Go-to-market articles and insights

Most teams adopting AI for delivery are optimizing for one thing: speed of adoption. Pick a tool, build it into your workflows, and move. It feels fast because you are moving fast. But nobody stops to ask the harder question. What happens when a tool in your new workflow doesn't show up for work?

Or worse: what if the platform you run those tools on is just having a bad day and calls in sick?

We found out yesterday.  

Claude is our primary work surface. We run most of our client delivery through it, both Claude Cowork and Claude Code, and it has done a lot to make a messy pile of client workflows feel like one place to work. Then, on a heavy delivery day when we needed Claude to show up, our team got the ominous service outage notice.

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Claude was down. And this time, it felt like half our company didn't show up for work.

If you use Claude as often as we do, you know outages aren't uncommon. To their credit, they are shipping at a crazy pace, and it would be even wilder if there was never a disruption! But as we scale the codebase our delivery system runs on, those outages hit harder. We rely on the platform more every month, and that reliance has created a new risk surface.

For an AI-native business like ours, a single work surface like Claude is now, besides our people, the most consequential operational risk we carry.

The real cost isn't downtime. The cost is going backward.

When people picture an AI outage, they picture work stopping. In real life, and as an agency operating in this mode everyday, you don't stop; you fall back to the old way of working.

Once you’ve run at the pace these tools allow, the old way is genuinely painful. The whitepaper that took a morning is back to a week of copy-paste and formatting. The ICP you would normally call up in seconds turns into a hunt through Google Drive. The article you would have shipped by lunch waits. Your clients' expectations don't reset because your tool had a bad day. The turnaround they now count on was set by the fast version of you, and on the slow morning you still owe it. That gap between the speed you built the business around and the speed you can hit without your stack is the cost. You feel it in every deliverable that used to be quick.

And the exposure runs deeper than the tool you work in. There are two layers here, and most teams have a backup for neither.

The first layer is the harness, the surface you actually do the work in (think Claude Cowork, chatGPT, etc). The second is the connectors, the pipes into the systems where the work lands. Webflow. Granola. HubSpot. Your CMS and CRM. Those connectors are not resilient in themselves, and they’re constantly changing. This morning, Webflow would not connect from Claude Cowork at all, while it worked fine from Claude Code and from every other AI tool we tried. Same company, same login, one path broken and the rest fine. A connector failure drops you back to the old way just as fast as a harness failure. You are only as portable as your weakest layer.

How we got back up in fifteen minutes

We recovered fast, and not by luck. We recovered because of how the stack was built.

Everything we do is a skill. A skill is a markdown file with instructions, a saved recipe the AI follows. Brand voice, ideal customer profile, whitepaper generation, each one is its own file. Those skills are grouped by client into plugins. The plugins live in a marketplace, and the whole thing is managed in code in GitHub. Our delivery stack is, in effect, a codebase. It is built on an open standard for defining a skill, not on anything proprietary to a single vendor.

That one design choice is what saved the morning. Because the stack was code and the format was open, we pointed it at a different engine. We took the same GitHub repository into Grok, then into Codex, launched it from the same folder the same way we would in Cowork, and ran the same client skills. There was some translation to get another engine to read a stack we had first shaped for Claude, but the assets themselves moved cleanly. From dark to running took about fifteen minutes.

The swap was natural because we were already multi-tool by choice, not just in a crisis. Claude is home base, but we run Grok and Codex for the workflows they handle better. Different engines are genuinely better at different jobs, so we already reached for the right one per task. The outage just proved that the same portability we used for fit, we could use for survival.

Think portable, single sources of truth

The reason the move worked is not the specific tools. It is that we treat marketing delivery the way an engineer treats a codebase, and one principle carries most of the weight. A single source of truth.

In our world, that means every process, standard, and reference we reuse lives in exactly one place, as a skill. A skill packages the knowledge of a workflow, a brand standard, a data mapping, or a template, with its own inputs and outputs. When a delivery surface needs that knowledge, it references the skill. It does not copy it.

