Amanda Zhu

Co-Founder

❗Your engineers are wasting 6+ months building meeting integrations.❗

Why? Because every platform (Zoom, Google Meet, MS Teams) has different APIs, constant updates, and hidden edge cases.

❌ Building in-house = wasted engineering time.
❌ Maintaining it = ongoing distractions from core product development.
❌ Scaling it = reliability nightmares that break at the worst time.

That’s why we built Recall.ai.

✅ One API to handle every meeting integration for you.
✅ Ship in days, not months.
✅ Powering 1200+ companies, from early-stage startups to billion-dollar SaaS teams.

🚀 See how Recall.ai can save your engineers months of work

✔︎ Who gets results using Recall
∙ Product teams at fast-growing startups who want to go to market in days instead of months. 
∙ Engineering teams that don't want to burn engineering hours maintaining their meeting bot integrations.

✔︎ Here’s what we do
We provide one API for meeting bots on every video conferencing platform. We abstract over differences between meeting platforms (even for platforms that don’t have an APIs). We run, monitor, and scale the complex real-time video infrastructure.

Interested in learning more? Go to: https://recall.ai 🌐

Recruiters offer our sales reps $400K roles. None have left.


Our reps are having a career year.

Our AE Liz closed $3M.

Every rep is over 100% quota.
And they do it while helping each other win.

In June, we launched self-serve.
And changed our pricing structure.

But our sales team didn't complain.
They leaned in.

John closed $200K+ in one quarter after our product launch.
Sam stepped up to close HubSpot when our VP of Sales got stranded in Nebraska.
Paul closed two deals while vacationing in Italy.
Marcus booked 20% of our outbound meetings.
Steve brought in 230+ new customers.

As a founder, there are a lot of moments that make the job hard.
Getting to work with a sales team like this isn't one of them.

The API for meeting recording | Co-founder at Recall.ai

I landed six $1M deals last year. Here’s when/how I rescue 7 figure deals by flying to prospects: 

I fly out when a deal hits a roadblock,
and I don’t know why.

This is the playbook:

1/ Make it easy to say yes

I never ask:
“Can I fly out to meet you?” 

Instead, I’m specific: 
“I’m going to be in Atlanta on Friday from 2-7pm. 
Could we connect for dinner?”

Why this works:
- it signals effort without desperation
- a tight time window is easier to say yes to
- it reduces coordination work for a busy exec

-----

2/  Fly out to uncover, not to pitch

In one deal, I learned that a decision maker was about to say no. 

That surprised me.
On Zoom, she never raised an objection.

I flew out to meet her,
and that’s when she told me the truth: 
she was more committed to her internal solution than I realized.

That information let me walk through:
- the edge cases she hadn’t considered
- the ceiling she’d hit
- the tradeoffs she was about to own

Why this works:
- you’re meeting them on their turf
- you have the exec’s full attention
- they don’t need to perform in front of their team

-----

3/ Be picky about where you meet

The setting determines the conversation you get. 

If I need to meet with multiple stakeholders,
I meet at their office.

It’s easier to loop people in
and align everyone at once.

If I need one exec’s honest take,
I meet outside HQ.

Why this works:
- offices optimize for alignment
- out-of-office settings optimize for candor
- the room shapes what people feel safe saying

-----

Flying out isn’t just about pushing the deal forward.
It’s about finding out what’s actually blocking it.

No alternative text description for this image

I wanted to sponsor a dev conference for $100K. My marketer said no. He spent $12,500 on lunch instead. $2.6M return.

We’d just raised our Series B.

I wanted to get the word out. 
Fill top of funnel.
Make some noise.

A dev conference seemed like the obvious move.
$100K for a booth, some swag, maybe a speaking slot.

But my marketer pushed back.

“We don’t have the bandwidth to staff a booth for three days.”

He proposed something simpler.
“What if we just bought developers lunch?”

I posted a short note on LinkedIn:
“Lunch is on us. We just raised our Series B and want to celebrate with SF devs.”

We linked a Typeform.
We manually reviewed every submission and sent out $25 DoorDash credits to 500 developers.

Total cost: $12,500.

No follow-up email.
No pitch.
No “book a demo” link.
We didn’t even send them to our website.

