DEV Community: Saul Fleischman

I Spent 10 Days Teaching an AI to Make Launch Videos That Aren't Embarrassing

Saul Fleischman — Sun, 12 Jul 2026 04:14:43 +0000

I'm a solo founder. I can build a product. What I can't do — what I never have time for — is the marketing: the launch video, the GIFs, the posts, the articles a good product needs and quietly dies without. So I built a tool to do it for me. It's called FoxPlug, and for about ten days straight it made videos I'd have been embarrassed to send anyone.

This is the honest story of those ten days, because the mistakes are more useful than the wins.

The idea was simple. The output was garbage.

The pitch is easy to say: paste a URL, get a launch video. Under the hood, FoxPlug pulled a product's public images and stitched them into a 30-second film with captions and music. Clean concept. The first videos were a paragraph of marketing copy laid over a stock gradient, set to a snappy beat. Walls of words. No product. If you'd shown me one and asked "want that for your launch?", I'd have said no.

The first trap was polishing the wrong thing. I kept making the text nicer — fonts, captions, transitions — when the real problem was that there was no product on screen at all.

"It doesn't show the builder actually working"

The turning point wasn't my idea. I gift these videos to founders on Product Hunt who launched without a demo, and I went back and asked, point blank: what would make this good enough that you'd actually put it in your launch gallery?

One reply did more for me than a week of my own guessing:

"The video looks nice, but it lacks actual usage of the builder — the drag and drop, moving and filling inventory, the exports. That's the whole point of it."

That's the whole thing, isn't it. A launch video isn't a pretty title card. It's the click, the drag, the "oh, that's what it does." I was making postcards when people wanted to see the product move.

The fix that mattered: use the real product, or don't pretend

So I rebuilt the engine around one rule: use the founder's real product screenshots, and refuse to fall back to generic animation when real ones exist. Sounds obvious. It was five bugs deep.

The images makers give you aren't clean screenshots — they're wrapped in marketing frames (a big header, annotation callouts). My engine rejected those wholesale and defaulted to stock motion. Meanwhile the GIF side of the same product cropped them to the app window and used them perfectly. Same input, opposite result, because one cropped and one rejected. Once I made the video reuse what the GIF already figured out, the tours finally showed the actual product — a real database, a real dashboard — not a lock icon on a gradient.

I also added a gate that blocks a weak render before it publishes: blank frames, the same asset used twice, too much text, a cursor that never moves. Fail, and it doesn't ship. And every render is stamped with the exact code that made it, so I can prove what produced what instead of guessing.

The bigger lesson: a recorder is a commodity; what you do with the recording isn't

Even with real screenshots, a video built from static images has a ceiling — it can't show the drag-and-drop. So the next move was obvious and a little humbling: let people record their live product — a 30-second click-through, in the browser, no software, no mic — and turn that into the film.

But I had to be honest with myself: a screen recorder is a commodity. Loom does it. macOS does it free. Nobody needs another one. The reason to record inside my tool can't be the recording — it has to be everything after. One real recording → a launch video and a GIF series and a sticker set and stills and a blog post and native posts for every channel. One input, a launch's worth of content out. That part isn't a commodity.

What I got honest about (the part most build-in-public posts skip)

Ten days in, here's my traction: a handful of signups, no revenue, and — I checked — time-on-site was dropping on the days I thought the videos were getting better. A third-party "usefulness" audit scored the project 37/100: genuine pain point, modern build, two big zeros — reach and traction.

That stung, and it was the most useful data I got. It told me the product isn't the problem; distribution is. Every early signup came from one channel: gifting real, white-label launch videos to founders who didn't have one, on the day they launched. Slow. Hand-earned. But it's the only thing that's converted anyone — and it works because I do something useful for someone before asking for anything.

Where this leaves me

I won't tell you it's fixed. (I've learned not to say "fixed" — I've said it too many times.) The videos are real now. The recorder works. The repurposing exists. Whether "record your real product, get a whole content kit" is the thing that finally moves those two zeros — that's the next ten days.

If you're building in public and staring at the same wall — you can ship, but you can't find the hours to tell anyone — that's exactly who I'm building this for. Because it's me.

— Saul, building FoxPlug in public

How to write an investment memo that actually gets a deal funded

Saul Fleischman — Sat, 11 Jul 2026 02:25:25 +0000

The investment memo is the most underrated skill in venture. It is the document your partners read, the record of your reasoning, and, when you are right, the thing that made the case at the moment of decision. A weak memo loses good deals. A strong one wins debatable ones.

The structure matters less than the honesty. A useful memo covers the company's purpose in a sentence, the problem and who has it, the solution and why it is better than the alternatives, why now, the market, the competition, the product as it exists today, the business model and unit economics, the team, the traction, the deal terms, and, the part most people rush, the risks. Then it ends with a recommendation and a conviction level you are willing to be held to.

The risks section is where memos earn their keep. A memo that only makes the bull case is not analysis; it is advocacy, and your partners can smell it. The strongest memos state the bear case as well as a skeptic would, then explain why you are investing anyway. Conviction that has stared down the downside is worth far more than conviction that ignored it.

Numbers belong in a memo, but only the ones that matter. At the early stage that is usually growth, retention, and the basic unit economics, what a customer is worth and what it costs to acquire them. Burying the committee in metrics is a way of hiding that you do not know which ones count.

The conviction score is the discipline. Putting a number on how strongly you would back the deal forces a real position instead of a hedge. It also makes you accountable later: when you revisit the memo after the company succeeds or fails, your stated conviction tells you whether your judgment was calibrated or lucky.

A good memo is short, honest about risk, specific about the few metrics that matter, and ends with a number you would defend. Write enough of them and your judgment compounds, because every one is a bet you can grade yourself on.