That one rule shuts down the two failures that turn AI adoption into chaos. The first is replication: a dozen versions of the same messaging framework scattered across delivery folders, each drifting a little further from the last, and a token tax paid every time someone hunts for the right file. The second is reinvention: two people rebuilding the same ICP or the same whitepaper process from scratch because neither knew the other had already done it. Single-sourced skills remove both. Improve the skill once and every workflow that calls it improves with it.

The payoff is composability. Skills stack into plugins, plugins are organized by client account, and a workflow gets assembled from single-sourced parts instead of rebuilt by hand. That is what lets us run our execution engine as code. It is also the difference between a pile of disconnected Claude chats each improvising and one organized, efficient, AI-native delivery motion.

Here is what that looks like in practice. Take an ideal customer profile. One approach is to write it up, save the document in the client's delivery folder in Google Drive, and move on. It works until you try to use it. Now someone has to remember where it lives, point the AI at it, and spend tokens loading it every single time. Worse, if your work is scoped to a subfolder, the tool may never look up the tree to find it at all. The ICP is right there and still out of reach.

The better version is to promote that finished ICP into a skill. Once it is a skill, it behaves like an imported library. You call it from anywhere, it loads on demand, and it reads the same every time. That is the difference between a filing cabinet and a system. It is the same property that let us move our entire stack into a new engine overnight. The ICP-as-skill and the fifteen-minute recovery are the same principle at two sizes.

Thinking like an engineer inside a marketing team is what pays off here. Same discipline, applied to a different kind of output.

Your over-reliance on Claude risk won’t go away on its own

It is tempting to treat all of this as temporary. Pick whichever model is winning, wait for it to pull ahead for good, and skip the hassle of staying portable. That bet misreads the moment.

We are not heading into a winner-takes-all world where one model rules them all. The lead changes every few months. The engine that is best at long reasoning today gets passed on coding next quarter, then leapfrogged again. That is exactly why we already run Claude, Grok, and Codex side by side, each for what it does best. Betting your delivery on one model winning permanently is betting against the one thing you can count on right now, which is that things keep changing.

And here is the part that holds even if you disagree. Say one model did win outright and stayed ahead. You still would not want your whole company wired to a single vendor's uptime, pricing, and roadmap. Over-dependency is a weak position even when the thing you depend on is excellent. Great today does not owe you great, available, and affordable tomorrow.

Change is constant right now. That makes a backup plan cheap insurance, and it makes skipping one an expensive mistake to get wrong, because the alternative when it breaks is the painful, slow way of working you already left behind.

What it costs to skip this

If you don't build this way, you are one bad morning away from being dragged back to the old pace. And the trigger is not yours to pull. It belongs to a vendor.

You will have rebuilt your business around a speed you cannot reliably hit. Every asset trapped in one tool or buried in a folder is a dependency you cannot see until it snaps you backward. Teams that skip this keep reinventing the same deliverables, and they keep getting surprised when a company they don't own decides how fast they get to move that week.

Redundancy here is not overhead. It is what protects the pace your client promises now depend on.

Oh, and just in case you need this later: https://status.claude.com/

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Earlier this month Chris and I got to spend time with a graduate-level marketing class at the University of Washington. It was a fun group, and the goal was simple: give them an honest look at what day-to-day marketing actually feels like inside a GTM agency like Trelliswork, and where the work seems to be heading. We talked through how much has shifted, and we kept circling back to one idea that matters more now than at any point in our careers: the way you learn marketing has to change too.

Here is what we walked through.

Marketing has changed

The fundamentals still hold. Know your audience, tell a clear story, earn attention. But the surface area of the job is bigger now. A marketer touches data pipelines, automation, AI tools, and code on a normal Tuesday. The role looks less like a campaign planner and more like a combination of builder and air traffic controller, making the thing and directing everything moving around it.

It's more technical than ever

You no longer hand off the technical work and wait. The people producing the best output are the ones who can open a terminal, wire up a tool, and ship the thing themselves. Comfort with technical work is becoming the difference between a good marketer and a stuck one.