Over the next four months, the ROI started to show.
Those 500 developers remembered the gesture.
Some signed up for the product.
Others brought their entire teams or sent us referrals.

$2.6M in revenue traced back to that one day.

It came from 500 people who had a positive, friction-less interaction with our brand because we didn’t try to sell them anything.

Lost a $815K deal. 6 months later, their CTO Slacked: “Read your HN article. Are you free today?”

Three months of conversations.
Clear use case.
Detailed technical validation.

Then the CTO reached out with a short message:
“We’ve decided to build this ourselves.”

No further explanation.
The deal was dead.

Six months later, my Slack lit up. Same CTO.
“Read your HN article. Are you free today?”

On the call, he was candid:

They’d spent 6 months building. Five engineers.
They got it working for one meeting platform. Stable-ish.
Nowhere near production-ready.

Then someone on their team sent the CTO our HackerNews article about Postgres scaling issues.
He read it and realized: we were solving problems they hadn’t hit yet, but he could see them coming.

They needed to ship their product. Not become an infrastructure company.

We signed two days later.
They were live in two weeks.

Sometimes the fastest way to close a deal is to let the customer try to build it.
When they see you’re operating one layer deeper than they ever want to go,
they’ll come back on their own.

I track WFH as PTO. Gray areas kill company culture faster than bad hires.


We’re a five-days-a-week, in-office company.

The hardest part of making that work is avoiding ambiguity.

The gray area looks like this:

Someone flies to Boston to attend their sister’s graduation.
They say they’ll work remotely.
They’re “technically” working.
Kind of available.
Half present.

In practice, that creates confusion.
Are they off?
Are they on?
What should others expect?

I chose clarity instead.

If you’re away, you’re off.
WFH is marked as PTO, and PTO doesn’t count against you.

If you’re traveling, enjoy it.
If you’re in the office, be fully here.

I’d rather someone disconnect completely than try to split attention.

Clear expectations create trust.
Ambiguity erodes it.

Cluely is a $120M company. They became a customer because we didn’t treat them like everyone else.

2:04 AM. Self-serve signup.
Cluely.

We went back and forth a bit.
Then we realized they were a 15-minute Waymo ride away.
So instead of adding another call, we showed up.

I’d been watching Cluely for a while.

They taken bigger swings with a higher hit rate than companies 10x their size by being:
intentional,
opinionated,
completely uninterested in generic playbooks.

That set the bar.

So we brought three things:

1/ a Teenage Engineering Recorder, engraved with their name

I picked the recorder because I personally think it’s cool, 
and because it actually maps to what we do.

I chose to engrave Cluely, not Recall.ai, to make it irreversible.

If this went nowhere, the effort was still spent.

The gift was one of one.
Swag without risk is meaningless. 

-----

2/ 10 lbs of chicken

We offered pizza. Their engineer said they wanted protein.
So we brought 10 lbs of chicken.

It seems trivial, but because we showed up exactly how they asked, 
the tone for the rest of the visit was set.

We’re here to understand what you’re actually building.

-----

3/ Tucker V., our engineer

Normally, this would’ve been a sales meeting.
Engineering would’ve come in later.

Instead, we brought Tucker on the first visit.
He worked with their engineers to start implementing Recall.ai live.

Instead of hypotheticals, now the discussion was grounded in what worked and what didn’t.

-----

By the end of the visit, there wasn’t much left to debate.

We’d already done the work.
We’d already made the tradeoffs visible.
And both sides knew exactly what this would look like in practice.

That’s how Cluely became a customer.

Wanting to work at OpenAI is a startup red flag.


Startups can’t and shouldn’t compete with OpenAI on salary or prestige. 
Especially at an early-stage.

So the people who actually fit startups are those who are skilled, but don’t want to work at OpenAI.

Because at OpenAI’s size,
- you’re rarely working on the thing that moves the business
- you don’t get to see how the whole system works end to end

It’s a tradeoff.

Early-stage startups need people who want exposure to consequence.
People who want to see how decisions ripple through the entire company.

I don’t want the people who want OpenAI now.
I want the people who wanted to join OpenAI before it was cool.

I’m not the reason our $250M company exists. 
If you’ve talked to my cofounder, you already know why.

One thing David is unusually good at is getting to the actual problem.