Building a 52,000-Investor Database From Scratch With No Funding

Saul Fleischman — Fri, 10 Jul 2026 07:28:56 +0000

Most people assume you need money to find money. That assumption cost me six months I will never get back.

How It Started

I launched MentionFox without a war chest, without a warm intro to a partner at a16z, and without a BD person whose job was to "build relationships in the VC ecosystem." What I had was a product that scraped, parsed, and organized public signals across the web, and a stubborn belief that the same infrastructure I was building for our customers could solve my own problem first.

The problem was simple to describe and brutal to live with. I needed investors. Not the idea of investors. Not a list of 200 firms copy-pasted from Crunchbase. I needed the right people, at the right firms, writing checks at the right stage, who had actually touched a company like mine in the last 18 months. That specificity is where most founder databases fall apart. They give you a phone book. They do not give you a lead.

So I decided to build my own. And because I had no budget, I had to be disciplined in a way that funded teams never are. Every data point had to earn its place.

What I Actually Built and How

The first version was embarrassing. A Google Sheet with 300 rows, sourced from LinkedIn searches, AngelList profiles, and a few Substack newsletters I had bookmarked. I thought I was being clever. I was being slow. The sheet was already stale by the time I finished populating it.

The second version was where things changed. I started treating investor discovery the same way I treated media monitoring. Instead of waiting for investors to announce themselves, I tracked the signals they were already leaving in public. Podcast appearances. Conference speaker slots. Twitter threads about specific verticals. Blog posts on firm websites. Portfolio company press releases that revealed a thesis. Quotes in TechCrunch deal announcements. Each of these signals told me something a database field never could: what this person actually cared about right now, not what their firm bio said they cared about two years ago.

I built structured scraping workflows that pulled this into a central store. Then I layered in deduplication, firm-level context, and a simple scoring model. Investors who had recently posted about B2B SaaS tooling, who had led at least one Seed or Series A in the last 12 months, and who had a check size that made sense for where I was got a higher score. Investors who had not touched the category in three years, or whose last deal was a Series D, got deprioritized regardless of how famous their name was. This sounds obvious. Almost no one does it.

By month four, the database had crossed 10,000 records. By month eight, 30,000. The jump to 52,000 came from expanding the signal sources: international LP databases, government-backed fund disclosures in the UK and EU, family office filings, and corporate venture arms that most founders completely ignore because they do not fit the classic VC mold. Corporate VCs wrote a lot of checks last year. They are faster to close than you think. They were dramatically underrepresented in every investor list I had ever seen.

The quality gate mattered as much as the volume. I ran periodic audits where I would pull 100 random records and manually verify them. Firm still active? Check. Partner still at the firm? Check. Fund vintage still open? Check. At month twelve, my accuracy rate on active, reachable investors was above 85 percent. That number matters because outreach to a stale record is not just wasted effort. It damages your sender reputation and, if you are emailing, your deliverability.

What the Data Taught Me About Investor Outreach

Three things surprised me when I started actually using the database.

First, recency of activity is a better filter than AUM or prestige. A managing partner at a $500M fund who has not led a deal in nine months is a worse lead than an emerging manager who closed two deals last quarter. The signal that someone is actively deploying capital is worth more than any credential on their bio page.

Second, the investors who engage with content in your category before you reach out convert at a rate that is roughly four times higher than cold outreach to people who match on paper. When I could see that someone had recently shared an article about AI-native B2B tools, my reply rate on a first email jumped from around 4 percent to somewhere between 15 and 20 percent. That is not a small difference. That is the difference between a process that feels like shouting into a void and one that feels like a conversation.

Third, the database itself is a leverage point in calls. When you can walk into a conversation and say "I noticed your last three investments all had this characteristic in common, and here is how we fit that pattern," you stop sounding like a founder pitching and start sounding like someone who has done the work. Investors notice. It shifts the dynamic in a way that no pitch deck polish ever will.

The Practical Version for Founders Who Cannot Build This Themselves

I am not going to pretend the path I took is repeatable for most founders. Building a scraping and enrichment infrastructure from scratch while also running a company is not a good use of your time unless that infrastructure is itself your product. It was for me. For most people, it should not be.

What I would tell you to do instead is think about your investor search the way you would think about a sales pipeline. Define your ideal investor profile with the same rigor you would use for an ICP. Stage, check size, vertical focus, geographic preference, recent activity, fund age. Then find a tool or a workflow that lets you filter on those dimensions in real time rather than working from a static list someone exported six months ago.

The feature we eventually built into MentionFox for this is what I still use every week. You can find investors using live signals rather than static database entries, which means the results you see reflect what someone is actually doing right now, not what their firm website says they do. That distinction, between declared thesis and revealed behavior, is the whole game. If you want to understand what it costs to access that and the rest of the platform, the pricing page lays it out without a sales call requirement.

The 52,000 records I built are now embedded in the product. I did not build them to flex about scale. I built them because I was desperate and had no other option, and somewhere in that desperation I found a method that actually worked.

If you found this useful, I write about solo-founder distribution, B2B SaaS, and what's actually working in the AI-search era over on my Substack (one post per week, no spam).

I'm building MentionFox - a B2B intelligence suite that combines brand mention tracking with AI-visibility (GEO) measurement, investor research, and outreach automation. There's a free tier and a 5-day trial of Pro at mentionfox.com/pricing.

What real diligence on an early-stage startup actually looks like

Saul Fleischman — Thu, 09 Jul 2026 04:03:55 +0000

At the early stage, there is not much to diligence in the traditional sense. There are no audited financials, the market is a guess, and the product may be three months old. So what are you actually checking when you do diligence on a seed or Series A company? Mostly, you are checking the founder and the story.