You have to jump in headfirst

You don't learn this from a syllabus. You learn it by staying curious, trying new tools and approaches before you feel ready, and looking for better ways to work along the way. The students who get ahead will be the ones who stop waiting for permission and start experimenting.

Everyone is relearning

Everyone is learning the new tools at the same time, so on the technical side the gap between a student and a veteran is smaller than it has ever been. But experience matters more than ever. Judgment, taste, and knowing what good looks like are valued above anything else, because the tools change but the ability to point them at the right problem does not. That is the opportunity: pair fast learning with real judgment and you become hard to replace.

Think in code

Code is becoming the medium for now, not just the back end. I say for now because coding may not stay as important as it is today. But in this moment, code gives you more control over the models than any other interface. When you can describe what you want and build it directly, you skip the handoffs, the misread mockups, and the version sprawl. It also lets you get to discrete points in the creative process, returning to any version or branching from it instead of working off one file that keeps changing under you. You stop decorating slides and start shipping work.

The tools have all changed

The stack you learn on today will not be the stack you used a few years ago, because most of it did not exist yet. Claude, RB2B, Granola, Clay, Gemini, Riverside. These tools enable more than 80% of the work we deliver for clients, and none of them existed five years ago. Learning a fixed set of tools is no longer the goal. Learning how to pick up new ones fast is. We evaluate new tools on an almost daily basis. The only real choice is to keep adapting and evolving. And to be fair, it is exhausting trying to keep up.

What this looks like in practice

To make it concrete, we showed the class three whitepapers we built for three different clients. Every one was created and published entirely in code, from a terminal. No design tool, no page editor, no dragging pixels around a canvas.

The point was not that the work built itself. It was that we got to rethink the whole process. When the document lives in code, a round of feedback is a change in the source, not a manual rebuild. A revision that used to mean a designer, an export, and a reformat became a single pass. So the cycle time went from days or weeks to minutes.

That is the shift. Not a faster version of the old process, but a different process altogether.

The takeaway

Marketing rewards people who build. If you are early in your career, that is good news. The tools are new enough that effort and curiosity can outpace tenure, but the point is not to beat experience. It is to gain it faster. Curiosity is the bridge. It lets you take on real work sooner, see more reps, and build judgment years ahead of schedule, and experience will always be what matters. Open the terminal, make something, learn from it, and do it again.

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For most of my career, writing was how I thought. Not a way to communicate ideas I'd already formed — the actual mechanism for forming them. Sitting down to write forced clarity. It revealed the gaps in an argument, surfaced the assumptions I hadn't examined, and turned vague instincts into something I could actually defend. Writing is thinking. I built a lot on that foundation, and I still believe it.

But something has shifted. And the more I sit with it, the more I realize it's simply a latency problem.

The ideas that matter most in a content program aren't the ones you schedule time to develop. They're the ones that surface in motion — on a call, between meetings, in the ten minutes after a conversation where something clicked. By the time you've cleared space to write, the latency has already done its damage. The moment has cooled. What you produce is a reconstruction: technically accurate, but missing the charge the original idea carried.

I've tried the usual fixes. Notes apps, calendar blocks, drafting on my phone. None of it addressed the real issue, which is that writing — even when you know exactly what you want to say — puts translation cost between the idea and the output. Attention, word selection, structure. You're spending cognitive load on the container instead of the content, and the gap between when you had the thought and when you finally sit down to write it widens that cost further.

That's what pushed me toward video capture. Not as a content format, but as a mechanism for closing that gap. Talk through the idea the moment it exists, in your own voice, before it flattens into something more considered and less alive.

What I found on the other side of that shift — for us and for the clients we've built this with — is a simple reframe:

The content problem isn't really a writing problem. It's a capture problem.

Start with the capture, not the content

Most teams approach content creation backwards. They decide what they want to publish, then go looking for someone to produce it. That model works fine when you have dedicated content staff. For everyone else, it creates a dependency loop: the subject matter expert is already overcommitted, the content request sits in a queue, and the moment passes.

Flip it. Make capture the habit, not publishing.