If I’m struggling to fill a role and bring it to him, the conversation doesn’t stop at “that’s hard” or “we’ll figure it out.”

It turns into:
- ”What have you tried so far?”
- “Who have you reached out to?”
- ”Did you test X? What about Y? Why not Z?”

I always leave those conversations with more work.
And that’s exactly what I need in the moment.

-----

That way of thinking sets the tone for how we operate.

It’s uncomfortable to be explicit about working long hours.
A lot of companies dance around it.

David doesn’t.

He’s clear about what this phase demands and steps up alongside everyone else. 
That clarity matters more than pretending otherwise.

-----

You can see his influence everywhere at Recall.ai.

A lot of teams try to do too many things at once. Parallelize everything.
David pushes the opposite.

One thing at a time. 
Finish it completely.

Doing things sequentially feels slower, but it consistently produces better outcomes.

“Done” doesn’t mean shipped.
Done means it actually works.
Done means people adopt it.

That mindset spreads.

------

Problems don’t get smaller around David.
They get clearer.
And you leave knowing exactly what to do next.

Which, in my experience, is why this works.

The expensive habit I had to unlearn managing a $250M company:
answering my team’s questions too quickly.

Before, these conversations were automatic.
Direct report: “Hey, I’m having trouble with this.”
Me: “Okay, then do X, Y, Z.”

But as the company grew, so did the problems.
Scopes got larger.
Situations got more nuanced.
The people asking often had teams of their own.

Every time I jumped in, 
I’d have to reconstruct context I didn’t fully have,
solve a problem I wasn’t closest to,
and walk away thinking: “that’s another thing on my plate.”

Now, when a direct report brings me a problem, 
I don’t respond with a solution.

I ask one question:
“What’s your plan?”

That does three things immediately:
- it keeps ownership with them
- it gives them the opportunity to figure it out 
- it lets me evaluate their abilities

I can still give feedback.
I can still pressure-test the plan. 
But I’m no longer carrying the problem for them. 

Teaching judgement takes more patience upfront.
But solving everyone’s problems costs you time and hides where your team actually needs to grow.

$623K deal. Two weeks to close. Then my champion went dark.


We’d been working this deal for 6 weeks with their VP of Engineering.
Kickoff was on the calendar.
I sent a final check-in.

My email to him bounced.
Out of office. Cabo. Back in 10 days.

I wasn’t worried.
Until our AE pinged at 4pm:
”Someone from product just killed the deal.”

A new stakeholder I’d never met had stepped in.
She reviewed everything and said no.

Her objections were familiar:
- risk of migrating
- fear of being on the hook if this broke in production

Every one of those had already been worked through with the VP of Engineering.
She just hadn’t been part of those conversations.

With 2 weeks until kickoff and no champion, the deal was effectively dead.
So I got on a plane to NYC that night.

The next morning, I grabbed breakfast with her.
And just listened.

She walked through her concerns. I brought her up to speed on everything she’d missed.
The 6 weeks of discovery. The plan. The commitments we’d made.

We signed 10 days after I landed in NYC.

When a new buyer says no, they’re rarely blocking the deal.
They’re protecting themselves from owning risk they didn’t sign up for.

“You’re here to replace me.” The lead engineer’s voice was shaking.
Two weeks later, he pushed our $800K deal through.

Up until that moment, Mike hated me.

Every sales call was a fight.

“Our current system already does this.”
“We don’t need another platform.”

I tried acknowledging his concerns.
I offered customer intros.
I tried logic.

Nothing worked.
The deal was dying.

So on a 1:1 call, I finally asked him:
“Why are you so against this?”

Long pause.

“You’re here to replace me.”

That’s when it clicked.
I was calling his baby ugly.

I was asking him to rip out months of his work.
To admit his system didn’t matter anymore.

I was trying to solve an emotional problem with a logical solution.
On the next call, I reframed the deal to make him the hero.

Instead of talking about what our product would replace,
I focused on what he would unlock.

He’d ship the new infrastructure in 2 weeks,
Get credit when it launches,
And finally get to spend time on interesting problems instead of babysitting plumbing.

Suddenly, the same engineer who was shutting us down
Became the one pulling the deal through procurement.

2 weeks later, I closed for $800K.

Most “hostile” stakeholders aren’t anti-you.

