I help entry-level, non-technical professionals get their first data analyst role | Data Analyst @RBC | Data Career Coach

I help immigrants and career-switchers land analyst roles—without burning out on certificates.

By day I build decision-grade analytics (currently at Autodesk). By night I run Data Career Compass, where we treat your job search like a product launch:
Market → Positioning → Portfolio → Distribution → Interviews.

My POV:
Most candidates try to add more tools. Winners learn to package their value. Your portfolio isn’t a gallery; it’s a sales page for your judgment.

Receipts:

→ Professionals land interviews by aligning projects to business outcomes (not toy datasets).

→ My frameworks have been used across finance, tech, and education to translate experience into results.

→ Built data products and controls in regulated environments (RBC Capital Markets) that leaders rely on daily.


What you’ll get here:

→ Weekly breakdowns of real projects (SQL, Python, dbt, DuckDB, Looker/Tableau/Power BI).

→ Storytelling patterns that turn insights into action.

→ Templates that remove friction: portfolio briefs, stakeholder one-pagers, interview scorecards.

Coming February:

I’m launching a coaching program designed for mid-level and senior professionals ready to transition into analytics.

This isn’t an entry-level bootcamp—it’s a career accelerator that helps you reposition your existing experience, communicate business value, and step confidently into data-driven roles.

Work with me:

↳ Cohorts + 1:1 coaching → DM “HIRED”

↳ January program for mid/senior professionals → DM “JANUARY” for early access

↳ Guest workshops for teams and communities → DM “WORKSHOP”


I build analysts who think like product managers and communicate like consultants.

Peter Ndiforchu

Most candidates think they’re being evaluated on what they built.

They’re not.

When you explain a project, hiring managers are quietly assessing something much deeper.

Here’s what’s actually happening behind the scenes 👇

1️⃣ Can you think in business outcomes—or just tasks?
If your story starts with “I used SQL to…” and ends there, that’s a red flag.
They’re listening for why the project existed and what changed because of it.

2️⃣ Do you understand the problem—or just the solution?
Strong candidates explain the constraint, the tradeoff, and the decision.
Weak ones jump straight to the dashboard.

3️⃣ Can you simplify complexity?
If you can’t explain your project to a non-technical stakeholder in 60 seconds,
they assume you’ll struggle in real meetings.

4️⃣ Did you drive the work—or were you just assigned it?
Ownership shows up in language:

“I noticed…”

“I proposed…”

“I pushed back when…”


That’s leadership without the title.

5️⃣ Do you sound like someone they can put in front of executives?
Clarity. Structure. Calm confidence.
That’s what earns trust.

Here’s the uncomfortable truth:

Two candidates can build the same project.
Only one gets the offer—because they frame it differently.

Your projects don’t get you hired.
Your interpretation of them does.

If this resonated, next time you explain a project, ask yourself:
“Am I telling a story—or reciting a task list?”

Helping Non-Technical Professionals Build Data-Driven Careers That Travel Globally | Data Career Strategist | Senior Data Analyst @ Autodesk

What Data Hiring Managers Really Evaluate on Interviews

Most candidates know the STAR method.
Very few use it in a way that actually signals seniority in data roles.

Here’s how hiring managers experience it 👇

The STAR method for data roles (with a real example)

S — Situation
❌ Weak: “We didn’t have good dashboards.”
✅ Strong: “Sales leaders were making weekly decisions using spreadsheets pulled manually from three systems.”

Why this matters:
You’re showing business context, not just a messy data problem.

T — Task
❌ Weak: “I was asked to build a dashboard.”
✅ Strong: “I was responsible for creating a single source of truth that leadership could trust for forecasting.”

Why this matters:
This reframes you from executor to owner.

A — Action
❌ Weak: “I used SQL and Tableau.”
✅ Strong:
“I audited existing tables, wrote validation queries to reconcile numbers across sources, aligned definitions with Sales Ops, and designed a dashboard focused on decision-making—not vanity metrics.”

Why this matters:
Tools are expected. Judgment is not.

R — Result
❌ Weak: “They liked the dashboard.”
✅ Strong:
“Forecast variance dropped by 18%, manual reporting time was reduced by 6 hours per week, and the dashboard became the default reference in leadership meetings.”

Why this matters:
Impact beats effort. Every time.

The real mistake candidates make:
They treat STAR like a storytelling template.
Hiring managers hear it as a decision-making framework.

