If you’re in B2B SaaS, you probably feel it already: the old way of “just hire more SDRs and send more emails” is broken.
Everyone has the same tooling. Everyone is running the same sequences. Everyone is “personalising at scale” with the same prompts. Yet pipeline quality is down, efficiency is under scrutiny, and suddenly… go-to-market (GTM) design has become a first-class strategic problem.
Few people are better positioned to talk about this shift than Harrison Rose.
Harrison co-founded Paddle, helped turn it into one of the UK’s fastest-growing software companies, and has now raised a $13M Series A (led by Notion Capital, with participation from Robin Capital, Inovia, Salicap, Common Magic, Andrena and more) to build GoodFit, an AI-driven GTM data platform.
Here’s what’s covered:
00:47 | What GoodFit actually does: mapping your entire market and scoring every account
01:32 | Paddle origins → the first-principles GTM problem that later became GoodFit
03:31 | From internal tool to standalone company recognizing the “product inside Paddle”
04:18 | Who buys GoodFit and why B2B tech is the first adopter (and why the market is much bigger)
06:28 | Second-time founder advantage: credibility, networks, and selling before the product exists
08:29 | Choosing investors: why Notion, avoiding echo chambers, and constructing a syndicate
13:24 | Bootstrapping for four years: optionality, profitability curiosity, and knowing when VC is the right path
18:34 | AI’s real impact on go-to-market: why most teams are just automating bad outreach
22:25 | The GoodFit vision: deciding who to sell to, why, and how (and leaving execution to others)
35:34 | Leaving Paddle: identity, founder evolution, and learning to lead differently the second time around
46:40 | Giving back: why Harrison opens his inbox for “weird, gnarly, unsaid” founder questions
What GoodFit Actually Is
Harrison’s description is refreshingly simple:
“GoodFit is a data platform for go-to-market teams.”
Concretely, GoodFit helps B2B companies:
Map their market
Build the real list of companies you could sell to – not just whoever happens to be in your CRM or on a scraped list.
Enrich that market with insight
Configure structured insight for every account so you can tell who is actually worth your time.
Prioritise & recommend GTM strategies
Start to answer not just who to go after, but why and how:
Which accounts should you spend real money and human effort on?
Which ones are better suited for more automated / programmatic playbooks?
GoodFit sits at the start of every GTM motion:
“Every go-to-market strategy starts with ‘who the hell am I selling to, why, and then how?’ We’re here to answer that.”
Today, GoodFit provides the data and intelligence layer and plugs into whatever execution tools a team uses – outbound, ads, ABM, events, etc. The vision is to become the “decision hub / mission control” for GTM: the system that continuously learns from performance and advises where to point your GTM machine next.
The Origin Story: Paddle’s Go-To-Market Engine
GoodFit was born out of the brutal practicalities of being a one-person commercial team at Paddle.
Paddle’s early days:
Two teenage founders, no prior work experience, no enterprise sales background
Huge theoretical market: any software company with a checkout
A very non-standard business model (Merchant of Record) that required real explanation
Harrison’s first-principles question was:
“I could sell to any of these software companies – so where do I start?”
The logic:
Map every possible software company they could sell to
Pull in as much structured data as possible on each
Use that to prioritise where to spend time, money, and headcount
Iterate and systematise into a GTM data engine
Over time that internal engine got:
More sophisticated
More automated
Clearly more valuable than “just a stack of spreadsheets”
At some point, it became obvious that this GTM engine was itself a product. That product is now GoodFit.
Who Uses GoodFit (And Who Shouldn’t)
Despite the GTM buzzword being very “SaaS-y”, Harrison pushes back on the idea that this is just for modern tech companies:
“Every company that sells anything needs to answer: who am I selling to, why, and how?”
But there are some practical boundaries.
GoodFit is a strong fit when:
Your TAM has >1,000 accounts
If you’re selling to 50 customers globally, you can probably brute force the strategy by hand.
Above 1,000, the complexity of segments, channels, and prioritisation explodes.
Your ACV isn’t tiny
If you’re selling $5/month subscriptions, the game is “acquire users as cheaply as possible”, not “decide the best combination of 4 acquisition motions by segment”.
If your ACV starts at $10k+ and can scale to $100k+, suddenly efficiency of GTM design matters a lot.