When an idea surfaces — a customer question, a pattern you're seeing in deals, a reaction to something happening in the market — spend two minutes on camera. Don't script it. Don't overthink the framing. Just talk through the core point like you're explaining it to a colleague. That's it. The recording goes to a content workflow, and the downstream pieces get produced from there.

This works because the barrier to hitting record is close to zero. Writing a LinkedIn post from scratch takes 20 to 45 minutes if you want it to be good. Talking through an idea takes two minutes, and the quality of insight is actually higher because you're not spending all your cognitive load on word selection.

The goal is to get that barrier as close to zero as possible. Riverside's mobile app gets you most of the way there — pull it out in the parking lot after a call ends, record while the conversation is still fresh, and put your phone back in your pocket. No desk required, no setup, no scheduling. The idea stays alive because you captured it at the moment it existed, not an hour later when you finally had time to sit down.

The workflow: capture to content

Once you have a recording, the rest follows a predictable path.

Your capture tool generates a transcript automatically. Don't clean it up too much — you want the natural language and rhythm intact. That transcript is your source material. Everything else gets built from it.

From there, pull the core argument. What's the one thing this capture is really about? That single point becomes your anchor for every downstream piece.

Then remix. A single capture maps to multiple content types:

• A LinkedIn post (the punchy, direct version of the core point)
• A website article or blog post (expanded context, practical guidance, SEO-ready)
• A newsletter section (shorter, more personal, written for subscribers who already know you)
• A short video clip (if the capture quality supports it, used natively on LinkedIn or in outreach)
• A graphic or visual asset (quote card, stat, or framework visual)

You won't produce all five from every capture. But you'll consistently get two or three, and the effort stays roughly the same regardless of output count because the hard part — the idea — already exists.

The tool stack

You don't need much. The goal is fewer tools in the chain, not more.

Riverside is the best option we've found for this workflow. It handles async recording with no scheduling required, generates a clean transcript automatically, and includes basic editing tools for shareable clips. It replaced a whole cluster for us: Zoom for recording, Otter for transcription, Descript for editing, a separate clip tool, and a scheduler. One platform. The mobile app extends all of it to wherever you are, so capture happens when the idea surfaces, not when your calendar allows it.

For teams that want to go further, we set up dedicated recording studios for our clients — a physical space in the office with a camera, a clean background, good lighting, and Riverside already open. No configuration, no fumbling with settings. You walk in, hit record, walk out. The setup effort happens once. After that, the only thing standing between an idea and a captured asset is the decision to walk into the room. That's what point-and-capture looks like in practice: a system where the bottleneck is the idea itself, not the infrastructure around it.

On the content production side, the transcript feeds into whatever writing workflow you already use. A content team works from it directly. AI-assisted tools take it as input. Either way, the raw material is specific and real, which produces better output than prompting from scratch. Connect your standard publishing tools on the back end — CMS, LinkedIn, email platform — build a simple queue, and the pipeline runs.

Why we like Riverside

We're not affiliated with Riverside. We just use it, and it's earned its place.

The capture piece gets all the attention, but what keeps us on it is everything else that comes with the recording. Audio leveling handles the difference between someone who's three feet from their mic and someone who's on a laptop speaker across a noisy office. Filler word removal cleans up the ums and ahs before the transcript hits your workflow, which matters more than you'd think when you're using that transcript as raw content material. Auto captioning means your clips are ready to post natively — no extra step, no third-party tool. Brand kits let you apply consistent visual styling across clips without touching a design tool every time.

For a two-minute capture, that's a lot of production value baked in before a content person ever touches the file.

Is it perfect? No. The organization layer is honestly a bit of a mess right now — finding older recordings, managing projects across multiple contributors, keeping things tidy at scale — it's not where it needs to be. They know it, and from what we're told, it's being worked on. In the meantime, the dedicated Studios feature (available on the Business tier) does a lot of the heavy lifting. It gives you a structured home base for captures, keeps contributors working in a defined space, and reduces the "where did that recording go" problem that tends to crop up as usage grows.