If you make their win obvious,
They’ll do more for the deal than you ever could.

I got 135 DMs last month asking “How on earth are companies paying you $1M+”


The short answer isn’t sales tactics or clever pricing.

It’s that LLMs can now automate most data entry and admin work.

Sales notes don’t need to be typed into a CRM.
Interview feedback doesn’t need to be copied into an ATS.
Standups don’t need to be summarized into an issue tracker.

Knowledge workers spend up to 70% of their time on admin work.

But for an LLM to do this well, it needs deep context.

Most of that context isn’t written down.
It’s in conversations.

And conversations are the largest human-generated dataset in the world.

There are ~0.88 quadrillion words on the internet across all of history.
Knowledge workers speak ~3.3 quadrillion words every year at work.

That’s why Recall.ai exists.

Recall.ai is the API for meeting recording.

Developers use Recall.ai to capture recordings, transcripts, and speaker attribution from meetings, desktops, and phone calls - without managing bots, integrations, or real-time infrastructure.

Our customers are building AI products that run on conversation data.

We price on usage.
Our new pricing starts at $0.50 per meeting hour and scales with volume.

The ROI shows up as:
- shipping months faster
- not staffing teams to maintaining brittle infra
- engineers spending time on product differentiation instead of plumbing

I’m deeply grateful to the teams and engineers who trusted us and are building on top of Recall.ai every day.

It’s still early, but it’s becoming clear every serious AI product will run on conversations.

I planned to hire 20 AEs to 3x revenue to $XXM. We did it with 5.


One rep brought in 230+ new logos.
Another closed a 7-figure deal on their 2nd month.

We passed on hundreds of candidates to find these people.
They’re incredible.

Now demand is outpacing what 5 people can handle.
So we’re hiring number 6.

If you’ve closed enterprise deals and want to see what you can do here, check out the JD below.

After growing 3x and raising our Series B, my co-founder is paying it forward to infra founders in SF.

The last 5 years, we learned how hard infra actually is:
finding PMF, earning trust, and executing at scale.

We only got here because other founders were generous with their time and advice.

David Gu wants to do the same.

If you’re building (or thinking about building) an infra company in SF, he’s opening up office hours for coffee and conversations.

Let me know in the comments, and I’ll connect you.

If I were to go back to $10M ARR, I would tell myself to walk away from that multi-million dollar deal. 

At the time, it felt insane not to chase.

Huge logo.
Real usage.
Their engineers were excited.

On paper, it looked like a perfect deal.
But I missed something in discovery.

The prospect’s problem was real, but it wasn’t on fire.
No one’s job depended on fixing it now.

So the deal stalled.

Looking back, harder discovery would have made this obvious:
- “Who feels the pain personally?”
- “What breaks if this doesn’t get solved this quarter?”
- “What internal initiative is this competing with?”

If I had pushed on those questions,
I would have found out before the deal died that 
the prospect’s problem wasn’t urgent enough yet. 

Big deals are expensive when they’re mistimed.
They pull you away from deals that can close,
and customers who can actually succeed with your product.

Learning when to walk away from a large deal is one of the most important sales skills you can develop.

As a co-founder of a $250M startup, here’s how I get direct reports to own things outside of their job description.

First, a misconception.

Most people don’t avoid ownership.
They avoid owning things that feel optional.

That’s a communication and hiring problem.

Here’s how I address that:

1/ Make ownership explicit by tying it to consequence

When I want someone to own a project,
I don’t assume they understand why it matters. 

I make the expectation and the consequence clear.

For example, I’ll say: 
“This is a top priority.
This came directly from a customer who’s blocked on it.
If we don’t solve this, we risk losing them.”

That does two things at once:
- it clarifies why the work matters
- it sets expectations

-----

2/ Hire for people who optimize for impact

Some candidates are drawn to the work itself.
A specific programming language.
A tool.
A narrow type of work they enjoy.

Others care about impact.
It’s less about the how, more about
what are we doing, and why are we doing it.

In interviews, I listen for the difference.

When someone describes their past work, I’ll ask:
- “Why did you choose to do that?”
- “Why did that matter to the business?”
- “What changed because of it?”

People who are impact-driven can explain:
- the business reason behind the work
- the tradeoffs they made
- the outcome they were responsible for

Impact-driven people are the ones who adapt when the job keeps changing.

