Follow-up · Oct 16 callExecutive networks, mapped

The people you want to hire are one introduction away.

Paddle came to us for a way into the networks around its ExCo and board of investors. The hard part is knowing who actually knows whom, using public data only. Since the call we mapped 99 CPTOs at fintech and SaaS companies across your European hubs, and found 32 with a career path through someone at Paddle today. Below: five plays your team could run in Dust, plus three for the revenue side.

Every number is from a live Crustdata pull · Oct 19, 2026
99
CPTOs at fintech, SaaS and internet companies in the UK, Netherlands, Portugal and Ireland
32
of them overlapped at a past employer with someone at Paddle today
3,134
VP and C-level leaders at companies your VC investors backed, in your hub countries
662
Paddle alumni in our index, 132 of them now Director, VP or C-level
What I heard

Get into the networks around your ExCo and investors, and find the people inside them.

Looking for introductory networks and connections to our ExCo and board of investors.
Market mapping, availability of talent, maybe aligned to our hubs.
Sometimes we want to engineer the search the other way around. We want to go company led.
How do we make sure that we're using this to the best of its ability that differs from ChatGPT?
What we're very reluctant to do is just sign up to another tool and then not have it used.
What it has to do

Four things the setup has to get right for Paddle.

A team of five running senior searches every week, working in Dust. Here's what has to be true for that to stick.

01

Warm paths with a reason and a strength

Public data means connections are inferred, not confirmed. So every path has to show why it's likely (same team, same years, same company size) and how strong it is. Your own connection exports turn the best ones into first degree.

02

Built around your hubs

London, Lisbon, Dublin, the Netherlands, Toronto and the US. A market map that starts from where Paddle can actually hire, not a global list you trim by hand.

03

Company-led, not keyword-led

Start from the company and the moment: who was there through an IPO, a growth phase, a profitability push. Then filter by tenure and seat. Most sourcing tools can't run the search in that direction.

04

Inside Dust, for non-coders

No code and no new tool to log into. Crustdata connects to Dust as an MCP server, your agents ask in plain English, and results come out as a file you own.

From the demo

Your CPTO search, run live. Then we ran it properly.

On the call, Jai asked an agent for CPTO profiles in fintech and the three with the warmest paths to Paddle. After the call we ran the same search across every Paddle hub country, then scored paths through your own team.

What went in

The search, in plain words

  • SeatChief Product and Technology Officer, plus CTO and CPO as the next seats over
  • EmployerFintech, SaaS and internet companies with 100 to 5,000 people, close to Paddle's own shape
  • WhereThe countries Paddle hires in: UK, Netherlands, Portugal, Ireland, Canada, US
  • PathsCareer overlap with the 350 people at Paddle today, ExCo weighted up
What came back · Oct 19, 2026

Product and tech chiefs, by Paddle hub country

CountryCPTOCTOCPO
United Kingdom72787305
Netherlands1717441
Portugal65517
Ireland46524
Canada926470
United States2453,7301,223

Highlighted rows are the 99 European CPTOs we scored for warm paths. Profiles in our index, not headcount.

32

After the call · warm paths, scored
Of the 99 European CPTOs, 32 overlapped at a past employer with someone at Paddle today. Two are strong (years together at a mid-size company), seven are medium, 23 are light. One of the medium paths runs through your ExCo: the CPTO of DataSnipper in Amsterdam spent a year at Optimizely alongside Paddle's Chief Innovation Officer.

What we'd build

Five things we'd build for Paddle's talent team.

Ordered by how fast Ferzana and Owen could run them. Play 01 is the search from the demo. We show companies and titles here; names and profiles come back in the pull.

01

A live market map for every senior search, by hub.

Run it this weekMarket mapping skill

Ask for a seat, an employer type and a hub, and get the whole pool back, not the handful a web search surfaces. The UK alone has 72 CPTOs, 787 CTOs and 305 CPOs at fintech, SaaS and internet companies of Paddle's size. The map breaks the pool down by city, tenure, years of experience, past employers and company stage, so you can see where the talent sits before anyone picks up the phone.

How it runs
  1. Your Dust agent turns the brief into filters on seat, employer industry, company size and country, and runs person_search for a full count.
  2. The market mapping skill groups the pool by hub, employer, tenure in seat and years of experience, and flags who's been in seat long enough to move.
  3. The pool comes out as a sheet with names, profile links and a reason per person, ready for Owen to work.

