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Investor Overview · 2026

Job search was built
for the unemployed.
We built it for everyone else.

70% of employed professionals would take a better job. Almost none of them are looking. Not for lack of wanting. For lack of time.

The Problem

Job search assumes you have
nothing else to do.

The process eats time you don’t have. Searching across sites, researching every company, rewriting your resume for every role. After a full day of work, it's never worth the hours.

So the best talent stays put, quietly unhappy, until desperation finally runs the search.

The Insight

Who each tool in this category
was actually built for.

The person who already has a job and wants a better one has not been the customer for any of them. That is the gap we build for.

LinkedIn
A networking platform. Jobs were bolted on to sell recruiter seats.
Job Boards & ATS
Built to sell postings and filter applicants out.
Seeker Tools
Auto-apply, resume tools, curated boards. Built for the active seeker racing to end a search. They match you to postings, not companies.
Passive
Built for the person deciding whether to move. We match you to the company, not just the job.
The Moat

LinkedIn owns identity.
Passive owns the decision layer.

The one company with the distribution to build this has a revenue conflict that prevents it. The mechanics are below.

The revenue trap
Recruiting products are ~60% of LinkedIn's revenue. A candidate-side product that hides candidates cannibalizes the product that funds the company.
The visibility trap
Their paying customers buy visibility into candidates. Our entire value is making you invisible while you decide. They can't ship it without unbuilding themselves.
The arithmetic
Twenty years, over a billion users, and the candidate's decision layer is still empty. That's not an oversight. It's arithmetic.
The Solution

The daily card stack.

Each day, a fresh stack of roles matched to you. Apply, Save, or Pass, one card at a time. Every choice sharpens what comes next. Five minutes a day. No scrolling.

You don't search. You discover.

Company Research

Know a company
before you apply.

Every employer says it's a great place to work. Company Pulse pulls the real picture onto the card: what employees say, how it pays, where it's headed. You decide if a role is worth your time without leaving the app.

Nobody pays us to soften the answer.

Resume Tailoring

Your resume, rewritten
for every role.

The Tailoring Engine rewrites your resume for any role in seconds. It never invents experience. It makes your real history speak the company's language.

The most time-consuming part of applying, gone.

Traction · Before the beta

Where demand stood
before the beta.

Acquisition
19K+
on the waitlist
$0.53
cost per waitlist signup
Intent
10×
oversubscribed beta
15 min
to fill from the waitlist
Signed ATS partnership Greenhouse. Applications land natively in employer workflows.
Traction · Cohort 1

The targets we set,
and what happened.

Targets were written before the cohort opened. Cohort 1 was 37 users invited from the waitlist, running on the pre-card-stack build.

Retention
73% 62% 38% D1 D7 D30
Targets were 35% at day 1 and 20% at day 7. Consumer benchmark for day 7 sits at 20–25%.
Engagement, against target
Weekly active usersTARGET 25–30% OF USERS
52%
Resume tailoring in flowTARGET 30–40% OF USERS
45.6%
Session lengthTARGET 3–5 MIN
5–7 min
What we did next Rather than open the doors, we rebuilt browse into the card stack and normalised both sides of the match on our own taxonomy. Supply grew to 3,000+ companies and +350% job volume, and relevance held.
Traction · Voices

Beta, in their own words.

It felt like it started with me. In other solutions you filter and search and it’s agnostic to you. Here, it started from me.

Sushma A. · Director, Product Operations

My Passive profile? Man, you wrote a better bio about me than I could write about myself.

Barbara G. · Administrative Assistant

I do really like that you can have AI tailor your resume. That saves me a lot of time… rewriting my resume every single time.

Hannah C. · Executive Assistant

I just started using Passive and it’s so much more fun than LinkedIn! It looks clean and feels super intuitive!

Jess S. · Product Manager

The concept and the approach… make it much easier for job seekers. Definitely for somebody who is working full-time and just wants to see what’s out there and see if something bites.

Arnold S. · Finance Executive

That Passive could be that always-on agent… ‘Yep, this one’s a good fit. Here’s the next step.’

Andrew V. · Senior VP, Product Management
The Market

The market,
and how we sized it.

27M US
TAM
101.9M knowledge workers
English-speaking and Western European digital-first markets. $23.3B annualised ceiling at $19 a month.
SAM
43.4M serviceable
English-language product, functions our matching covers. $1.1B real revenue pool: 13M in market each year at $85.50 per job change.
Beachhead
27M in the US
Where we start. $693M pool.
What we take, and when
Year 322.5K subscribers · $5.1M ARR · 0.08% of the beachhead
Mature US
Year 7–10540K subscribers · $123M ARR · 2% of the beachhead

Two percent of the beachhead would be a $123M business.