-----

Startups don’t stay static long enough for rigid roles.

To manage through constant change,
you have to make outcomes explicit and hire people who can own them.

Shortly after raising our $38M Series B, we realized we had a problem: we had no system for surfacing what our reps were learning from the market.

Reps talk to prospects every day.
They hear people are saying.
They see objections evolving.
They notice when the buying committee changes.

But that intelligence was disappearing.

Yes, you can watch sales calls. That takes hours.
Yes, you can ask people. But it’s random and incomplete.

We needed a way to systematize it.

That’s when I started running situation rooms.

Here’s how they work:
- First 5 minutes: everyone fills out a form together (blockers, customer intel, priorities)
- Next 5 minutes: everyone reads
- Rest of the hour: we talk through what’s surfacing

The form is public. Anyone can read it.
Engineering sees what sales is hearing.
Marketing sees the patterns across deals.

The repetition matters.
Every week, the same format. The same questions.
It keeps critical information top of mind.

And it creates space for knowledge transfer that doesn’t happen anywhere else.

0.4%. That’s the difference that won a $1.1M deal in our AEs second month.


Liz was selling to the CEO of a company that was recording 1M meeting hours per month.
They’d built their own meeting recording infrastructure.

99.5% uptime.
We offered 99.9%.

Leadership wasn’t impressed.
A 0.4% difference doesn’t justify ripping out something you already built.

This is where most ramping AEs lose the room.

Liz didn’t argue the number.
She translated it.

“At your scale, that’s 4,000 meetings per month that don’t get recorded. What happens, when that happens?”

That’s when it connected:
- Customer conversations that can’t be recovered.
- Customers lose trust fast when the product “misses” what they said.
- Engineering gets dragged into firefighting instead of shipping product differentiators.

They signed two weeks later.

This was Liz’s second month…

At enterprise scale, decimals aren’t details.
They’re thousands of conversations that either get captured or disappear.

p.s. we’re hiring for Strategic Account Executives. JD in the comments

One of the scariest parts of leading a $250M company is taking visible bets. Last year, I made one knowing short-term revenue would drop.

Not eventually.
Not hypothetically.

Immediately.

This wasn’t a small optimization or a local decision
It affected the entire company.

When a decision changes pricing, packaging, or how customers buy, there’s no hiding it.
Everyone feels it at once.

That’s what makes these bets hard.

You worry about the obvious things:
- What if it fails?
- What if people lose confidence?
- What if teams feel like they’re paying the price for a bad call?

And you worry abut the less obvious ones:
- What signal does this send internally?
- What precedent does it set?
- What happens if the numbers dip before the upside shows up?

In our case, the tradeoff was clear.

Short-term revenue would go down.
Long-term velocity, leverage, and customer access would go up.

There’s no committee for bets like this.
No vote.
No A/B test.

At a certain level, leadership is about owning the blast radius.

That’s the part of leadership most people never see.

p.s. we closed the year with six seven-figure deals

The exec team you hire at $3M ARR will be wrong at $20M ARR.


At $3M ARR, most startups are still figuring out their motion.
What works feels fragile.
What doesn’t work isn’t obvious yet.

And yet, that’s often when the pressure to hire for execs shows up.

The mistake isn’t the leaders you hire.
It’s assuming the shape of the company is stable enough to lock in leadership roles.

In our case, I waited to $6M ARR to hire our first VP.

If we had hired earlier, we would’ve optimized for a version of the company that didn’t last.
I wouldn’t have understood the work well enough to know what “good” actually looked like.

By then, the motion had settled.
The tradeoffs were clear.

Hiring an exec team early feels like progress.
But it also freezes assumptions and those are expensive to unwind later.

Synapsa

Building the world's #1 AI to convert buying intent into qualified, booked pipeline.

- Backed by Drive Capital & 11 Tribes Ventures
- US Patent Granted for Text-to-Schedule Technology
- 2023 AI Startups to Watch (American Inno)
- Winner of 2022 Global Slack Hackathon
- Winner in 2022 Square Global Hackathon
- Main Street Ventures Grant Recipient
- OCEAN Accelerator Class 7

Co-Founder & CTO

NielsenIQ

Promoted to lead Data Science Product Development and Delivery for North America’s Retail Intelligence division before leaving to start Synapsa.