In data roles, STAR isn’t about what you built.
It’s about:

How you understood the business

How you made trade-offs

How your work changed behavior


If your STAR answers don’t reveal those signals,
you’re underselling yourself—no matter how strong your skills are.

📌 Save this before your next interview.

How I went from Cameroon → Canada → a US data role (without ‘local experience’).

When I moved, I assumed the biggest hurdle would be visas, credentials, or “local experience.”

It wasn’t.

The real question global recruiters were silently asking was:
“Who can vouch for this person when we can’t observe them directly?”

Here’s what made the difference on my journey 👇

1/ Relationships reduce hiring risk
Recruiters outside your country can’t see your work day-to-day. They rely on people they trust to validate your judgment and impact.
I focused on building genuine connections with:

Hiring managers

Senior data practitioners

People in the ecosystem who could confidently say,

 “I’ve worked with him. He delivers. I’d hire him again.”

2/ Visibility beats invisibility
I didn’t just rely on a resume. I made my work and thinking visible by:

Sharing real projects publicly

Discussing trade-offs and decisions

Engaging with communities in LinkedIn, Slack, and Discord

This gave recruiters context before even meeting me.

3/ Skills matter, but signals win
SQL, Python, Excel—all necessary. But what got me hired was showing:

Structured thinking

Clear communication of impact

Ability to navigate ambiguity

Coupled with endorsements from people who could vouch for me, geography became irrelevant.

Global hiring isn’t about ‘local experience’—it’s about trusted signals.

Ask yourself: “Who in this market can confidently vouch for my work?”

That’s the real game.

Cameroon → Canada → a US data role

The first time I realized hiring expectations weren’t universal…
I thought I had done everything right.

Strong technical skills.
Solid projects.
Clear results.

Then I walked into an interview outside my home market and something felt off.

They weren’t impressed by what tools I knew.
They kept probing how I worked.

Questions like:

“How did you influence stakeholders who didn’t agree with you?”

“What trade-offs did you make under pressure?”

“Who trusted your judgment and why?”


That’s when it clicked.

In some markets, hiring is about capability.
In others, it’s about credibility.
And in global roles, it’s about signal alignment.

Same resume.
Same experience.
Very different evaluation lens.

I stopped optimizing for: 
❌ tool checklists
❌ keyword stuffing
❌ “local experience” myths

And started optimizing for: 
✔ decision-making stories
✔ business judgment
✔ proof that someone would trust me in the room

That shift changed everything.

Global hiring isn’t harder.
It’s just calibrated differently.

If you’re preparing for roles beyond your home country, ask yourself: Are you proving skills…
or earning confidence?

Because globally, confidence hires before competence.

Here’s the uncomfortable truth most candidates don’t hear early enough:

Most data portfolios aren’t evaluated as “work.”
They’re evaluated as signals.

And most of them send the wrong ones.

I’ve reviewed a good number of portfolios as part of the hiring team.
The pattern is consistent:

What candidates show:
• Dashboards with no business question
• Kaggle-style analyses disconnected from real decisions
• Tools-heavy walkthroughs (“I used SQL, Python, Tableau…”)
• Perfect charts that solve imaginary problems

What hiring managers are actually scanning for:
• Can this person frame a messy business problem?
• Do they understand tradeoffs, not just techniques?
• Can they explain why something mattered — not just how they built it?
• Would I trust them to influence a decision with imperfect data?

Here’s the part most people miss:

A portfolio isn’t proof of skill.
It’s evidence of judgment.


That’s why two candidates can use the same dataset —
and only one gets a callback.

The one who: 
• Anchors the project to a real stakeholder decision
• Explains assumptions and constraints
• Shows what they didn’t do and why
• Communicates impact in plain business language

Strategic shift to make today:

Stop asking, “How do I show more tools?”
Start asking, “What decision does this help someone make?”

That question alone will put your portfolio ahead of 90% of applicants.

If you’re building a portfolio right now, build it like you already have the job — not like you’re trying to pass a course.

Growth @Rifa AI | Making AI work like your most reliable teammate | Previously @ Topmate | 30u30 IQ

AI isn’t replacing people. It’s replacing processes that slow them down.

At Rifa AI, we’re building intelligent agents that automate back-office conversations across chat, voice, email, and support without breaking ops.

The goal isn’t to sound human.
The goal is to get work done, with AI that can reason, act, and complete entire workflows across tools.

Our best users don’t even realize they’re talking to a bot.
That’s the point.