Availability of talent, by hub, in one ask. Portugal has 6 sitting CPTOs and 55 CTOs at companies like this; the UK has more than ten times that. That changes where you'd open a senior role.

→ Dust · sheetPer searchA new senior req or a board ask
Where Europe's CPTOs sit · a sample of the 99Oct 19, 2026
CompanyHub countryProfiles
DojoUnited Kingdom1,890
EburyUnited Kingdom2,054
PleoNetherlands933
OakNorthUnited Kingdom685
ComplyAdvantageUnited Kingdom507
PipedrivePortugal1,091
BooksyPortugal1,234
DataSnipperNetherlands284
LatticeIreland443
YapilyUnited Kingdom143
A sample of the companies in the pool. Profiles is the company's size in our index.
02

Warm paths through your ExCo, your team and your investors.

The ask that started thisInferred, then scored

We can't see who's connected to whom, and nobody can from public data alone. What we can see is every career history: who sat at the same company, in the same years, at a size where people actually meet. Across the 99 European CPTOs, 32 overlapped with someone at Paddle today. Paddle's VC investors (FTV, Notion, Kindred, 83North) have also backed 220 companies with 50+ people, with 3,134 VP and C-level leaders in your hub countries, including the CTOs of GoCardless and Currencycloud. Those are doors your board can open.

How it runs
  1. Pull the career history of everyone at Paddle with person_search on Paddle's company id, plus the companies your investors backed. Add your ExCo's own connection exports for exact first degree.
  2. For each target, find overlaps: same employer and overlapping dates, same school and years. Score each path on months together, how small the company was, same function, and ExCo weight.
  3. Return a ranked list: target, path, the reason in one line, and who at Paddle to ask. Connection exports stay yours and never go into our index.

The intro request writes itself. "You and our CPTO target were at Dojo together for four years" is a message an analytics manager can send today. A big-company overlap (both at IBM) scores light, on purpose.

→ Dust · ranked path listPer target listA shortlist is ready
Top paths to European CPTOs · of the 32Oct 19, 2026
TargetThrough someone at PaddleTogetherScore
CPTO · DojoAnalytics eng manager · both at Dojo47 moStrong
CPTO · LumonSenior software engineer · WorldFirst28 moStrong
CPTO · DataSnipperChief Innovation Officer · Optimizely12 moExCo
CPTO · CloudTalkHead of web · Mews15 moMedium
CPTO · IdeagenDirector of product design · Verizon Connect23 moMedium
CPTO · PleoSenior PM, payments · Adyen22 moMedium
CPTO · LatticeChief Revenue Officer · Shopify28 moLight
Inferred from career history: a likely acquaintance, not a confirmed one. Lattice scores light because Shopify is large. Paddle titles shown, names withheld.
03

Company-led search: the leaders who were there through the IPO.

Company-ledRun it this week

Pick the company and the moment, then find who was in the room. We searched for people who held a Director, VP or C-level seat at Wise, Adyen or Klarna for the full year before listing day, have since left, and live in the UK, Netherlands, Portugal or Ireland: 83 people (Wise 34, Adyen 41, Klarna 8). The same query works for any event with a date: a hiring spurt in the headcount history, a funding round, an acquisition, a carve-out.

How it runs
  1. Get the event date: IPO and funding dates, or the months a company's headcount grew fastest, from company_enrich.
  2. person_search on employment entries at that company that start before the window and end after it, at the seniority you want.
  3. Keep the ones who've moved on and sit in your hubs, then rank them on the brief with a reason each.

Proven at the stage Paddle is heading into. These are people who've already scaled a payments business through public-market scrutiny, found by the company, not by a keyword on their profile.

→ Dust · sheetPer searchAn event you choose
There through the IPO, since moved on · a sample of the 83Oct 19, 2026
ThenNowHub
Product Director, Wise BusinessCPTO · MarTrustUK
Director of Engineering, Cards (Wise)Head of Engineering · Wand AIUK
SVP Business Development (Adyen)Chief Revenue Officer · TrustlyUK
Head of Plugins & Partners (Adyen)VP Engineering Integrations · YunoNL
GM Spain & Portugal (Klarna)VP Western Europe · SolidgatePT
Head of Commercial Finance (Wise)Group CFO · FinomUK
Seniority is the title they held at the time. Some current employers may be Paddle customers or partners.
04

A shortlist on Paddle's logic, for any senior role.