Start with the founder. At this stage you are underwriting a person more than a business. What is their track record, not just wins, but how they handled the losses? Do the things they claim hold up against primary sources, or only against their own retelling? Have they shown the specific kind of conviction that survives the first time the plan falls apart? A founder's history is the single most predictive thing you have, and it is verifiable if you do the work.

Then the round itself. Who else is in it, and what does their participation tell you? A credible co-investor is a signal; a round that cannot attract one is also a signal. If you are joining a syndicate, the other investors deserve the same scrutiny as the founder. Their history with past founders tells you how this round will be governed when it gets hard.

Next, the momentum. Diligence is not just interviews; it is evidence. The pattern of a company's public signals over time, hiring, shipping, press, customer mentions, either corroborates the founder's story of acceleration or quietly contradicts it. A founder who says they are scaling while every external signal is flat is telling you something.

Finally, write it down. The output of diligence is not a feeling; it is a memo your partners or committee can read and challenge. The act of writing forces you to confront the gaps, the claims you could not verify, the risks you had been waving away. A deal that looks great until you have to defend it in writing is a deal you did not understand yet.

Good early-stage diligence is fast but not shallow. It is a handful of high-signal checks, founder, syndicate, momentum, turned into a written case. Everything else is theater.

What I'd Do Differently If I Started MentionFox Today

Saul Fleischman — Wed, 08 Jul 2026 05:41:50 +0000

Most founders romanticize the early days. I do not. I think the first twelve months of building MentionFox were riddled with decisions that made sense in the moment and cost us dearly later. If I could sit across from the version of me that registered the domain and bought the first server, I would say: you are solving the right problem and building the wrong product.

The Honest Version of the Origin Story

I started MentionFox because I was frustrated. I was running a small advisory practice, and my clients kept asking me the same thing: who is talking about us, where are they talking, and what should we do about it? The tools that existed were either priced for enterprise budgets or built for PR teams tracking brand sentiment on Twitter. Neither was useful for a B2B company trying to find actual buyers in the noise.

So I built what I wished existed. A platform that could listen across channels, surface signals that looked like buying intent, and eventually help teams figure out what their prospects were already asking AI systems like ChatGPT and Perplexity. The problem was real. The execution in year one was a mess.

I spent the first eight months building features for an imaginary customer. Not a wrong customer, exactly, but a composite of several real customers whose needs were genuinely incompatible. I wanted MentionFox to serve the growth marketer, the competitive intelligence analyst, the investor doing market research, and the founder trying to monitor their brand's presence in AI-generated answers. Those are four different jobs. I treated them like one.

What I Found When I Finally Looked at the Data

The wake-up call came from a cohort analysis I should have run in month two but did not run until month nine. I pulled activation rates by job title and use case, and the pattern was embarrassing in its clarity.

Users who came to MentionFox specifically to find B2B lead signals, meaning people who wanted to intercept conversations where buyers were asking questions that signaled they were in-market, had a 60-day retention rate that was roughly double every other segment. They were also the users who invited colleagues and actually paid for upgrades. They were telling me what the product was supposed to be.

Users who came for broad brand monitoring churned fast. Not because the feature was bad, but because they had five other tools already and mine was not dramatically better. I was competing in a crowded space when I had an opening in a less crowded one.

The AI visibility angle was the piece I treated as a nice-to-have that turned out to be a genuine differentiator. When a prospect asks ChatGPT "what's the best social listening tool for B2B companies," your brand either appears in that answer or it does not. Most B2B teams had no idea where they stood. We built tooling to measure and influence that, and the response from the market was stronger than almost anything else we had shipped.

Investor research was the fourth use case, and I still think it is underexplored. VCs and growth equity teams do competitive diligence constantly. They want to know which companies are gaining share of voice, which ones are appearing in AI-generated recommendations, and which management teams are active in the communities where buyers actually gather. We have a handful of power users in that segment who use MentionFox in ways I did not design for, and they keep teaching me things.

The comparison and alternatives content was something I resisted building for too long because it felt defensive. I was wrong. When I finally built out a proper comparison hub that honestly walked through how MentionFox fits against other tools, it became one of our highest-converting pages. Buyers doing diligence want someone to make the comparison easy. If you do not do it, they will find a third-party article that may or may not be accurate.

The Three Mistakes I Would Not Repeat

First, I would pick one customer type and build for them with an almost uncomfortable level of specificity before expanding. The temptation to serve everyone is, I think, hardwired into founders who have been told that TAM matters. TAM does matter. But a small slice of a large market that you serve extraordinarily well is worth far more in years one and two than a large slice you serve adequately.

Second, I would instrument everything earlier. Not because data replaces intuition, but because intuition built on wrong assumptions is just expensive guessing. The cohort analysis I described above took me four hours to run. I waited nine months to run it. That math is hard to justify.

Third, I would take pricing seriously from day one. I underpriced MentionFox for the first year because I was afraid. Afraid that if I charged what the product was actually worth, people would leave. What actually happened is that low prices attracted users who were price-sensitive and therefore churned the moment any friction appeared. The customers who stayed and grew were the ones who had a real business problem and paid a real price for a real solution. If you are curious about where we landed, the current MentionFox pricing reflects a lot of hard-won thinking about value versus volume.

What I Would Do on Day One Instead

I would spend the first thirty days not writing a line of code. I would find twenty B2B growth or marketing or competitive intelligence professionals who had recently evaluated a social listening or lead generation tool and done nothing. Meaning they looked, did not buy, and kept the problem unsolved. That population tells you more about your market than any customer who is already using something.