Vice President - Data Science

Nielsen

Led a combined team of Data Engineers and Data Scientists that built analytical products for Retailers at an unprecedented level of scale and efficiency.

Director - Data Engineering

Led team that developed hybrid cloud-based scalable, automated processes which acquire and blend data from a multitude of corporate factories in a seamless manner for a variety of stakeholders from business analysts to data scientists.

Associate Director - Data Platforms & Product Delivery

Responsible for conceptualizing, developing, delivering, and maintaining the next-generation reporting platform. Worked with internal and external stakeholders to understand customer needs and build tailored solutions for Walmart and Sam’s Club.

Solutions Manager - Insight Products Development

GE Aviation

Responsible for leading the development, test, and delivery of jet engine software for two multibillion-dollar program initiatives across seven cross-functional teams spanning over 100 developers in the U.S. and Mexico.

Lead Software Engineer

Supported new program initiatives as an individual contributor working on metrics platform development and embedded jet engine software control logic while completing GE’s renowned Edison Enginerering Development Program (EEDP).

Software Edison Engineer

GreenWave Sciences

Worked on research and development of a microwave-energy alternative to pre-plant chemical pesticides as part of the Duke University Pratt Undergraduate Research Fellows program.

Research Engineer

L-3 Communications, Electro-Optical Systems Division

Published "Single and Multiple Event Temporal Response Characteristics of Ungated I2 Tube Systems" (SPIE Proceedings, 05 May 2010). Findings used to help identify ideal systems for military helicopter pilots under siege from enemy ground fire.

Research Intern

Mike Bell 🗓️⚡️

We have a client who sends over 500k emails per month and was drowning in replies. She believed AI could work but got absolutely burned by a prior vendor. Gave us a shot. 200 booked meetings later...

The easiest way to build the best GTM conversations at scale | Patented AI Builder | Believer, Husband, Girl Dad x 3

My prediction is we are walking into a golden age of non-computer engineering. A ton of kids who would have historically studied CS are going to choose harder sciences and we will be better off for it as a society. 

With the barrier to coding falling through the floor, our best minds can focus on higher order and more difficult engineering problems.

We have 3 full-time engineers on the Synapsa team. This is the product vision I gave them for 2026 ⬇️

𝗧𝗵𝗲 𝗲𝗮𝘀𝗶𝗲𝘀𝘁 𝘄𝗮𝘆 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗚𝗧𝗠 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲. 

3 engineers, 3 clear focus areas:

(𝟭) "𝗧𝗵𝗲 𝗲𝗮𝘀𝗶𝗲𝘀𝘁 𝘄𝗮𝘆 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱" - we've already made it pretty darn easy from what I've been told by folks who have used other tools, but we still have a lot of room to grow here (think MCPs, more "easy-button" playbook deployment, deeper first-party integrations, etc). 

(𝟮) "𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗚𝗧𝗠 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀" - objectively, at the end of the day, the conversation either gets a prospect to take a next step or it doesn't. Subjectively, it either "feels good" or doesn't. We care a lot about both of these, and the company that wins in this space will be the one that both gets people to take an action and makes it feel natural. 

(𝟯) "𝗮𝘁 𝘀𝗰𝗮𝗹𝗲." - kinda obvious but not easy...this is (and was before AI) still a numbers game to some extent, and if you can't actually scale the conversations (reliably) across channels then you'll lose to the team that can. 

Every one of these is mapped to a Linear initiative, and we'll build our projects / issues under them. 

If the question "how does this make our product easier to use / have better conversations / operate at scale" can't be answered, then we won't do it.  


P.S. If you're looking into ways to start more conversations that turn buyer interest into qualified, booked meetings in 2026, we are here for you.

taplio

The Battle

Amanda Zhu

Mike Bell 🗓️⚡️

Followers

Posts

Engagements

Likes

Performance ScoreAmanda Zhu's LinkedIn Performance

Performance ScoreMike Bell 🗓️⚡️'s LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Amanda Zhu

Mike Bell 🗓️⚡️

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Amanda Zhu's LinkedIn Performance

Performance ScoreMike Bell 🗓️⚡️'s LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreAmanda Zhu's LinkedIn Performance