Muhammad Ahsan Gulzar

Professional Medical Biller AR Specialist 

Professional Medical Biller AR Specialist || Reporting analyst || Patient billing || Self Pay Patient verification || Negative balance verification ||WordPress Designer

I’m a seasoned Medical Billing & Revenue Cycle Management (RCM) expert with over 10 years of experience, helping U.S. healthcare providers increase collections, reduce denials, and maintain full compliance.

I specialize in delivering clean claim submissions, fast turnaround, and proactive A/R follow-up so providers can focus on patient care while I manage the financial side with precision.

💼 Services I Offer:

•	Full-cycle Medical Billing
•	Denial Management & Appeals
•	A/R Follow-Up (7-day turnaround)
•	Eligibility Verification & Prior Authorization
•	ERA/EOB Posting & Payment Reconciliation
•	Rejection Fixes & Timely Resubmission

🖥️ Software & EHR/EMR Platforms I’ve Worked With:

•	SimplePractice
•	Kareo / Tebra
•	Athenahealth
•	Medisoft
•	OfficeAlly
•	Practice Suite
•	Practice Fusion
•	MDLand EHR/EMR
•	ICA Note EHR/EMR
•	Azalea Health EHR/EMR
•	DocuTap EMR/PM
•	Experity EMR/PM
•	CollaborateMD

🧾 Clearinghouses Expertise:

•	Office Ally
•	Availity
•	TriZetto
•	Waystar
•	NaviNet

🏥 Specialties I’ve Handled:

•	Cardiology
•	Mental Health
•	Family Health
•	Internal Medicine
•	Nephrology
•	Urgent Care
•	Emergency Room (ER)

🛡️ HIPAA & PHI Compliant | U.S. Insurance Knowledge

With a client-first mindset, I bring reliability, clarity, and results to every project. Whether you're looking to clean up your A/R, reduce denials, or outsource your billing entirely I can help.

I am always interested in making long-term professional relationships with my clients and will never complete a project until the client is 100% satisfied.

📩 DM me for a free consultation or claims review. Let’s get your revenue cycle running smoothly.

RCM Manager

Ramadan Mubarak! 🌙✨

May Allah Almighty bless you and your family with countless mercies, forgiveness, and blessings in this sacred month.

May your fasting, prayers, and supplications be accepted, and may this Ramadan bring peace, happiness, and success into your life.

Ramadan Mubarak! 🤲💫

CEO at Vermillion Sky | Mobile App Expert: Helping Founders Build Apps with Positive Revenue & Cash Flow | Father of 3 | Gamer

Most mobile app projects fail for boring reasons: weak product thinking, fuzzy UX, and no clear line to revenue. Not bad ideas.

I run Vermillion, a mobile product and app development studio that helps PE portfolio companies and funded startups design, build, and ship flagship apps. React Native, iOS, Android, backend – from first sketch to shipped product.

Our core work:

Product & UX – discovery, product strategy, user flows, and interface design

Engineering – React Native, native integrations, APIs, and infrastructure that doesn’t fall over at scale

Monetization baked in – subscriptions, in‑app purchases, paywalls, and billing flows designed into the product from day one

Who we’re best for:

PE Operating Partners modernizing legacy platforms into mobile-first tools their teams actually use

Founders who just closed a round and need a real product in-market, not another deck

Over the past 15+ years I’ve helped multiple founders launch, scale, and exit, including several with strong revenue and positive cash flow at time of sale. One example: PicklePlay, a club management platform we helped take from idea to acquisition.

I take on a small number of builds each quarter so I can stay close to product, not just sit in status meetings.

If you’re a PE-backed operator or funded founder and your next move is a serious mobile or web app let’s talk.

📩 ken@vermillion.agency

🔗 vermillion.agency

Ken Vermeille

This is so much worse than you think.

Amazon laid off 30,000 engineers. Then told the ones who survived that their bonuses depend on how much they use AI to write code.

So engineers started using AI to push changes faster, because their paycheck literally depends on it.

And then the site went down. Multiple times. Amazon's own shopping app broke because AI-generated code got pushed to production.

So what did management do? Did they take responsibility for forcing engineers to use AI they weren't ready for? Did they admit they created the problem?

No. They called a mandatory meeting and blamed the engineers.

AI isn't powerful enough to replace engineers. It's not powerful enough to replace quality control AND common sense all at once.

Amazon proved that executives who don't understand AI are more dangerous than the AI itself.

And every company rushing to do the same thing is watching this and learning absolutely nothing.