GoodFit works today with:
B2B tech / SaaS companies
Often VC-backed (though they’ve also just closed a six-figure deal with a 15-year-old, bootstrapped company)
Revenue teams that are past “just hustle and email anyone” and are now trying to turn GTM into a predictable, optimised machine
And interestingly, they also sell to:
PE funds: mapping software markets they might invest into
Other non-obvious segments who have the same core problem: who is in my market and how do I sequence my touchpoints intelligently?
How AI Is Really Changing Go-To-Market
Most teams are currently using AI in GTM in one of two ways:
Generate more emails
Generate more variants of the same landing pages / ads / sequences
Harrison’s view:
“Right now, most people are just using AI to automate a lot – not to actually do things better.”
And that’s the trap.
The real opportunity is in using AI to answer much harder questions:
For each account in your market:
What’s the expected value?
How much should you be willing to spend to win it?
Which channels are statistically most effective?
In what combination?
Over what time horizon?
“If I asked a human that on a single company today, it’s really hard. Ask them to do it for 50,000 accounts? Impossible.”
But with rich data + AI:
You can analyse your historical performance by segment, channel, ACV, sales cycle, etc.
You can learn which patterns consistently work for which types of accounts.
You can assign probabilistic expected value and optimal channel mix across your entire market.
This is where Harrison sees the next step-change:
AI-informed GTM design, not just AI-written copy
Programmatic go-to-market where humans do high-value work on top of a data-driven orchestration layer, instead of brute-forcing their way through generic lists
His critique of current practice is blunt:
“People are jumping straight to the execution layer and feeding their gen AI a bunch of really crap data. Then they automate more crap. No surprise it doesn’t work.”
GoodFit’s bet: the winners will be the ones who get the data + decision layer right, then plug that intelligently into execution tools.
Staying Out of the Execution Layer (For Now)
Harrison is quite intentional about where GoodFit sits in the stack:
They don’t want to be yet another sending tool (outbound/ads/calls).
They want to be the system that decides what should go into those tools and why.
And eventually, the one that reads back what happened, closes the loop, and makes the next decision better.
Why?
Execution is becoming commoditised
It’s increasingly easy to build tools that send emails, trigger calls, or launch ads.
It’s much harder to build defensible market intelligence and decision systems.
GTM strategy is a unique recipe
The way a company stands out is often how they orchestrate their GTM – which channels, in what order, with what intensity, to which segments.
GoodFit wants to support many different “recipes” and tools, not force everyone into one pattern.
But he’s realistic: if the path eventually leads to owning both the decision layer and parts of the execution layer, they’ll re-evaluate. For now, there’s a huge prize just in being the brain that tells all the arms and legs what to do.
Bootstrapping for 4 Years… Then Raising $13M
GoodFit’s path wasn’t the classic “napkin → pre-seed → seed → Series A”.
The sequence was:
Alex (ex-VP RevOps at Paddle) leaves Paddle and starts GoodFit, originally as a data service.
Within 12 months as a one-person team, he signs $500k ARR.
He also churns a lot of it. It’s COVID, he’s figuring things out alone, but the problem-market fit is undeniable.
They bootstrap for four years, selling what was essentially GTM data “like candy”.
When Harrison joins, he has two big questions:
“What outcome do we want?”
Stay a profitable, bootstrapped machine?
Raise debt and keep 100% ownership?
Or go full VC-backed and commit to building another huge outcome?
“Is this truly a venture-scale opportunity?”
Not every good business is a VC business.
As AI started to fundamentally change GTM, Harrison’s conviction grew that this was a category-defining opportunity, not just a good niche product.
At that point, the decision flipped:
“We realised go-to-market will probably never again see as much disruption as it’s seeing right now, in our lifetime. And we have a shot at changing how every B2B product in the world is bought and sold.”
And that demands venture capital.
They raised $13M Series A, led by Notion Capital – the same fund that led Paddle’s Series B.
Choosing Notion (Again) – And Building a Syndicate
Harrison is very clear that this wasn’t just a case of “go back to what’s familiar”.
Yes, he had:
Years of relationship with Notion
A shared obsession with GTM
Already been advising their portfolio on GTM before GoodFit existed
But he also looked at:
Do they genuinely understand and care about the problem?
Can he be fully authentic with them as a second-time founder?
Would they have told him not to raise, if they thought the opportunity was wrong?
“If you have an investor who genuinely thinks about what’s best for the business – not just their ownership – that’s a great sign.”