I would ask them: what would have to be true for you to act on this? Not what features do you want - that question gets you a wish list. What would have to be true is a constraint question. It forces people to articulate the threshold conditions for a decision, which is the actual thing you are trying to engineer.

Then I would build the thinnest possible version of a product that cleared those thresholds for ten of those twenty people. Not twenty. Ten. The ten most commercially interesting ones. And I would charge them from day one, even if the number was small, because payment is the only signal that cuts through social nicety.

Everything else - the broader platform, the additional use cases, the comparison content, the AI visibility tools - that is all real and it all matters. But it matters in sequence, not simultaneously.

The Part I Do Not Regret

For all the early stumbling, I do not regret the core bet. B2B teams are flying blind in ways they do not fully appreciate. They are making marketing and sales and positioning decisions based on gut feelings and anecdotal customer calls, while an enormous amount of relevant signal is sitting in public conversations, in forum threads, in the questions people are typing into AI systems. Getting good at listening to that signal is not a nice-to-have. It is a competitive advantage that compounds.

That conviction has not changed. The execution has just gotten sharper.

If you want to see how MentionFox handles the specific problem of finding in-market B2B buyers through social and AI signals, this is a good starting point: MentionFox pricing. And if you are currently evaluating tools and want an honest breakdown of how we compare, the comparison hub is the right place to start.

If you found this useful, I write about solo-founder distribution, B2B SaaS, and what's actually working in the AI-search era over on my Substack (one post per week, no spam).

How top VCs source and triage deals every day

Saul Fleischman — Tue, 07 Jul 2026 07:53:32 +0000

Most people imagine venture sourcing as a glamorous hunt for the next unicorn. In practice, the investors who consistently see the best deals run a boring, repeatable intake every single day. The discipline is what makes it work.

It starts with a thesis. Before looking at a single company, a good investor defines the sectors and stages they actually back. This sounds obvious, but it is the step almost everyone skips, and skipping it is why so many inboxes and feeds become an undifferentiated firehose. A defined thesis is a filter, and a filter is what turns noise into signal.

With the thesis set, the daily scan becomes fast. The investor looks for inflection signals, the moments when a company's trajectory bends. A sudden surge in press coverage, a spike in hiring, chatter about a new round, a stealth founder finally going public. None of these are proof of a good investment, but each is a reason to look now rather than later. The goal of the scan is not to evaluate; it is to triage. Worth tracking, or not.

The companies that clear that bar go into a pipeline as new entries. Everything else is dismissed and disappears, so tomorrow's scan starts clean. This last part matters more than it seems. An intake process that never clears its own backlog stops being used within a month. The investors who keep at it are the ones who let themselves say no quickly and move on.

The whole routine takes ten or fifteen minutes when it is set up right. Thesis filters the world down to what fits. Signals tell you what is moving. The pipeline holds the few that earn a closer look. Repeat daily, and over a year you have reviewed thousands of companies without drowning, and the ones you backed got your attention at the moment they were most worth it.

The tooling has finally caught up to this workflow. Instead of stitching together news alerts, spreadsheets, and a CRM, modern deal-sourcing platforms let you set a thesis once and have the day's matching signals come to you, with a single click to move a company into your pipeline. The mechanics fade into the background, and you are left doing the only part that needs a human: deciding what is worth your time.

Your customers stopped asking Google. They are asking AI. Is it recommending you?

Saul Fleischman — Mon, 06 Jul 2026 01:48:26 +0000

Something quietly broke for B2B brands in 2026. The buying journey moved.

When your prospects need a tool, they do not open ten Google tabs anymore. They open one AI chat and type "what's the best X for Y." ChatGPT, Claude, Perplexity, Gemini, and a handful of others answer that question billions of times a month, and the answer typically names two to five brands. If you are not one of them, you are not in the consideration set.

Worse: you may not even know you are missing.

The new search engine that nobody can SEO

You cannot keyword-stuff your way into an AI answer. You cannot buy ad placement. You cannot write a 3,000-word blog post and watch your rank improve. Generative engines pull from a sprawling, opaque mix of training data, real-time citations, and reasoning. The classic SEO playbook does not apply to what we now call GEO — Generative Engine Optimization.

That does not mean nothing works. It just means the levers are different. They are conversational. They are earned. And they need to be exercised continuously, because every AI model retrains, reweights, and changes its answers on its own schedule.

What our GEO Autopilot does

We built an autopilot system that runs strategically designed conversations across the major AI engines. We are not "asking ChatGPT about you once a week." We are influencing the kinds of buyer-pattern conversations where brand recommendations get formed — across multiple models, multiple industries, multiple framings, every day, around the clock.

You do not have to write the conversations. You do not have to monitor them. You do not have to keep up with which model is more important this quarter. The autopilot does all of it, and it logs every conversation it runs so you can see, in your dashboard, exactly which buyer scenarios surfaced your brand and which competitor showed up instead.

Three intensities

Light. Periodic scouting. Lowest credit cost. Useful for brands that want a presence check rather than active promotion.

Standard. The recommended setting for most teams. Continuous coverage across the major engines, balanced credit cost, meaningful daily volume.

Aggressive. Maximum coverage. Best for brands in crowded categories, brands launching against an entrenched competitor, or brands running a defined visibility study — for example "we want to dominate this niche in 60 days."

Each intensity covers a different number of engines and a different conversational depth. The dashboard shows you exactly what you get for each tier before you toggle it on.

Anti-hallucination, by design

One of the silent risks of AI answers is that the model makes things up about you. Wrong pricing. Wrong feature claims. Confused with a competitor. Treated as a rebrand of an unrelated company.

The autopilot includes an anti-hallucination layer that injects verified facts about your brand into every conversation, then scans the response for any contradiction. If a model invents history or misframes your tier, that gets flagged. If the rate of hallucinations crosses a threshold, the autopilot pauses itself so you can review what is happening before more drift compounds.