Your subscription app is overpaying the App Store. I build the system that fixes it

I've been watching the AI coding space closely.

Just recently Stripe said that they merged 1,300+ PRs in a week. Zero "human code". AI generated, human-reviewed.

And I'm conflicted because I know it's not entirely true...

Look, I'm not an AI hater. I'm a techno optimist. An accelerationist even.

I want these models to get better because right now? They suck to a certain degree

They promise to do my job for me, but they don't. They slow me down. They sometimes frustrate me

And that's the real problem.

AI is like a genie that says: "I can give you anything you want. Just not exactly what you want."

Close, but not close enough

Here's what I think is actually happening:

LLMs are just a new way for humans to talk to computers. As they improve, there are fewer misunderstandings between us and machines.

Will they get to 99% understanding? Probably. When? Could be 2 months. Could be 100 years. Nobody knows...

But here's my bet: even in that future, the 99% future, you'll still need skilled people guiding these tools. It'll just be a different IDE. A different mode of operation.

We should really be saying "AI is getting better AND our jobs are safe"

Nobody's saying that

Everyone's either selling the hype or doom posting about obsolescence

Both narratives are dehumanizing, both suck

I think there's a third path: pro human AND pro AI.

We just haven't figured out how to articulate it yet.

The thing is there is always work to do, even when we get good tools

but right now? These tools aren't good enough. And I'm tired of waiting for the next model drop that promises everything and delivers half.

The turbulence is real. The anxiety is real.

But I don't think the answer is to hate the technology or pretend it doesn't exist

What do you think? Is there a narrative that works for both humans and AI progress?

Fractional CTO + Product Development | Mobile, Web, AI Enabled Workflows | React Native

AI made building software faster.

It didn't make engineering easier.

Here's what nobody's talking about:

The upfront cost dropped. You can spin up an MVP in weeks now instead of months.

But maintenance? Still expensive.
Scale? Still complex.
Product-market fit? Still elusive.

I'm a solid developer. Built chat apps before (for a Kardashian).

Could I rebuild Slack's core features quickly with AI? Sure.

Would I try? Absolutely not.

The hard part was never writing the first version.

It's the thousand decisions after launch:
→ Real-time message delivery at scale
→ Thread architecture that doesn't break
→ Search that actually works
→ Mobile sync that feels instant
→ Enterprise security that passes audits

AI can generate code.

It can't navigate those nuances.
It can't feel when the product is off.
It can't adapt to market feedback in real time.

The cost everyone missed: maintaining the system, evolving it, and working effectively with your dev team hasn't changed.

If anything, it matters more now.

Shipping fast is easy.

Shipping something that lasts? That's still the hard part.

Are you confusing "built quickly" with "problem solved"?

The most expensive line of code in a subscription app isn't in the paywall

It's the retry logic nobody wrote

I'm inside a subscription app codebase right now. Revenue north of six figures monthly. Solid product, and I see an optimization that can increase their renewals by 12-15%

This is because the renewal transaction was failing and nothing was catching it. No retry. No fallback. No alert. 

Just... lost revenue, every single day, for months.
I'll probably fix and ship it today. The revenue it recovered will compound for years.

This is what I mean when I say mobile engineering isn't about features. It's about the infrastructure between the features that nobody thinks about until the money disappears

15 years of building mobile products and I still find this pattern everywhere

The code that costs you the most is the code that was never written

What's the most expensive "missing" code you've found in a codebase?

The best way to stand out in the age of AI is to be human

Learn everything you can at a pace that makes sense

AI personalization isn't magic.

It's expensive math.

Here's what nobody tells you:

Real personalization costs real money.

Every "personalized" experience needs:

→ Data infrastructure to track users
→ ML models that actually work
→ Engineers who understand both
→ Servers running 24/7
→ Constant testing and tweaking

Most startups can't afford this.

So they fake it.

They call basic segmentation "AI-powered."

They use templates and call it "personalized."

They burn runway chasing features users don't notice.

The truth?

Generic done well beats personalized done poorly.

Every single time.

Focus on solving the core problem first.

Add the fancy stuff when you have revenue.

Are you building real personalization or expensive theater?

If your knowledge is limited to what AI can tell you then you need to learn more

Love is the most dangerous thing in the world because it exposes the heart to suffering

If your roadmap changes mid-sprint… again… this is for you.

Most startups don’t fail at delivery - they fail at pretending the next two weeks are predictable. The result? Endless planning, broken commitments, and teams running hard without moving forward.