He then deliberately pulled in a broader group of solo GPs and funds – Common Magic, Andrena, Robin Capital, Inovia, Salicap and others.
Motivations:
Avoid an echo chamber with a single large investor he already knew well
Add complementary operator and GTM experience around the table
Get more diverse perspectives on GTM, AI, and category-building
Build what he calls “good governance” from day one, not just capital.
When Should You Raise VC At All?
Harrison’s advice to founders is straightforward:
“You need to be really, really clear on outcomes.”
If you’re unsure what kind of company you want to build, bootstrapping buys you optionality.
You can choose later between:
Staying profitable and founder-owned
Using debt
Bringing in institutional capital and chasing a massive outcome
But if you do raise VC:
You are effectively committing to build a very large company over a long period.
You need alignment on time horizon and outcome with your investors.
Angels and institutional funds do not optimise for the same outcomes.
He’s critical of how often this is glossed over:
“I see too many founders and investors not being mindful enough about this. You’re signing up to try and return a fund – that’s not a small promise.”
🇪🇺 Europe vs 🇺🇸 US – GTM Reality
Harrison has seen both sides. Paddle eventually expanded into the US, and 50% of GoodFit’s revenue already comes from the US.
His take:
Talent:
He rejects the idea that “Europeans can’t sell”.
Senior leadership GTM talent used to be thinner in Europe, but that gap has narrowed significantly.
Virality & density:
Yes, there’s a density effect in places like SF – everybody knows everybody, and local network effects are very real.
But GoodFit grew for four years without spending a dollar on marketing, driven by referrals and word-of-mouth from revenue leaders. You can build that in Europe.
The real difference: willingness to spend
Metrics, ACV, funnel economics – all look better in the US.
US companies are more willing to invest aggressively in sales & marketing.
European teams are often more cautious, which impacts growth speed.
He’s convinced Europe will continue to close this gap, but for now, it’s a real factor.
Founder Evolution: From 17-Year-Old to Second-Time CEO
One of the most candid parts of the conversation is Harrison reflecting on his own journey.
At Paddle:
He and his co-founder started the company at 17–18 years old.
They had no idea what culture was when asked at their Series A:
“We were like: what’s culture?”
They grew into being leaders of a 100+ person organisation with all the pressure that comes with it.
His view on founder longevity:
“If the company is growing 300% year-on-year, every single person, process, and system has to grow at the same rate – or the company slows down, or you get left behind.”
That includes founders.
Why he left Paddle:
It wasn’t because he was bored.
It wasn’t because he’d “topped out” skill-wise (the board wanted him to stay).
It was deeply personal:
“My entire identity, friendships, how I spent my time – it was all Paddle. I wanted to experience life not as Harrison at paddle.com.”
He wanted to know:
Who am I as an entrepreneur outside of this one company?
Can I build something meaningful again?
What does life look like when my job isn’t my whole identity?
Life & Leadership the Second Time Around
At Paddle, his mindset was extreme:
“It always felt like either we’d fail so dramatically I’d never work again, or we’d be so successful nothing else would matter.”
That drove a desperation to win, which he says is powerful but not entirely healthy.
At GoodFit:
He’s married.
He’s planning a family.
He can’t be first in and last out every day.
He doesn’t have the luxury (or need) to work in sheer brute-force mode.
The leadership challenge this time:
“The thing I’ve always prided myself on is intensity and pace. I want people to feel it when I walk into a room. If I can’t be in the room all day, every day… how do I do that?”
He’s actively looking to learn from founders who’ve built great companies and integrated family and life – something he simply didn’t have to think about when he was 18.
Closing Thoughts
GoodFit sits at the intersection of:
Deep GTM operational experience
A real, tested internal engine from a previous unicorn journey
And a moment in time where AI + data can genuinely rewire how B2B companies decide who to sell to, how, and why.
Harrison’s story is a good reminder that:
Great second-time companies often come from deep personal pain in a previous journey.
Bootstrapping vs VC isn’t a moral choice – it’s about being honest about outcomes.
AI in GTM is not about “writing more emails” – it’s about designing better systems.
If you’re a founder, GTM leader, or investor thinking about:
Using AI in commercial strategy
Whether to stay bootstrapped or go VC-backed
Or how to build a more intelligent GTM engine
…this is one of those episodes worth saving and revisiting.