The production quality you get out of a two-minute phone capture is genuinely good. Good enough that we've used raw clips directly in client content without any additional editing. That's the bar we needed to clear, and Riverside clears it.

Better results with less busywork

More content, produced faster — that part is obvious. The structural fix underneath it is less talked about.

Most content programs die from coordination overhead. You need a meeting to brief the subject matter expert. You need a follow-up to get their review. You need another round to reconcile their edits with the brand voice. By the time the piece publishes, the moment it was relevant has passed.

Video capture kills most of that coordination. The subject matter expert contributes raw material in two minutes with no dependencies. The content workflow runs downstream from there. You're not chasing people for drafts — you're processing what they already gave you.

The other thing it fixes is access. When capture friction is low enough, the people who actually know the customer start contributing consistently. That's where the best content comes from — not content teams writing in a vacuum, but the people having the real conversations.

Getting started

You don't need to build the full system before you start seeing value. Start with three things.

Pick your capture tool and set it up for async recording. Establish a simple habit — record when an idea hits, not when you have time to produce content from it. And designate someone to run the downstream workflow, whether that's a content team member, an agency, or an AI-assisted process.

Run it for 30 days. Measure how many captures you produce versus how many content pieces ship. Adjust from there.

The teams that get this right aren't the ones with the biggest content budgets. They're the ones who found a way to make showing up consistently easier than not showing up. Video capture is how you get there.

Trelliswork helps B2B teams build content systems that scale without scaling headcount. If your content program is stuck on consistency, let's talk.

We're not Reddit marketing experts. We're a B2B GTM team that kept seeing the same thing from different angles: buyer conversations on Reddit that weren't happening anywhere else, AI answers citing Reddit threads over vendor content, and data suggesting the channel deserves more attention than most B2B teams give it.

This is Part 1 in a series where we evaluate Reddit as a B2B GTM channel. This piece covers why we think it deserves serious consideration. Part 2 gets into the practical side: how to actually show up without getting ignored or banned.

Where your buyers are actually talking

Reddit is one of the most-visited websites in the United States, with 121.4 million daily active users as of Q4 2025. It's where your buyers go when they want an honest answer, not a vendor blog. A peer conversation with someone who's used the product and has no reason to sell them anything.

The modern B2B buying journey is largely self-directed. Buyers complete most of their research before they engage with sales, and they're turning to communities where the answers aren't sponsored. According to Reddit's advertising materials citing Comscore data from June 2025, 61% of B2B decision-makers are active on Reddit, and 38% of those buyers aren't on LinkedIn at all. Reddit also cites that 72% of tech decision-makers use the platform for peer reviews and 49% for active product research. (These figures come from Reddit's own marketing, so take them with appropriate context.)

The intent is different from other platforms. When someone types a question into r/marketing or r/revops, they're looking for an answer to a specific problem. That research posture makes Reddit one of the highest-intent channels in B2B. The smaller, more focused communities like r/revops, r/CRM, and r/marketingops consistently carry more purchase intent per conversation than the large ones.

What's possible on Reddit

Reddit supports more than comments: long-form posts, live AMAs, newsletter distribution, and paid advertising. The posts that drive engagement are specific, backed by data, and written from direct experience. The model is to publish the kind of content your marketing team usually saves for gated assets and give it away for free.

Comments are probably the most underrated GTM activity on the platform. When a thread in r/SaaS asks "what tool does your team use for X?", that's a public buying signal. A useful, honest response gets upvoted, indexed by Google, and cited by AI systems for years. The challenge is volume. It gets overwhelming fast. Tools like Reddit Pro help by surfacing threads that match your target language so you're responding to the ones that matter, not trying to read everything.

The format that works: give a concrete answer first, cover tradeoffs honestly, and end with a soft mention — "we cover more of this in our newsletter if you want to go deeper." Value first, link last.

On the paid side, Reddit offers subreddit-level targeting that puts your message in front of communities defined by professional interest. CPCs run 50-70% lower than LinkedIn for comparable B2B audiences ($0.50-$2.00 vs. $7-$12). Reddit's ad business grew 74% year-over-year in Q3 2025, reaching $2.2 billion in total annual revenue. That cost gap won't hold forever.