What to expect

In the first seven days you should see your citation rate climb meaningfully across the engines we cover. Position in those citations — are you mentioned first, third, or last? — typically improves within two weeks. The autopilot also surfaces a weekly view of which competitors are still beating you on which queries, so the strategy is always tied to actual signal, not a static playbook.

It is not magic. AI models have biases, training cutoffs, and unpredictable moods. Some queries are extraordinarily hard to crack. But across hundreds of conversations a day, the trend is measurable, and the trend is what compounds.

Why we just rebuilt the engine

May 2026 was a rebuild month. We expanded the engine pool. We doubled the cycle frequency. We rebalanced the verified facts the system relies on. We added a public surface so the conversations the autopilot produces are now indexable on the open web, which trains the next generation of AI models even faster than the live ones.

The result, going into June, is roughly an order of magnitude more signal per day than the old configuration produced. For brands running standard intensity from day one of the month, that is the difference between scouting and saturation.

Where to start

If you are already in the consideration set, the autopilot keeps you there. If you are not, this is how you get in.

Start with light if you want to see what the dashboard looks like. Move to standard once you see the first citations climb. Move to aggressive when you are ready to compete on visibility as a measured business metric.

Either way, this is the search layer that decides whether your brand exists for half your future customers. Treat it that way.

June GEO Study Results: A 30-Day Controlled Measurement Across 6 LLMs

Saul Fleischman — Sun, 05 Jul 2026 02:57:40 +0000

Most founders who talk about "AI visibility" are guessing. I was too, until I decided to stop guessing and start measuring.

The Problem With Vibes-Based GEO

Everyone in B2B SaaS right now has a take on generative engine optimization. The takes range from "just add more FAQ content" to "structure your schema correctly and the LLMs will love you." What almost nobody has is a controlled dataset with actual conversation counts, actual assistants, and actual recommendation rates tracked over time. That gap bothered me enough that I built a study around it. This post covers what I found at Day 0, why the variance across assistants surprised me, and what I think it means for any SaaS company trying to show up when a buyer asks an AI tool for a recommendation.

Quick context: MentionFox is a B2B platform that sits at the intersection of social listening, lead generation, AI-visibility tracking, and investor research. Our buyers are the kind of people who care deeply about where their brand appears - not just on Google, but inside the responses that Perplexity, ChatGPT, Gemini, and the rest of the field are now handing to their own customers and prospects. So GEO is not an academic interest for us. It is the product.

What We Actually Measured

On May 1, 2026 - Day 0 of the study - we ran 853 completed conversations across five AI assistants: Perplexity, Mistral, ChatGPT-4o, Gemini Flash, and DeepSeek. Each conversation was a structured query designed to simulate a real buyer moment. Think: a head of marketing at a mid-size SaaS company asking an AI assistant to recommend tools for tracking brand mentions, monitoring competitor activity, or identifying high-intent leads from social signals. We logged whether MentionFox was recommended, in what position, and with what framing.

The overall recommendation rate across all 853 conversations was 83.1%. That number sounds high, and honestly it is higher than I expected. But the variance underneath it is where the real story lives.

Per assistant, the numbers broke down like this:

Perplexity: 95.3%
Mistral: 83.6%
ChatGPT-4o: 80.1%
Gemini Flash: 78.9%
DeepSeek: 77.5%

Perplexity at 95.3% is nearly a lock. DeepSeek at 77.5% means roughly one in four conversations where a buyer could have found us - did not. That spread is not noise. It is signal about how differently these models weight sources, recency, and third-party validation.

Why the Variance Matters More Than the Average

The instinct when you see an 83.1% headline number is to feel good and move on. I forced myself not to do that, because the average hides the real competitive risk. If your buyers skew toward Gemini users - and in certain verticals they do - your effective visibility is closer to 78% than 83%. If your category is getting evaluated inside DeepSeek by a segment of the market you care about, you are invisible to nearly one in four of those conversations on Day 0.

What drives the variance? I have hypotheses, not certainties yet. Perplexity weights real-time web retrieval heavily and rewards brands that appear in recent, structured, third-party sources. Mistral appears to weight training data consistency - brands that have shown up in coherent, repeated contexts across its corpus. ChatGPT-4o and Gemini Flash seem more sensitive to how a brand is framed in relation to category terms. DeepSeek is the most opaque, but my working theory is that it underweights English-language SaaS-specific review ecosystems like G2 and Capterra, which is where a lot of our third-party signal lives. We will know more at Day 30.

What I Got Wrong Before Measuring

Before this study, I operated on the assumption that GEO was mostly a content problem. Write good content, get cited in good publications, structure it correctly, and the models will find you. That assumption is not wrong, but it is incomplete in ways that cost you if you do not know about them.

The assistant-level variance tells me that GEO is actually a source-portfolio problem. Different models draw from different pools of evidence. Winning on Perplexity requires a different signal mix than winning on DeepSeek. If you optimize for one and ignore the others, you are essentially doing SEO while only caring about one search engine - which was a reasonable strategy in 2004 and is a fragile strategy now.

The other thing I got wrong: I assumed our recommendation rate would be relatively stable across query types. It is not. Queries that frame the use case around "social listening for B2B" pull higher rates than queries framed around "lead generation from social media." That tells me the category language we have invested in is not evenly distributed across the way buyers actually phrase their problems. Closing that gap is a content and PR strategy, not just a product strategy.