Before this, I led growth at Topmate and picked up a few hard truths:
 → Shiny doesn’t scale
 → Funnels lie
 → Speed breaks things, but it also builds them

Rifa is a tougher game. The stakes are higher.
You’re shipping into systems where mistakes cost trust, not just clicks.

That’s why I care about:
 → AI that’s functional, not performative
 → Growth that’s compound, not chaotic
 → Shipping like it’s a reflex, not an event

If you’re building at the intersection of AI, ops, and velocity, let’s chat.

Shubham Khoker

Most Voice AI adoption in US clinics flops because clinical ops teams expect it to fix the wrong mess.

You're drowning in front desk calls: missed appointments, endless insurance checks, follow-ups piling up. That’s 15+ hours a week that should’ve gone to patients, but instead disappear into phone calls and follow-ups.

So, clinical teams roll in Voice AI, thinking it'll sort the chaos, handle judgment calls, and nail every edge case.

When it can’t, they conclude “AI doesn’t work for us,” instead of “we gave it the wrong responsibilities.”

Voice AI works when you point it at right data:
– Owning high‑volume, repetitive conversations (“What time is my appointment?”, “Can I move it?”, “Do you take my plan?”) that don't need judgment
– Answering instantly and consistently, without getting tired or distracted
– Closing simple workflows end to end: verify → schedule/reschedule → confirm → notify → update systems
– Taking sustained pressure off front desks and ops teams so they’re not constantly in firefighting mode

But Voice AI doesn’t work when you point it at clinical judgment, medical advice, or sensitive conversations. It wasn't built for that.

Used right, Voice AI absorbs repetition so that humans can focus on patients.
That’s how clinic operations actually scale. Isn’t it?

I honestly thought the "Man vs AI" debate with Arnab Goswami was going to be a gimmick. 
I was wrong.


Seeing one of the loudest, most aggressive debaters in the country go head-to-head with Blue Machines AI for an hour was a massive pattern interrupt.

If you haven't seen the clips yet, you should.

Arnab did exactly what he’s famous for—he pushed for soul, for gut instinct, and for that "human spark." He basically tried to break the machine's logic by challenging its lack of lived experience.

At one point, it genuinely felt like he was trying to out-debate the silicon itself 😄

But the AI didn’t flinch. It didn't get flustered or talk over him. It just sat there, calm as anything, pulling patterns and data in real-time that no human brain could possibly track in the heat of a moment.

Watching that back-and-forth made me realize two things about the space we’re in:

1/ We used to say AI could never handle nuance or context. But when you see a system hold its own against one of the most aggressive debaters in the country, you realize the "ceiling" is much higher than we thought.

2/ It’s not about "Who wins"; the machine itself said it best—it’s a "bounded instrument," not a "final arbiter of truth." The goal is to use that massive processing power to clear the loop so humans can actually focus on the judgment calls that matter.

Also, if even Arnab can’t shout an AI into submission, that’s saying something 😄

This was a glimpse of how public, unscripted human-AI interaction is going to look more and more normal over the next decade.

So, what was your takeaway from that debate?

If your clinic saves 3 hours a day by automating calls, do you know what actually changes after that?
In most places, nothing.

The phone rings less, sure.
But when your clinical ops team intentionally decides where those saved hours go,

→ Your staff will walk patients through reschedules without sounding stressed.
→  You will finally stop pushing insurance edge cases to “later,” which means fewer angry callbacks the next day.
→  Or it simply gives time for your front desk staff to address consultation follow-ups with ease, instead of surviving hour to hour.

When that happens, you don’t need a dashboard to see the impact.
The waiting room feels calmer, with fewer things falling through the cracks, and people leave work feeling less exhausted.

Automation doesn’t create value on its own.

It just gives you time back.
If you don’t decide what that time is for, the system will quietly waste it.

So if you saved 3 hours tomorrow, where would you actually put them?

I’ve watched a lot of AI demos impress healthcare operations leaders.
And then quietly die once they hit a real clinic.

That’s because AI companies were solving for a good-to-have problem instead of a painful, need-immediate-resolution problem 

But if you spend actual time inside clinics, the signals are very different from what you’ll ever see in a sales demo.

And when you listen closely, the same three breakdowns come up again and again from front-desk teams and ops managers.

1/ 𝐂𝐥𝐢𝐧𝐢𝐜𝐬 𝐝𝐨𝐧’𝐭 𝐧𝐞𝐞𝐝 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞; 𝐭𝐡𝐞𝐲 𝐧𝐞𝐞𝐝 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐢𝐭𝐲.
A chatbot that answers questions but drops the context creates more work. If the system can’t carry state across scheduling, verification, and follow-ups, humans step back in, and adoption stalls.

