. More sophisticated scoring requires enterprise-level organizations, and that market is already competitive. Additionally, accurately implementing lead scoring is a significant challenge.

Natural Language Bot for Google Spreadsheets: Formulabot had already reached 26,000 MRR in 2023. Airtable launched this feature last year, and it works amazingly well. Expect Excel/Google Sheets to launch AI semantic formulas as we speak.

The Innovative Ones

ChatGPT Bot for Product Onboarding: Instead of help docs, it shows you how to do things directly in the product. Opens emails and guides users through setup processes. I marked this as innovative because I haven't seen anyone doing this at a functional production level yet.

Glean.com Without Enterprise Pricing: Glean starts at $55,000 per year, with a very enterprise-focused approach. A version without the "get demo" pricing could be interesting for smaller organizations.

Blog Post Editing Tool: I had this idea before ChatGPT's deep search existed. Now that AI + AI search is almost eliminating deep content, people care more about unique insights and perspectives that AI cannot replicate.

The Updated Idea Dump

Fast forward to my recent research, I've been compiling a new list over the past few weeks.

Here are some standouts:

Call Agents

HeyRosie generates around $50-60K MRR. You call your local hairdresser, and get greeted by an AI agent that sounds almost human. Works well for inbound calls, but not so much for outbound (robocalls aren't legal in the EU and the US anyway).

Pro tip: If I’m to build a voice-based toold I’d use Vapi. Read my full review on Vapi.

Product Demo Agent

Handhold.io – they do an agent for product demos and user activation, sitting between support bots and sales, with a focus on demos that include step-by-step tutorials.

Pro tip: Use competitors to validate. When there's already a direct competitor in early development, you don't need to build from scratch. Use their tool to gauge interest and traction.

I reached out to Slack communities asking about Handhold. Key objections emerged:

• Most founders don't want to substitute real salespeople with agents

• People wouldn't use it for high-value tickets (one person said only if ACV is under $100)

Digital Sales Rooms (DSRs) for SaaS

Going back to my Encharge days, I often sent long emails after sales calls with next steps, links, documentation, APIs. A tool to organize this would have been valuable.

I discovered there's a whole category called "Digital Sales Rooms" – microsites or glorified Google Drives where salespeople collect sales collateral. My idea: what if this were AI-first (automatically generated DSRs with AI) instead of legacy tools with AI sprinkled on top?

After speaking with salespeople, I learned that they only use DSRs for high-value tickets ($10,000+ ACV), and there aren't enough of these customers/leads to justify scale and automation. They already have templates, and the manual process doesn't take much time.

Sales Intelligence Signal Mapping

These tools (CommonRoom, UserGems, etc.) track time-based activities, such as when leads change jobs, post on LinkedIn, or display any intent signals (which could be as implicit as viewing your LinkedIn profile).

My thinking: merge inbound motion with outbound for product-led SaaS, i.e., unify signals from 1st party data (Segment.com behavior activity, for instance) with 2nd and 3rd party data (CRM activity, enrichment, etc.)

Short answer: There’s no market gap. People engaging in "hot outbound" (reaching out to inbound leads proactively) already utilize existing outbound solutions for signals and lead enrichment. Also, the sales tooling market seems tired — salespeople paying for too many tools and not using them enough.

The Micro-Acquisition Detour

After the exit, I also considered buying a micro tool from Microns.io or MicroAcquire.

I put a list of potential startups to buy:

My research conclusions:

• Pre-revenue startups are of low quality

• Most didn't have working public domains

• Many were just AI wrappers

• Products often didn't work

However, some tools with traction caught my attention. I considered acquiring one and selling it to AppSumo to recoup investment, then using that as a growth engine. But in the end, I decided against this approach because I wasn't passionate about products I hadn't founded, especially tiny single-point solutions.

Key Lessons from the Idea Phase

• Ideas are worthless without talking to people. I think it's good to brainstorm, but you really need to put them to the test.

• The main value was discovering what I gravitate toward, my founder-idea fit. You're always drawn to specific types of ideas, certain audiences you like working with. For me, as a marketing person, I keep coming back to sales and marketing tools for SaaS companies.