Exec and senior sourcingSourcing skill

Take a subscriptions engineering leader as the example. We found 321 engineering managers and heads of engineering in the UK and Ireland who've built at Monzo, Checkout.com, Stripe, GoCardless, Wise or Revolut, and 102 of them have since moved on to companies like MoonPay, Tide and Definely. The sourcing skill takes it from there: your intake notes become criteria, you calibrate on five profiles, and every name comes back scored with a reason.

How it runs
  1. Drop in the kickoff notes or the brief. The skill drafts must-haves and nice-to-haves and asks you to confirm.
  2. person_search on title, past employers and location, then a full profile for the ones that pass.
  3. Claude scores each person against your rubric with a one-line reason. Contact details only for the shortlist, through person_contact_enrich.

The first 15 profiles are easy in any tool. The value is profiles 16 to 200, ranked on your criteria with the reasoning shown, so Owen doesn't have to read the haystack.

→ Dust · ranked sheetPer reqA role opens
Where the 321 built payments softwareOct 19, 2026
Monzo128
Checkout.com62
Stripe41
GoCardless39
Wise35
Revolut19
Some people built at more than one of these companies.
05

The people who already speak merchant of record.

Only Paddle can run this one

Merchant of record is a small world, and Paddle sits at the center of it. 185 senior people in your hub countries describe merchant-of-record work in their own profiles, and 176 of them are outside Paddle, with the biggest groups at FastSpring, Breeze and Cleverbridge. Then there's your own bench: 662 Paddle alumni in our index, 132 now Director, VP or C-level. They're the warmest referrers you have, and the first place to look for a boomerang hire.

How it runs
  1. person_search on role descriptions and headlines for the exact phrase "merchant of record", by seniority and country.
  2. A second search on past employer = Paddle, grouped by where alumni are now and how senior they've become.
  3. Put both lists on a watch, so a job change on either one shows up in Slack or email the week it happens.

No one else can use your alumni as a talent network. And no generic sourcing filter knows to look for merchant of record.

→ Slack or email digestWeeklyJob change on either list
185

senior profiles mention merchant of record, across your hub countries

132

Paddle alumni now Director, VP or C-level, of 662 in our index

Where merchant-of-record talent works nowOct 19, 2026
FastSpring8
Breeze6
Cleverbridge4
Reach3
Stripe2
Verifone2
The rest are spread one or two per company. Eight of the 185 are Paddle alumni.
Why not just ask ChatGPT

Web search finds the famous ones. Structured data finds all of them.

A web search ranks people by how visible they are, then stops after a few. Crustdata's index ranks nobody: every filter you add narrows a complete pool.

That's why the same Dust agent gets better with Crustdata behind it. Ask for CPTOs in fintech and it can count all of them, then keep narrowing by company size, country and career overlap with your team without the results thinning out into guesses. The quiet CPTO with no conference talks and a modest following is in the pool the same as the famous one.

  • The source1B+ people and 60M+ companies, real-time, with employment dates you can filter on
  • The logicYours: company-led, event windows, tenure, ExCo-weighted paths
  • The outputA file you own, with a reason on every row

Counts from live pulls on Oct 19, 2026. The top row is illustrative, not a measured pull.

Where it plugs in

Your stack stays. Crustdata sits underneath it.

No new tool to log into. Crustdata is the data layer your Dust agents call, and everything comes back as a file you own.

Already yours

Dust

Where the team works. A Dust admin adds Crustdata as a remote MCP server under Spaces, then Tools, with an OAuth sign-in or a token. Your agents call it from there.

New

Crustdata

People and company data your agents can filter on: career dates, seats, employers, events. Skills for market maps, sourcing and warm paths on top.

Already yours

Sheets and your exports

Results go out as a sheet or a file. Connection exports from your ExCo come in for exact first degree, and stay yours.

The test

Try it on real searches before anyone commits.

A hands-on test week in Dust, set up together on a short call so nothing sits unused. Score the outputs, not the pitch.