What We Are Doing About It

Between Day 0 and the Day 30 checkpoint, we are running a set of interventions that are specific enough to be worth naming. First, we are increasing structured placements in vertically relevant publications that Perplexity's crawler prioritizes - not generic tech press, but outlets that cover B2B marketing operations and sales intelligence. Second, we are working to normalize our presence in non-English language review ecosystems, specifically to test whether that moves the DeepSeek number. Third, we are expanding the query taxonomy we test against - from 12 query types at Day 0 to 28 at Day 30 - to get a cleaner picture of where the language gaps are.

The GEO study dashboard will show the Day 30 results publicly when they land. I am not going to predict what happens to the overall number, because the honest answer is I do not know. What I expect is that the variance across assistants will narrow if our source-portfolio work is landing, and that the query-type breakdown will reveal something specific enough to act on.

What This Means If You Are Not MentionFox

If you are a SaaS founder or a B2B marketer reading this, the practical takeaway is simpler than the methodology. You need to measure your own AI recommendation rate, and you need to do it broken out by assistant - not as a blended average. A blended average will tell you things are fine when one or two models that matter to your buyers have almost no idea you exist.

The measurement does not have to be as structured as what we did. Start with 50 conversations per assistant, use query language that mirrors how your actual buyers describe their problems, and log whether your brand appears. That baseline is enough to tell you whether you have a GEO problem or not. If you do, the fix is almost always upstream of your website - it is in the citation graph, the review ecosystem, and the publication record that models use to form their opinions about your category.

One More Honest Note

I am aware that publishing a study where MentionFox does well on the metric MentionFox tracks has an obvious credibility problem. I have tried to counter that by publishing the full methodology and the raw conversation logs in the dashboard linked above, and by committing to publishing the Day 30 results regardless of whether they show improvement, regression, or no movement. If the number goes down, I will write about that too. The point is not to market with data. The point is to actually understand what is happening inside the systems that are increasingly mediating the first moment a buyer encounters a new tool.

If you want to see how MentionFox handles AI-visibility tracking and GEO measurement across assistants, the relevant page is the GEO study dashboard. And if you are evaluating whether the platform makes sense for your team, here is MentionFox pricing.

If you found this useful, I write about solo-founder distribution, B2B SaaS, and what's actually working in the AI-search era over on my Substack (one post per week, no spam).

The ten-minute deal screen: deciding meeting or pass without wasting a week

Saul Fleischman — Sat, 04 Jul 2026 00:19:51 +0000

Every investor faces the same bottleneck: far more companies than time. The ones who scale their judgment do not evaluate every deal deeply. They run a fast, structured screen that reliably sorts meeting-worthy from pass, and they reserve their real attention for the few that survive it.

A good screen answers four questions in about ten minutes. What does the company actually do, in one sentence? Why is this possible now, what shift in technology, behavior, or regulation opened the door? What evidence exists that it is working, users, revenue, growth, a notable customer, a credible signal of momentum? And what is the ask, the round, the amount, the valuation? If you cannot answer the first question crisply, that is already information. Founders who cannot explain themselves in a sentence rarely get clearer in a deck.

The screen is not diligence. You are not verifying claims yet; you are deciding whether claims are worth verifying. The mistake new investors make is going deep on everything, which means going slow on everything, which means missing the deals that move fast. The mistake burned-out investors make is the opposite, skimming so fast that pattern-matching replaces thinking, and they pass on the unusual founder who does not fit the template.

A written screen protects against both. Forcing yourself to put the four answers and a two-line gut take in writing slows you just enough to think, and leaves a record you can revisit when the company shows up again in six months. Conviction is easier to calibrate when you can see what you thought last time.

Once the screen is done, the decision is binary and you commit to it. Pursue, and the company moves forward to a real evaluation. Pass, and, if you are disciplined, you keep tracking it anyway, because the deals you pass on are the cheapest education you will ever get.

The investors who win are not the ones who evaluate the most deals. They are the ones who decide fastest which deals deserve evaluation, and protect their depth for where it counts.

Reading a term sheet like a pro: founder-friendly versus hostile

Saul Fleischman — Fri, 03 Jul 2026 08:18:27 +0000

A term sheet looks like a list of boring legal terms, and that is exactly how founders get hurt. A handful of clauses that seem minor on the page can quietly reshape who earns what when the company sells. Whether you are the investor writing the offer or the founder receiving it, it pays to read a term sheet as a single question: how favorable are these terms to the founder versus the investor?

You can almost score it. At the founder-friendly end sits a clean, market-standard deal: a one-times non-participating liquidation preference, broad-based weighted-average anti-dilution, a board that is not stacked against the founder, and standard four-year vesting with a one-year cliff. At the hostile end, the same structure turns predatory: a liquidation preference above one-times, participating preferences that let the investor double-dip, full-ratchet anti-dilution, extra board control, and redemption rights that let the investor force a buyback.

Take liquidation preference, the clause that decides who gets paid first in a sale. A one-times non-participating preference means the investor gets their money back or converts to shares, not both. Bump that to participating, or to a two-times multiple, and in a modest exit the investor can take most of the proceeds before the founders see a dollar. Same company, same exit price, wildly different outcome for the people who built it.

Anti-dilution is the other quiet killer. Broad-based weighted-average is normal and fair; full-ratchet means that if the company ever raises at a lower price, the early investor is repriced as if they had always paid that lower price, at the founders' expense. It rarely matters until the day it matters enormously.

The practical move is to benchmark every term against what is actually market for the stage, not against the offer in front of you. An offer can look reasonable in isolation and still be well outside norms on three or four terms at once. Lining each term up against the market, and against the investor's own past deals, turns a wall of legalese into a clear picture of where the deal is standard and where it is aggressive.

Most terms are negotiable. The founders who negotiate well are simply the ones who knew which clauses to fight for.