Does Reddit content age better?

A LinkedIn post has a shelf life of 24 to 48 hours. A Reddit comment can rank in Google for years and gets pulled into AI-generated answers indefinitely. According to Semrush's analysis of 150,000+ AI citations, Reddit is the most-cited source across major AI platforms including ChatGPT, Perplexity, and Google AI Overviews.

Google has increased Reddit's visibility in search results. Threads now regularly rank on the first page for B2B software comparisons. Tailscale's Reddit engagement, built through months of technical participation with no promotional agenda, has produced more than 1,300 subreddit discussions ranking in Google search results and thousands of monthly referral visits. All organic, no ad spend.

For organic GTM, the asset doesn't depreciate when you stop spending.

The trust factor

Buyers know the difference between a case study written by the company and a peer recommendation from someone with no stake in the outcome. According to Reddit's own research (a July 2024 survey of 1,250 business decision-makers), 90% of Reddit users trust the platform to learn about new products and 74% say it influences their purchasing decisions. These are Reddit's numbers, not independent research, but the directional signal is hard to ignore.

When someone from your team shows up in r/revops and gives a useful answer, it reads like expertise, not advertising. The companies doing this well treat Reddit as a trust-building channel that makes every other part of their funnel work better.

Companies worth studying

Shopify built r/Shopify into a community hub with over 274,000 members. Team members participate across multiple subreddits without a promotional agenda. The community now generates its own brand advocacy and Google-ranked discussions without Shopify having to push it.

Tailscale built credibility in r/sysadmin, r/devops, and r/networking — communities hostile to vendor promotion — by answering technical questions with no product mention for months. Organic recommendations followed from community members, not from Tailscale itself.

How would you even measure this?

Standard attribution can't capture how Reddit works. Most influence happens through passive consumption. Someone reads a comment, closes the tab, and books a demo three weeks later through branded search. The Refine Labs research on dark social documented a 90% gap between software-attributed and self-reported revenue. Reddit sits firmly in that gap.

There's also a built-in tension. Your instinct is to wire everything up: UTM codes, tracked URLs, attribution pixels. But Reddit punishes that. The community can smell a tracked link, and the more you optimize for measurement, the less your engagement looks human.

The most reliable approach is also the simplest. Add a free-text field (not a dropdown) to conversion forms asking how the person heard about you, and train reps to ask the same question on calls. "I saw your comment in r/revops about attribution" tells you more than any dashboard. Also use extended attribution windows. Reddit's influence typically materializes 60 to 90 days after first contact.

Is the window closing?

Most competitors haven't built a Reddit presence yet. The ones who are there often show up wrong, treating it like a broadcast channel. Reddit rewards real expertise and penalizes anything that feels like advertising. The bar for differentiation is still low.

The AI citation advantage, the search visibility, the buyer trust. It's all still available in most B2B categories. The cost gap between Reddit and LinkedIn won't hold forever, and the time to start building is before your competitors figure out the same thing.

So what next?

The harder part is knowing how to actually show up without getting ignored, downvoted, or banned. Which accounts to use, which subreddits to prioritize, how to write a comment that earns trust, and how to build a system your team can maintain. That's what we'll cover in Part 2.

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Your investment team spent three months evaluating a $20M software company. The financials looked solid. The market was there. The product worked. Everything checked out until you sat down with their sales and marketing leader and asked a simple question: "Show me how you actually acquire customers."

What they showed you was a spreadsheet. Not a system. Not a process. Just a sheet with projected pipeline numbers that looked like they came from thin air. When you dug deeper, you found what was actually driving deals: the founder. His network. His ability to get on the phone and close. The company had a scattered marketing motion, no recent thought leadership building brand, and no repeatable customer acquisition process.

You killed the deal.

This isn't an edge case. Most companies in growth mode operate this way. They start with founder-led sales, and fewer companies actually evolve beyond this stage. They grow through founder hustle, not systems. And when you're evaluating acquisition targets or trying to understand the quality of a portfolio company's growth engine, you need a framework to spot the difference between real growth and spreadsheet theater.