• Use existing competitors for validation. If someone's already building what you're thinking about, use their tool and gauge market interest rather than starting from scratch.

• Talk to real users early. Every idea that looked promising on paper fell apart when I actually spoke to potential customers about their real problems and workflows.

This journey is just beginning. If you want to follow along as I build in public, subscribe here and to my YouTube channel.

The goal isn't just to pick any idea. It's to find the right idea that matches my skills, interests, and market opportunity. That process is messier and more iterative than most people share publicly.

In my next post, I'll dive into:

• How I organize research for startup ideas using ClickUp

• Quick frameworks for evaluating ideas

• My exact templates for collecting feedback and brainstorming

• 10 characteristics of a good startup idea for bootstrap founders

• How I do deep competitive research and G2 analysis

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TL;DR
After I sold Encharge, I discovered that launching again is harder. I'm sharing my personal "Life System". A simple framework combining daily habits, accountability measures, and mindset shifts to overcome post-exit paralysis and build momentum for my next venture.

Everyone claims launching your first business is brutal. They're wrong. Starting your second is infinitely harder. Now you know exactly what hell awaits.

I'm Kalo, founder of Encharge. We launched Encharge in 2019 and sold it in 2024. When we started the business, I had less than $1,000 in my bank account, no job, and 0 outside funding. Encharge generated ~$2M in all-time revenue, and we sold it in 2024 for $800k.

Now it's time to dance this dance again.

I'll use this space to #buildInPublic and share every single bit of my second startup rodeo. As Justin Welsh said, “I'm walking through my own mess in public.” If you learn anything from this, then great.

This blog will focus on validating my next idea, driving growth, increasing sales, and breaking stuff around.

However, today I'd like to talk about something more personal. If you are not into this stuff, feel free to skip to the post I’ll share next week, which will be purely business-focused. That said, I strive to provide actionable tips in all my content.

The Post-exit Paradox

After selling Encharge, I found myself in what should have been the best position of my life: in my 30s, financially all right, healthy, and free. Yet, I felt terrible.

The freedom from the exit paralyzed me. The startup I had dedicated years to was gone. While friends were having human babies, I had just "sold my baby," leaving me feeling purposeless.

Result: too many hours in the gym, filling the void with external activities, and procrastinating.

The Midas touch of exited founders is fake news. Nobody knows what they are doing; the only thing you carry on from your previous venture is the hard-working habits and grit.

Breaking the Pattern

"Enough is enough. I need to do something."

I told myself three weeks ago.

That's when I decided to start sharing my journey publicly. Partly for accountability, so the next time someone asks me "how's your new business going", I won't do the signature move of suddenly becoming super interested in my shoes.

My goal is to start the next business and share everything publicly.

But before focusing on business, I had to clear up some mind baggage.

I put together what I call a "Life System." The foundation of the business mindset. I wanted to start with a fresh canvas.

The Life System

It's a simple Google Doc containing my goals, manifesto, and daily practices. I’ve printed it and read it daily.

Here are the key components of mine, but frankly, you can just use anything that works and brings peace to you (if systems help, that is)

Core Principles (My 10 Commandments)

1. Do the right thing - 80-90% of the time, we know what's right, but don't always do it

2. Take action - Be patient with results, impatient with actions.

3. Rely on process over motivation - Systems beat willpower, especially on tough days

4. Follow your system and daily schedule.

5. Build positive habits, eliminate destructive ones.

6. Control emotions - You can't escape them, but you can wade through them like a leaf on water.

7. Practice self-accountability - Not everything is your fault, but everything is your responsibility.

8. Develop self-knowledge - Know your strengths and weaknesses

9. Exercise self-control - Use the pause-plan-response approach before decisions

10. Maintain self-confidence - Face reality and embrace making mistakes

The person I want to be

• Someone with inner peace and joy

• Master of emotions who acts with intention

• Has purpose in work

• Creates deep, meaningful connections with people I care about

These sound simple, but are incredibly hard to sustain when you're grinding daily.

Life System Fill-in-the-blanks Prompt

Life System Template (Year-Year)

“The first step to getting all you want in the world is allowing yourself to want it — and facing the fears necessary to be able to get what you want.”