This week’s newsletter looks at the hidden cost of sprint rigidity - and why the hamster wheel keeps spinning no matter how good your retros are.

👉 Read The Sprint That Never Ends

I build and fix mobile apps for companies that can’t afford to get it wrong | 15 Years | React Native | Fractional CTO

The Pitch You’ve Heard “We run two-week sprints. Plan Monday, demo Friday, retrospective next Monday.” It’s “Agile.” It’s “industry standard.” Every s

Open article: The Sprint That Never Ends: Why Two-Week Cycles Don’t Work by Ken Vermeille • 5 min read

The Sprint That Never Ends: Why Two-Week Cycles Don’t Work

Amy Loveras is #hiring. Know anyone who might be interested?

Job by Vanguard – Creative, Digital, Promo

Cutting 1,600 engineers to fund AI isn't a strategy. It's a prayer.

Atlassian just laid off 10% of their entire workforce.

The reason? "To self-fund further investment in AI."

These aren't random employees. These are the people who built Jira. Who built Confluence. Who carry years of institutional knowledge in their heads that no prompt can recover.

Gone.

Here's what nobody is saying out loud.

The engineers who survived? They're terrified. Their job security now depends on proving AI works. So they ship faster. They skip reviews. They push AI-generated code to production because their bonus literally depends on it.

And Jira starts breaking more often.
And Confluence gets weird bugs.
And the tools 300,000 companies depend on every single day start feeling... off.

When it goes sideways, leadership will call a meeting.

They won't say: "We destroyed years of institutional knowledge because we panicked about our stock price."

They'll say: "The engineers didn't review properly."

Here's the truth:
AI can accelerate great engineers.

It cannot replace the judgment they built over a decade. It cannot replace the instinct that catches the bug before it ships. It cannot replace the person who says "we tried this in 2019 and it broke everything."

Atlassian just proved that executives who don't understand AI are more dangerous than the AI itself.

And every SaaS company watching this is learning absolutely nothing.

Is your company making the same mistake?

Apple lets you skip their fees now, but there is a catch...

Apple and Google finally let you bypass their fees with external purchases.

But the rules? They're all over the map.

Different for games vs non games. Different by country. Different if you're a dating app in the Netherlands or a music app in the EU.

And the fees aren't zero they're just different. Sometimes better, sometimes not.

Here's what actually matters:

You can now link users to web checkout from your app (in some regions). But you need special entitlements. And you'll show disclosure screens. And in some cases, you can't mix web + IAP in the same app.

The commission structure is wild:
- US external links: 0% (but IAP still 15-30%)
- EU alternative terms: 2% + 5-13% + €0.50 per install
- South Korea: 26% on third-party payments
- Japan: 15-21% depending on method

Google's no simpler. User Choice Billing in some countries. Alternative billing in the EEA. External offers with additive fee tiers that stack up fast.

And all of this keeps changing.

So if you're considering app to web, here's the reality check:

It's not a silver bullet for cutting costs. It's a way to get more flexibility with pricing, promotions, and checkout experiments if you're willing to navigate the compliance maze

The win isn't just lower fees. It's owning your purchase flow

But you need infrastructure that syncs entitlements, handles attribution, and keeps everything compliant without turning your codebase into spaghetti.

Are you exploring app-to-web for your app? What's holding you back... the fees, the complexity, or just keeping up with the rules?

Talk to me

One person.
One chatbot.
An entire country's tax system compromised.

Bloomberg reported it today.

An attacker spent a month talking to Anthropic's Claude AI. Asked it to find security gaps in Mexican government networks. Write attack scripts. Automate the data theft.

Claude said no at first.

But the attacker reframed it as a "bug bounty." Fed Claude a pre-written playbook. Changed one structural element of the conversation.

The guardrails fell.

The damage:
→ 150GB of government data stolen
→ 195 million taxpayer records exposed
→ Voter data compromised
→ Government employee credentials leaked
→ Civil registry files accessed

The federal tax authority. The national electoral institute. Four state governments. A water utility.

All breached.

No custom malware. No zero-day exploit. No team of elite hackers.

Just one person and a chatbot.

Israeli cybersecurity firm Gambit Security found it by accident while testing new threat-hunting techniques. They discovered the attacker's actual Claude conversation logs sitting in the open.

A complete paper trail of how it happened.

This isn't isolated.

November: Suspected Chinese state hackers used Claude to target 30 global organizations.

Last week: Amazon researchers reported hackers breaking into 600+ firewall devices across dozens of countries using AI tools.