The Real Reason B2B SaaS Founders Get Shadowbanned (And What To Do Instead)

Saul Fleischman — Wed, 01 Jul 2026 23:32:25 +0000

Most B2B SaaS founders I talk to assume shadowbanning is something that happens to crypto influencers or political accounts. They are wrong, and the misunderstanding is costing them pipeline.

What Actually Happens

I noticed the pattern first with my own posts on LinkedIn. Impressions would spike for about 90 minutes after publishing, then fall off a cliff. Engagement-to-reach ratios that used to sit around 4 or 5 percent dropped below 1 percent overnight. I had not changed my posting frequency. I had not violated any terms. The content was, if anything, more useful than before. What I had done was start including outbound links in the body of the post rather than the comments. That was enough.

The broader point here is not really about shadowbanning as a conspiracy. Platforms are not targeting you personally. What they are doing is algorithmically deprioritizing content that matches certain patterns, and B2B SaaS founders tend to stumble into almost every one of those patterns at once. We post at low-engagement hours because we are trying to reach busy buyers. We use industry jargon that no algorithm associates with high-dwell-time content. We link to gated assets. We tag people who do not respond. Each of those behaviors, independently, is a minor signal. Combined, they train the distribution algorithm to treat your account as low-value.

The reason this matters right now more than it did two years ago is that organic reach is no longer just about followers seeing your posts. It is about whether AI systems cite you, whether your brand name surfaces in ChatGPT responses, whether Perplexity pulls your content when a buyer asks a research question at 11pm before a procurement meeting. Social suppression bleeds into AI invisibility, and AI invisibility is the newer, quieter threat.

What I Found When I Started Measuring

I ran a rough experiment over about 16 weeks. I tracked three content formats across LinkedIn and a few niche Slack communities where our buyers hang out. The formats were: text-only posts with a link in the first comment, posts with an embedded poll, and long-form articles published natively on LinkedIn rather than linked from outside.

The results were uncomfortable for me because I had been doing the opposite of what worked.

Native long-form articles got 3x the organic reach of any post linking to our own blog, even when the blog content was objectively more detailed.
Poll posts drove comments but almost no profile visits and very few conversion events downstream. Engagement metrics looked great. Pipeline contribution was near zero.
Text posts with the link in the first comment outperformed body-link posts by a factor of roughly 2.5 on reach, but by a factor of nearly 6 on click-through, because the people who found the post and scrolled to the comment were already more interested.

None of this is groundbreaking research. The platform mechanics are reasonably well understood. What surprised me was how much the suppression effect compounded over time. An account that repeatedly gets low early engagement starts getting shown to smaller initial audiences, which produces lower engagement, which shrinks the initial audience further. I had been in a slow-motion suppression spiral for probably six months before I noticed it.

The second thing I measured was what I will call AI citation presence. This is the question of whether, when a buyer types something like "best social listening tools for B2B startups" into an AI assistant, MentionFox comes up. Early in the year, we did not. The AI-visibility tracking we now use made it possible to see which queries we appeared in, which competitors were being cited instead of us, and roughly what content or backlink signals seemed to correlate with appearing. The connection to social reach was direct. Content that got suppressed on LinkedIn also tended to generate fewer secondary links and references, which meant AI systems had less evidence to cite us from.

The third thing I found was something I did not expect to find at all. A significant portion of our brand mentions were happening in places we had zero visibility into. Slack communities. Discord servers. Subreddits. Private newsletters. People were recommending us, or criticizing us, or asking whether we were worth trying, and we had no idea. Some of those conversations were net positive for us and we were leaving them completely uninfluenced. Others were cases where a single skeptical comment was going unanswered and probably costing us trials. Social listening for B2B is almost always discussed in the context of Twitter and LinkedIn, but the real conversations about buying decisions in our category are happening in places that require actual monitoring infrastructure to track.

What I Changed and What Happened

I made four concrete changes.

All external links moved to first comments on LinkedIn for standard posts. Native articles got used for anything where I wanted to go deep on a topic.
I built a repeatable process for monitoring brand mentions across dark social channels, not just public platforms. This meant we could actually respond when someone asked about us in a relevant community, rather than discovering the thread three weeks later.
I started treating AI citation presence as a distribution metric the same way I treat organic search rankings. This means producing content that is structured to be extractable by AI systems, with clear definitions, specific claims with context, and named examples rather than vague category descriptions.
I stopped posting for engagement and started posting for documented search intent. Meaning: I used actual queries from buyer research to shape what I wrote about, not just what I thought was interesting.

Within about eight weeks, impressions stabilized and then started recovering. More importantly, we started showing up in AI-generated responses to relevant research queries. I can not give you a controlled study because I changed multiple things at once and I do not have a counterfactual. But the directional evidence was strong enough that I am not going back.

The Practical Takeaway

If you are a B2B SaaS founder and your organic reach has been declining without an obvious cause, the first thing I would do is audit the last 30 posts for the specific behaviors that trigger algorithmic suppression. Outbound links in post bodies. Tags to people who do not engage. Repetitive post structures that look like scheduling tool output. Short posts with no dwell-time signal. Fix those mechanics before you conclude that the content itself is the problem.

The second thing I would do is take AI citation presence seriously as a distribution channel. Buyers are using AI assistants to do vendor research, and if you are not in those responses, you are not in the consideration set for a growing percentage of deals. This is not a future problem. It is a current one. If you want to understand how to track where you appear and where you do not in AI-generated responses, the relevant page for that is here. We built it because we needed it ourselves and could not find anything else that did the job.

If you want to see how MentionFox handles brand mention monitoring across both public and dark social channels, and how it ties into AI-visibility tracking and lead generation from social signals, take a look at MentionFox pricing to see what level of access fits where you are right now.