Ask 01Coverage

Run the same CPTO prompt in ChatGPT and in Dust with Crustdata

Count how many real, current CPTOs each returns for your four European hub countries.

Our count today: 99
Ask 02Warm paths

Check our paths against the people who'd know

Show the strong and medium paths to the colleagues named in them. How many do they recognize? Then add one ExCo connection export and see what moves to first degree.

Strong and medium today: 9
Ask 03Company-led

Pick an event and a company you care about

An IPO, an acquisition, a growth year. Check whether the people who come back were really there for that window.

Wise, Adyen and Klarna IPOs: 83
Ask 04A real role

Shortlist a senior role you're hiring for now

Put our top 20 next to what your current tools give you for the same brief. Grade every name as yes, maybe or no.

Example pool, subscriptions engineering leaders: 321

A fair result for Paddle: the team runs real searches in the test week, the warm paths hold up when your own people check them, and at least one search finds someone your current tools missed. If that's not what happens, you'll know before signing anything.

Straight answers

The questions from the call.

Q1Is it only public data, so the connections are inferred?

Yes. Crustdata is a primary indexer of public data, and we don't log into anyone's accounts. Connections are inferred from career history (same employer and years, same team, same school and years) and from public engagement, then scored by how likely they are.

For exact first degree, your team exports its own connections as a CSV and we enrich and match them. That's the route for the ExCo network specifically.

Q2If we upload our connection exports, does that feed your database for other customers?

No. What you send is used to answer your request, and we don't add it back into our index or share it. The data you get back is yours to use however you like.

Q3Does it work inside Dust?

Yes. Crustdata runs as an MCP server, and Dust supports remote MCP servers: a Dust admin adds it under Spaces, then Tools, then Add MCP Server, and signs in. No code. Doug's email confirms the exact steps for your workspace, and Jai can walk your admin through it on the setup call.

Q4How is this different from asking ChatGPT or our Dust agent to search the web?

Web search ranks results by popularity and agents rarely read past the first few. Crustdata is structured: titles, employers, dates, locations and company facts you can filter on together, against a complete pool. The more filters you add, the more that difference shows. See "Why not just ask ChatGPT" above for the CPTO numbers.

Q5Can we get the results out of the chat?

Yes. Ask the agent to put everything in a file or a sheet. Every row keeps its reason and the profile link.

Q6Do you help us work out which prompts and searches get the most out of it?

Yes. Onboarding and best practices come with it, and we build custom skills for the workflows you repeat (the market map, the sourcing shortlist, the warm-path score), so the agent follows the same steps every time.

Beyond talent

The same data, pointed at Paddle's revenue team.

The team asked where else this fits. Paddle sells to 10,000+ digital product companies through self-serve signups and a sales team, so three plays stand out.

ABM · timed to the AI Toolkit
1,892
software companies founded since 2023 that raised since April

AI app builders who just raised and are about to sell globally

Paddle launches its AI Toolkit on Oct 20: "From prompt to global selling." These are the companies that need it next, found by founding year, a recent round, and what they sell.

In the sample: Emergent, Sekai, Raindrop, Siena AI, Rightbrain AI.

company_search · semantic mode · basic_info.year_founded · funding.last_fundraise_date
PLG · self-serve signups
+24%
two-year growth on paddle.com's own record, from one call

Enrich every signup, route the big ones to sales

A signup's domain comes back with company size, growth, funding and team in one call. Run on paddle.com itself, it returns 422 profiles, +24% over two years and a July 2025 raise. Big or fast-growing signups go to a BDR; the rest stay self-serve.

company_enrich · headcount.growth_percent · funding · webhook into your routing
Expansion · champion moves
573
finance and RevOps leaders who started at software companies since July

Know when a buyer at a customer changes jobs

When a finance or RevOps lead who already runs on Paddle moves to a new software company, that's a warm account. A watch on your customers' buyers flags the move the week it happens.

Count is before dropping interim and fractional seats.

watch_create · experience.employment_details.current.start_date
What happens next

Let's get it into Dust and let the team try it.

Everything below came out of the call. No dates we didn't agree to.

The short version

Your ExCo, your investors and your team already know the people you want to hire.

Now you can see who knows whom, rank it, and ask for the intro, from inside Dust.