Commitment Letter

Date: [Insert Date & Time]

To: Myself

My Commitment

I, [Your Name], commit to following this life system daily to build positive habits that will improve my life, help me achieve my goals, and enable me to become the person I aspire to be.

Signed: ____________________

The Life Manifesto I Commit To

Current phase: [Insert your current life phase]

[Manifesto point #1]

[Manifesto point #2]

[Manifesto point #3]

Think clearly:

Self-accountability → [your reminder]

Self-knowledge → [your reminder]

Self-control → [your reminder]

Self-confidence → [your reminder]

The Person I Want to Become

“I’m someone who [describe the future version of yourself].”

Yearly Goals ( [Start Date – End Date] )

Business

[Insert business goal #1]

[Insert business goal #2]

Mental Health

[Insert mental health goal #1]

[Insert mental health goal #2]

Physical Health

[Insert physical health goal #1]

[Insert physical health goal #2]

Finance

[Insert finance goal #1]

[Insert finance goal #2]

Daily / Weekly Habits

Physical Habits:

[Habit + Minimum Viable Habit (MVH)]

Work:

[Habit + MVH]

Education:

[Habit + MVH]

Mental:

[Habit]

[Weekly review habit]

✅ Rule of Thumb: Progress > Perfection. If I miss a day, restart immediately — never twice in a row.

Accountability: If I miss a habit twice in a row, I’m to [insert accountability consequence].

Weekly Schedule ( [Month-Year] )

Mon [Insert focus]

Tue [Insert focus]

Wed: [Insert focus]

...

⚡ Prompt Instruction:

“Fill in the blanks of this Life System template with my own details, and output a clean final version I can print and sign.”

Daily Implementation

I use Todoist in conjunction with the Getting Things Done methodology to clear my mental bandwidth by de-loading tasks into the app. They recently added voice task creation.

For offloading startup ideas and any brain dump, I capture them via AI-transcribed voice notes for easy categorization.

My habit system focuses on minimal meaningful actions. Progress over perfection, restart immediately after missing days. I have financial accountability with a friend who gets $60 if I fail to follow through.

Pro tip: Use these lock-in architecture prompts by Excelleng Prompts to design a fully-fledged accountability system. Here's an example:

Using Momentum from Previous Success

Similar to the "stair step concept" from Rob Walling (Drip/MicroConf): start small, then build larger products until you reach a fully featured SaaS.

I use the same concept, but applied to Go-To-Marketing. Force multiply the previous success. I'm leveraging momentum from selling Encharge, just as I used momentum from my earlier startup, Headreach, to launch Encharge (meta-ception).

By sharing the journey live, I aim to build accountability, gain insights from my network, and leverage the credibility from my previous exit, even though I missed some opportunities (like not having this outlet ready during my Reddit AMA about the exit).

What's Next

I'm currently in week three of working on my next startup. I'll be sharing weekly videos about the ideas I'm evaluating, customer development interviews, and everything I'm doing to validate the next business idea. Next week, I’ll do a retrospective video on what has happened in the last three weeks. Make sure to subscribe to learn more.

It's going to be a long road, but hopefully an exciting one filled with practical advice on marketing, sales, and growth. Subscribe to follow.

Let’s get to (second ;) business!

Books Mentioned In the Video & Post

"The Million Dollar Weekend" by Noah Kagan

A practical guide to starting businesses quickly and validating ideas. Kagan, founder of AppSumo, focuses on taking immediate action rather than endless planning. The book emphasizes the importance of allowing yourself to want success and facing the fears necessary to achieve it.

"Atomic Habits" by James Clear

A comprehensive guide to building good habits and breaking bad ones. Clear explains how small, incremental changes compound over time to create remarkable results. The book introduces concepts like habit stacking, the two-minute rule, and designing your environment for success.

“Clear Thinking” by Shane Parrish

A book focused on improving decision-making and mental models. Parrish, founder of Farnam Street, explores how to think more clearly by understanding cognitive biases, improving self-awareness, and developing better judgment. Much of it is common sense, but common sense is often what we need most.

Tools

• Todoist

• Voicenotes

Friends Sharing their Built-In-Public Journeys

• Georgi Barnev building Hot Stocks today

• WIlco de Kreij building WinningAds

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