Rings drawn to true area. Sources · BLS · US Census · LinkedIn Premium conversion benchmark
Business Model

How we make money.

Free to discover. We earn the upgrade by being useful every week, not once every few years.

Now Consumer · B2C
Free
  • Discover and apply to roles
  • No deep company insights
  • No premium resume tailoring
Premium$19/mo
  • Everything in Free
  • Deep company insights
  • AI resume tailoring for every role
Next Interview Intelligence tier
$49/moInterview Intelligence for the high-stakes moments. Working today, delivered white-glove, priced against outcomes that matter.
Team

Two founders who have
each built this before.

One founder knows what gets people hired. The other builds systems that learn what people want. That's the product.

Michael Wenning
Co-Founder & CEO
15 years in recruiting and staffing. Founded Hire Metrics and Wenn AI. Based in Zagreb, Croatia.
Josh Jordan
Co-Founder & CTO
Built personalization for Amazon Alexa. Owns the engine that makes the card-stack feel personal.
The Raise

We know what a user costs.
This round proves what one is worth.

Twelve months: 10,000 active users, and the first cohort that pays.

Now raising · terms shared in conversation

Product · 50%
Improve match relevance, sharpen the taxonomy engine, and advance our machine learning.
Growth · 25%
Scale acquisition while CAC stays under a dollar.
Retention · 25%
Convert beta love into durable weekly active retention, and prove the first paid cohort.
The Vision

From beta to Career OS.

Not a someday story. Interview prep, the first module beyond discovery, works today, delivered white-glove while we build it into the app. A beta user who landed an offer with it called it "a real game changer."

Roadmap
Year 1
Match quality → PMF.
Year 2
Premium scale. Negotiation Prep. Leadership-tier traction.
Year 3
Career co-pilot. Manager today, director next. Cradle to career-peak.
The category's exit history
LinkedIn → Microsoft$26.2B · 2016
ZipRecruiter → IPO$2.4B · 2021
Indeed → Recruit Holdings~$1B+ · 2012

Comparable outcomes in this category have been measured in billions. The dataset compounds with every interaction.

Michael Wenning · Co-Founder & CEO  ·  Let's build the candidate's side of work.
Appendix · Why Now

Job boards are 75 years old.
So why hasn't this been built?

Because every dollar in recruiting flows from employers. The experience was never built for the candidate, and until now it couldn't be.

Since the 1950s
Job boards arrived, then the internet simply digitized the paperwork. Nobody reimagined the experience.
The tools that tried
Built for the active seeker who needed the search to end. Serving the employed majority never made economic sense.
Now
The economics flipped. Company intelligence that cost seven figures to license is now a query measured in cents. Reasoning that meets a senior professional's bar costs pennies per output. The bias is seventy-five years old. It became solvable eighteen months ago.
Appendix · The Acquisition Insight

$0.53 isn't a stat.
It's a cost structure.

Six months. $10,000. 18,996 signups. The cost was paid up front; the channel kept delivering.

Cost per signup $0.53 Three LinkedIn job postings, $10,000 total, six months live.
3LinkedIn postings
·
$10KFixed, up front
·
6 moLive
=
18,996Signups accrued
Fixed cost paid up front. Signups accrued the full duration.

The pool refreshes constantly. Functionally unbounded inflow.

Americans actively job-searching at any moment ~11M BLS data shows the active-search pool turning over roughly twice a year, as searches average six months.

The channel doesn't saturate because the audience never sits still. As candidates land roles and exit the pool, new candidates enter at a rate faster than any single posting can consume.

Competitors pay CPM/CPC, recurring marginal costs. We pay a fixed listing fee that amortizes across an unbounded pool.

Mobile CPI (NA)$5.28 average. Per-install marginal cost.
LinkedIn Ads$15–50+ per qualified click.
HR tech$50–150+ per acquisition.
Passive, per signup$0.53. 10× more efficient.
Cost modelFixed listing fee. No CPM. No CPC.
AmortizationSpreads across the full 6-month inflow.
The cost structure inverts the category.

Re-fire the channel. Stress-test what's underneath.

  1. Re-fire, instrumented. Run the proven channel at controlled spend, $20K, $50K, $100K, with monthly cost-curve granularity. Know what it does at scale, don't infer it.
  2. Stress-test activation and retention. The channel works. The funnel needs to convert at scale before we spend on the megaphone.
  3. Lock the unit economics. A clean LTV:CAC with monetized cohorts, so we walk into the seed conversation with the math already proven.
The proof just finished. The six-month cycle completed May 3. Re-firing the channel is part of what this capital pays for.
Appendix · LinkedIn Counter-Move

This isn't a gap they haven't filled.
It's a structural impossibility.