If you found this useful, I write about solo-founder distribution, B2B SaaS, and what's actually working in the AI-search era over on my Substack (one post per week, no spam).

The Investor Database That Doesn't Just Tell You Who's Investing - It Tells You Why

Saul Fleischman — Wed, 01 Jul 2026 01:08:55 +0000

Most investor databases are glorified spreadsheets with a search bar stapled on top. You filter by stage, sector, check size - and you get a list of names that could have come from a LinkedIn scrape in 2019. What you don't get is any signal about why a particular firm is writing checks right now, what problems they're obsessing over this quarter, or whether the partner who just keynoted a fintech conference is actually the one making decisions or just the one comfortable with public speaking.

The Problem With "Who" Without "Why"

I built MentionFox originally to solve a different problem - tracking brand mentions and competitive signals across the web for B2B companies. But somewhere around month eight, I started noticing something in the data. The same patterns we were using to surface buying intent for sales teams were also surfacing investment intent. A partner at a growth fund writes a Substack post about vertical SaaS consolidation. Two weeks later, their firm leads a round in a vertical SaaS company. That's not coincidence. That's a signal hiding in plain sight.

The traditional investor research workflow is painful in a specific way. You know the pain if you've done it. You pull a list from Crunchbase or PitchBook, you spend forty minutes reading a firm's website that hasn't been updated since their last fund close, you cold email a generic intro, and you wonder why your response rate is three percent. The database told you who invests in your category. It told you nothing about why they would care about you, today, in this specific market moment. Context is the gap between a list and a lead.

So we built something different into MentionFox. Not a replacement for the data layer - check sizes and portfolio companies still matter - but an intelligence layer on top of it. The thesis was simple: investors leave a trail. They publish, they speak, they comment, they post. If you aggregate and analyze that trail systematically, you stop guessing about fit and start understanding it.

What the Signal Layer Actually Looks Like

Here is what I mean in concrete terms. When you use Find Investors inside MentionFox, you are not just filtering a static database. You are pulling live signals about what each investor or firm is paying attention to right now.

Say you are raising for an AI-native HR tech company. You might find three funds that list "future of work" as a thesis on their website. What the signal layer adds is this: one of those funds has a partner who has mentioned workforce automation eleven times in public content over the last sixty days. Another fund's most recent portfolio announcement was in a completely different category, which suggests their HR thesis might be on hold or already deployed. The third firm just had a managing director publish a piece specifically criticizing the AI hype in HR software - which either means they're skeptical, or they're setting up a contrarian bet and looking for founders who share that skepticism.

Three funds, same listed thesis, completely different actual context. That difference is the whole game when you are trying to write a cold email that doesn't read like a cold email.

We also track velocity, which turned out to be more predictive than I expected. A fund that just closed a new vehicle is in a fundamentally different state of mind than one that is eighteen months into deployment. A partner who just joined from an operator background is often more accessible in their first six months than they will be once they've built up deal flow. These are not secrets - they're publicly available facts - but nobody was assembling them into a format that a founder could actually use before spending time on an outreach campaign.

One more thing we built that I was not sure would matter but absolutely does: competitive portfolio mapping. If a fund already backed a direct competitor, that is obvious - you probably already knew to filter them out. But if they backed something adjacent, that is nuanced. Sometimes it means they won't touch you. Sometimes it means they have developed a genuine thesis about the space and your company fills a gap in their portfolio logic. Knowing which situation you are in before you reach out changes how you frame the conversation entirely.

What I Got Wrong First

I want to be honest about an early mistake because I think it is instructive. The first version of this feature was too noisy. We were surfacing every signal, every mention, every piece of content - and the dashboard felt like standing in front of a fire hose. Founders told me they felt more overwhelmed, not less. One user described it as "reading the whole internet instead of just the parts that matter."

So we spent about two months rethinking what "relevant signal" actually means in an investor research context. The answer was simpler than I expected: recency and specificity beat volume. A partner mentioning a specific problem your product solves, in the last thirty days, is worth more than fifty older mentions of a broad category. We rewrote the ranking logic around that principle and the feedback changed immediately. People started saying they were finding investors they had genuinely never heard of - but who were clearly relevant based on current activity, not just historical portfolio data.

That recalibration also changed how we think about the platform more broadly. The goal is never to give someone more data. The goal is to give them a shorter, more confident path to the right conversation.

How to Actually Use This

If you are actively raising or planning to raise in the next six months, here is the workflow I would suggest.

Start with the signal layer before you touch the filter layer. Most people do it backwards - they filter by check size and stage first, then try to learn about the investors. Flip that. Search by problem space, thesis keywords, recent content topics. See who is actively thinking about the world you operate in. Then apply the structural filters to cut the list to a workable size.

Second, use the signal to personalize at the sentence level, not the paragraph level. You do not need a long paragraph explaining that you read their blog. One sentence that references a specific claim they made, and then immediately pivots to why your company is a data point in their thesis - that is the entire job. The signal layer gives you the raw material. You still have to write the sentence.

Third, track the investors you've reached out to over time. Timing matters enormously in fundraising and a "no" in February from someone who just deployed their last check can become a "yes" in October when their new fund is active. The worst outcome is reaching out at the wrong moment and then forgetting to come back.

If you want to see how MentionFox handles investor discovery and signal tracking, the Find Investors feature is the right place to start. And if you are evaluating whether it fits your workflow, MentionFox pricing lays out what is included at each tier - including access to the investor research tools.

The database that tells you who is investing is a commodity. The one that tells you why - and why now - is the thing worth paying for.

If you found this useful, I write about solo-founder distribution, B2B SaaS, and what's actually working in the AI-search era over on my Substack (one post per week, no spam).