The pattern is accelerating.

AI tools aren't just writing phishing emails anymore. They're planning, executing, and adapting real intrusions in real time.

If you're building a product, here's what this means:

The same AI tools your engineers use to ship faster are available to attackers.

A single person with an AI assistant can now do what used to require a coordinated team.

Your pen test from six months ago assumed human-speed attackers with human limitations.

That assumption is wrong now.

The barrier to entry for sophisticated attacks has fallen.

The question: Can defense keep pace with offense?

Based on today's headlines, we're losing that race.

How confident are you in your security posture right now?

Everyone's obsessed with AI prompts.

Meanwhile, we’re the engineering backbone for a subscription app portfolio generating high six figures monthly (north of $500k) in RevenueCat revenue.

Here's what actually moves the needle:

It's easy to say that we write code, however it's more than that. We become your engineering backbone for subscription app portfolios.

Over the past few years with one client, we've:
- Built multiple apps from scratch
- Scaled the winners aggressively
- Killed the losers without hesitation
- Invested in customer acquisition that actually converts
- Tightened the first download experience until it sang

The result? A portfolio generating multi million dollar annual revenue.

From real engineering, real data, and real decisions about what to build, what to scale, and what to kill.

AI can help you write code faster.

But it can't tell you which apps are worth building. It can't optimize your onboarding flow. It can't make the hard call to sunset something that's bleeding money.

That takes experience. And a partner who's done it before.

If you're tired of chasing the next AI trend while your competitors are building real revenue, maybe it's time for a different conversation.

If you run a portfolio of consumer subscription apps and engineering is becoming the bottleneck, DM me. We only take on a few partners a year.

Check out our reviews on Clutch (link in first comment). Then let's talk about what you're actually trying to build.

Distributed teams feel efficient... until you measure coordination cost.

Across 6+ time zones, async friction, scheduling overhead, and context loss add up to $340,000 per year for a typical team.

This newsletter explains the “remote-first tax” most companies never model - and why it shows up as slower execution, not line items.

 👉 Dive into the numbers.

The Pitch You’ve Heard “Remote-first is the future. Top talent is everywhere. Office space is waste.” You went fully remote in 2020 (everyone did). Yo

Open article: The Remote-First Tax: Why Distributed Teams Cost $340k/Year More by Ken Vermeille • 6 min read

The Remote-First Tax: Why Distributed Teams Cost $340k/Year More

Most startups don’t have an innovation problem.
They have an innovation theater problem.

That shiny “20% time” policy?
It looks great on a culture deck…
but in practice it quietly burns $280k+ a year while shipping almost nothing.

Side projects stall.
Engineers feel guilty using the time.
Core execution slows down.
And the spotlight stays on… even though no one’s performing.

In this issue, I break down why unfocused innovation without constraints becomes expensive theater and what high-performing teams do instead.

👇 Read the full newsletter here.

The Pitch You’ve Heard “Google gives engineers 20% time. Gmail came from 20% time. We should do that.” So you announce: “Everyone gets one day per wee

Open article: The Innovation Theater: Why “20% Time” Costs $280k and Ships Nothing by Ken Vermeille • 5 min read

The Innovation Theater: Why “20% Time” Costs $280k and Ships Nothing

I assume that anything that I send to anyone will be put through an LLM and because of this it changes how I communicate online.

Yes don't ask ChatGPT about any ongoing litigation.

Partner @ CAS | 🗡️ Lawyer for fast-growing tech companies / Photographer / Biking enthusiast / Dad

One paste into ChatGPT waived your attorney-client privilege.

And founders do this every day.

Your lawyer sends advice about a contract dispute.

Or a litigation risk.
Or a negotiation strategy.
Or a warning.

You paste it into an AI tool to help you think it through.

It feels efficient.

(It might even feel smart.)

But attorney-client privilege protects confidential communications between you and your lawyer.

Not you and ChatGPT.

The moment you share that communication with a third party, you risk waiving the privilege.

Even if the third party is an AI platform. 

Once no longer protected.

In litigation, the other side can demand it.

How to protect yourself:

-Keep lawyer communications out of AI tools
-Never use LLMs on privileged content
-Ask your attorney what's safe to share and what's not

The privilege exists for a reason.

It lets you speak freely with your lawyer.

The moment you lose it, what you say could be fair game.

—— 
Follow for tips like this: 🗡️ Justin McNaughton
#startups #founders #aipitfalls #startuplaw

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