The good news: you can evaluate this in diligence. The better news: you can use the same framework to assess and improve any company you acquire.

What a modern growth engine looks like

A real growth engine has layers. Each layer is a system that works independently but connects to everything else. When all layers are operating, growth is predictable and repeatable. It doesn't depend on one person's rolodex.

Here's the simplified system to show how we connect all the pieces:

• GTM Foundation is the base. This is your positioning, messaging, and ideal customer profile. Does the company know who they actually sell to, and can they articulate why customers should buy from them. Messaging either stays consistent across the team or it doesn't. Positioning either gets written down or it lives in the founder's head.
  
• Content is what fuels awareness and demonstrates expertise. This is case studies, thought leadership pieces, nurture sequences, educational content. Content can be produced on a schedule or sporadically. It can align to the buyer journey or just be random top-of-funnel noise.
  
• Campaigns are how you put your strategy in motion. Campaigns can be structured around segments, buyer stages, or specific value props. Done well, campaigns have specific objectives, measurable outcomes, and keep your actions focused and intentional.
  
• Channels are where your audiences live, and serve as the pipelines that you activate to increase awareness and drive engagement. This is linkedIn, email marketing, reddit, paid ads, etc.
  
• Activate and Capture is how you convert the 5% of your audience that’s ready to act. This is a lead capture form on your landing page, lead magnets your audience bites on in direct social outreach, or your customer who replies to the monthly newsletter with a referral. 
  
• RevOps Underneath. Are systems connected or siloed? Do you know your metrics or are you guessing? Do you have a CRM that actually tracks contacts, deals, and engagement across the stack, or are you managing spreadsheets masquerading as systems? RevOps isn't about having the fanciest tools. It's about having a platform that connects your foundation to your measurement, so you can see what's working and what isn't.

Growth is messy, non-linear, and multidisciplinary. Frameworks and systems add clarity and structure that help control the chaos. At scale, companies have to have systems or they break. At early stage, the hope is that if you build the right system from the beginning, you avoid the death march of trying to retrofit it later.

Every company we speak with has a different level of maturity across the framework. The trick is knowing when the existing setup is a long-term liability or an immediate opportunity.

How Do You Actually Spot This in Diligence?

The real work of GTM diligence is asking the right questions about each layer and knowing when you're looking at real answers versus vague reassurance. Spreadsheets are easy to write down. Knowing when to call BS on what you're being told is the hard part.

Here are the five questions we use to structure the conversation:

1. What channels are actually working for growth? Not "what channels do you want to use." What's generating deals right now, and how much pipeline is each channel creating? 
2. How do you create content today? Who's responsible? Is there a schedule? Do people actually use it to sell, or does it sit on a shelf? Does it align to your buyer's journey, or is it random top-of-funnel noise?
3. Walk us through a recent marketing campaign. Who planned it? How did it get organized? What were you trying to achieve? What actually happened? You'll learn more about how a company actually operates from one campaign than from any document they send you.
4. For your active pipeline, what are the different lead sources? Break it down. If it's 90 percent founder referrals and 10 percent everything else, you know what you're inheriting.
5. What's the average deal cycle duration for pipeline in the last 12 months? Not a single deal. Look at the last 12 months. If it's chaotic, you're seeing founder-dependent selling. If it's predictable, you're seeing a system.

The key is listening to how they answer. Do they describe a process, or do they describe people? Do they reference metrics, or do they tell you what they think should happen?

Case Study: The Software Company We Walked Away From

Last year, our team spent two months evaluating a tech-enabled services company that had household name clients, a solid delivery team, and reasonably unique software that was under invested in recently. On paper, it looked solid. Mid-market B2B SaaS business, $20M ARR, good margins, established customer base. The investment thesis made sense.

So we dug into the growth engine.

GTM Foundation was weak. Positioning existed but wasn't consistent across the team. Their content engine was minimal. Campaigns weren't structured. Channels were a mystery. 