Talent Solutions is ~60% of LinkedIn's revenue. It cannibalizes any candidate-side product.

Share of LinkedIn revenue ~60% Talent Solutions, recruiting tools for employers. LinkedIn's largest single revenue line.
  1. LinkedIn was built in 2003 as a networking and identity platform. Job postings came later as a revenue stream, never the design intent.
  2. Their paying customers are recruiters who pay for visibility into candidate profiles. The business model requires candidates to be visible.
  3. Making candidates invisible, Passive's entire value proposition, directly cannibalizes the product that funds everything else.
This isn't a strategic choice. It's arithmetic.
LinkedIn is the surface where your boss is watching.

LinkedIn's whole product is visibility, to colleagues, networks, recruiters. The moment you signal "open to opportunities," the people you're trying to leave can see it.

"Open to Work" exists. Most professionals refuse to use it. Many set visibility to "Recruiters only," afraid their manager catches them looking.

LinkedIn can't fix this without unbuilding itself. Private job-seeking breaks their engagement model. Anonymous browsing breaks their ad and recruiter products. The product they'd need to ship is the product Passive is.

LinkedIn solved "help recruiters find you." Passive solves "let you look without being seen." Different problem. Different category.

LinkedIn captures declared identity. Passive captures behavioral intent.

TypeDeclared identity.
CapturesTitle, tenure, connections, endorsements.
StateStatic. Updates when the user does.
PredictsWho you say you are.
TypeBehavioral intent.
CapturesEvery role browsed, company researched, resume tailored, decision made.
StateLive. Refined per interaction.
PredictsCareer moves before they happen.
Building this would mean redesigning how 1.3 billion users engage with the platform.

The category exits via acquisition. We're building the dataset they'll eventually want to own.

2012 · ~$1B+
Indeed → Recruit Holdings
Aggregator acquired by a global staffing giant after proving consumer-side demand.
2016 · $26.2B
LinkedIn → Microsoft
Identity platform acquired for the dataset and the distribution moat.
2021 · $2.4B
ZipRecruiter → IPO
Public exit on the strength of consumer-side recurring revenue.
We're not competing with LinkedIn. We're building the dataset LinkedIn will eventually want to own.
Appendix · Habit & Retention

Spotify, not Indeed.
Turn the volume down between chapters.

Job boards have a structural retention problem. Users leave the moment they succeed.

TriggerUnhappy. Job search begins. Late and emotional.
EngagementActive search. Transactional. End-state.
On successUser leaves. Account dies. Indeed.
TriggerCuriosity. Five minutes a day, between chapters.
EngagementAmbient. Always-on, low-effort. Stays in the pocket.
On successVolume down, not off. Spotify.
People don't churn when they accept a role. They turn the volume down until the next chapter.

The concrete mechanic. Not just the analogy.

  1. Set criteria once. Dream-role parameters captured at onboarding: title, level, comp, geography, company stage.
  2. Curated roles surface when relevant. Quiet when they're not. The product earns its place on the home screen by never demanding attention.
  3. Five minutes a day. No work. No spam. The volume knob, not the on-off switch.
A career is a 30-year asset, not a six-month transaction. We built the product for the asset.

Every new product surface extends the customer lifetime.

LTV : effective CAC 10:1 $0.53 per signup ÷ 10% free-to-premium conversion (modeled, LinkedIn Premium benchmark) = $5.30 effective CAC against $57 modeled LTV. A model, not a measurement. This round instruments it.
Surface 1Today
Discovery + Research + Apply
The daily habit. Five minutes a day, ambient retention.
Surface 2Next
Interview Intelligence + Negotiation Prep
High-stakes moments. Premium pricing tied to outcomes that matter.
Surface 3Later
Skills Mapping + Career Trajectory
Manager-to-director path. The career operating system layer.
D7 retention sits at 62% in beta, well above the 20–25% consumer benchmark. The retention thesis is already proving at our cohort size.
Appendix · Interview Intelligence

The next Career OS module
already works.

Beyond discovery, we've built AI-guided interview prep, delivered white-glove today while we productize it. Early users walk in more prepared, and they're landing offers.

"I accepted a job offer this week. The interview prep document was gold, super helpful and a real game changer. It gave me a lot of confidence going into the final round."

Beta user · Discord · 28 April 2026

"I've completed 3 interviews so far and feel more confident than I ever have."

Beta user

"I felt the most prepared and knowledgeable for an interview as I ever had."

Beta user
Sr. Software Engineering Manager

"F***, I FINALLY FEEL LIKE I CAN DO THIS!"

Beta user
Cohort 1

"I would not have had a successful one-hour interview without the answers and phrasing on that guide."

Beta user
Sr. HR Business Partner