More importantly, the company had one person capable of selling. Not one person doing most things. One person the business actually depended on. He was the founder. He closed deals, maintained relationships, knew how the business worked. And he wanted out.

He was tired. He'd been running on fumes for three years, ready to hand it off and move on, and we knew this going in. But what the company underestimated was the degree to which their lack of any growth engine handicapped the deal.

We evaluated what it would take to fix this post-close. Build a GTM foundation. Create content. Structure campaigns. Enable a sales team to replace what one person was doing. 

We walked away.

Six months later, the business was acquired by a competitor. Founder checked out like he said he would. Customers started churning. The revenue that looked solid in the data room turned out to be fragile. Every month, the business eroded a little more. The acquirer is now managing decline instead of managing growth.

The issues we identified weren't unsolvable. They were all things we could have fixed with our operating engine. But the founder was unrealistic on valuation, and the deal numbers didn't support what it would cost to fix it. Not all deals work out.

What the acquirer didn't spend: two months of diligence with operators who knew what to look for.

What they're spending now: managing the fallout.

Putting It Together: What the GTM Layers Tell You

When you evaluate a target company, you'll find different combinations:

Strong across all layers. This company has a real growth engine. Post-close, you're scaling and optimizing, not building from scratch. Low risk. Lower post-close investment.

Weak foundation, strong execution. This one is tricky. The team executes well, but execution depends on people. Positioning and messaging aren't locked down. Red flag. When that person leaves, execution falls apart. Moderate risk. Plan for significant change management post-close.

Weak execution, strong foundation. You're buying a platform with solid messaging and positioning. Campaigns and channels aren't built out yet. But you have something to build on. Moderate risk. High post-close investment in building execution, but you're not starting from zero.

Weak across all layers. This is spreadsheet theater. Real growth doesn't exist. You're buying a founder and hoping his network scales. It won't. High risk. Either massive valuation adjustment or pass.

How We Approach GTM Diligence

Other firms skip GTM assessment in PE diligence. Not because it's unimportant. Usually because it's unclear how to evaluate it without being a marketing expert yourself. So it gets skipped, or it gets delegated to consultants who'll hand you a 50-page report that doesn't help you make a decision.

We approach it differently.

Trelliswork joins your deal team as GTM operators during diligence. We sit in management meetings with you and the target company. We help you understand what actually exists and what doesn't. We ask the specific questions that separate real growth from founder-dependent hustle. We help you build a roadmap for what needs to happen post-close if you move forward.

Here's what that engagement looks like:

Two management meetings. We join your team in the room with the target company's leadership. We sit alongside your deal team as an extension of your team, not as external consultants. We dig into their channels, campaigns, and metrics. We ask about process and tooling. We help you get a clear picture of what's actually built versus what's aspirational. You understand the gap between where they are and where they need to be.

A realistic post-deal roadmap. Then we work with you to build a specific GTM roadmap for this company, this market, these people. Not a generic template. A plan that's actually executable. What needs to happen in month one. What needs to happen in quarter one. What gets fixed versus what gets rebuilt. What you're inheriting versus what you're building.

You close the deal with clarity about what you're acquiring and what it actually costs to scale it.

The Cost of Missing This

You can discover this in diligence. You price accordingly. You go in with eyes open about what you're buying and what you'll need to invest post-close.

Or you miss it. You assume that because the company has grown, they have systems. You close the deal. You onboard the company into your portfolio. And somewhere around month three or month four, you realize the growth engine doesn't actually exist. Now you're managing a broken revenue machine while you should be scaling it.

The valuation adjustment you didn't make in diligence becomes the operational headache you own in year one.

Evaluate the layers. Ask the hard questions. Decide what you're actually buying.

That's how you spot the difference between real growth and a spreadsheet.

Ready to derisk your next deal?

Value creators and deal teams use Trelliswork to pressure-test growth engines before the close. We embed with your team during diligence, identify the gaps that don't show up in a data room, and build realistic post-close roadmaps so you know exactly what you're buying and what it costs to fix.

See how we work with investors or get in touch to talk about your next deal.