2/ 𝐄𝐝𝐠𝐞 𝐜𝐚𝐬𝐞𝐬 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭, 𝐧𝐨𝐭 𝐭𝐡𝐞 𝐞𝐱𝐜𝐞𝐩𝐭𝐢𝐨𝐧.
In healthcare, every real workflow is full of overrides, callbacks, and “this is how we actually do it.” AI that works only when things go right won’t survive week two.

3/ 𝐓𝐫𝐮𝐬𝐭 𝐢𝐬 𝐞𝐚𝐫𝐧𝐞𝐝 𝐢𝐧 𝐬𝐢𝐥𝐞𝐧𝐜𝐞.
If staff have to double-check the AI, they stop using it, and if compliance feels bolted on, leadership slows rollout. And one moment of patient friction is often enough for the tool to quietly disappear.

That’s why so many AI tools don’t fail loudly.
They just… fade.

The ones that last are boring in the best way.

→ They reduce load without asking for attention.
→ They finish the work instead of creating another step.
→ They make the day feel slightly easier for the people on the ground.

Healthcare doesn’t need smarter demos.
It needs AI that can survive real-world patient flow and scheduling chaos.

And that bar is much higher than most people think

I’ve spoken to nearly 13 folks over the last few months and seen that insanely good AI products lose healthcare deals for a very boring reason.


It was because the founders fell in love with the demo and forgot about everything that came after.


Stop pitching to a clinic manager how well your AI can “chat.” It doesn’t move the needle.

Because he is already dealing with unpaid claims and constant staff turnover.

He doesn't care about chatting if it doesn’t reduce his backlog or make his day easier.

What actually matters here are three very unsexy things:

1/ 𝐓𝐡𝐞 𝐒𝐎𝐏 𝐆𝐚𝐩: You’re selling a tool, but they need a workflow. If your AI doesn’t slot perfectly into their existing SOPs, you’re just giving them more work.

2/ 𝐄𝐝𝐠𝐞 𝐂𝐚𝐬𝐞 𝐀𝐧𝐱𝐢𝐞𝐭𝐲: A shiny demo handles the "happy path" perfectly. But healthcare is 90% edge cases. If you can't explain exactly how your AI handles a patient with a heavy accent or an out-of-network referral, the trust is gone in seconds.

3/ 𝐑𝐞𝐯𝐞𝐧𝐮𝐞 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭: This is where most deals quietly die. If you don’t understand how your product affects billing or revenue flow, you’re not part of the conversation. Making something “faster” doesn’t help if it makes it harder to get paid.

So, want to lose a deal fast? Go ahead and talk about your AI model instead of their ops.

Instead, if you want to make them say YES, start talking about fewer callbacks, shorter days in AR, and less load on staff—care about their IRL workflow.

What’s the most "boring" operational detail that ended up being the make-or-break point in your last deal?

Patients don’t hate AI. They hate being misunderstood.

There’s a lot of fear that bringing Voice AI into healthcare kills “patient empathy.”

But let’s pause and be honest about what patients already deal with.
→ Is it empathetic to force a sick patient to listen to 15 minutes of hold music?
→ Is it empathetic to make them repeat their birthdate to three different people?
→ Is it empathetic to say "Please call back during business hours" when they are in pain at 8 PM?

That’s not empathy.
That’s just friction we’ve become accustomed to.

Real empathy in healthcare ops is about respecting time and context.
Most patients get frustrated because they are talking to a wall—a rigid IVR menu that doesn't know who they are or why they are calling.

So, when voice flows are designed with empathy, that wall disappears.
𝐓𝐡𝐞 𝐎𝐥𝐝 𝐖𝐚𝐲: "Press 1 for Scheduling." (The patient does the work).
𝐓𝐡𝐞 𝐍𝐞𝐰 𝐖𝐚𝐲: "Hi Sarah, I see you have an appointment on Tuesday. Are you calling to reschedule?" (The system does the work).

If your Voice AI understands people better than your old IVR, patients don’t complain.
They relax.

And in healthcare, that’s the bar.

People hate paperwork.
And you can’t fix human behavior with another form.


We keep trying to "digitize" healthcare by throwing more links and forms at patients.

Forms assume patients have time, clarity, and comfort with reading and typing.

But real life is messier, and people are busy, anxious, confused, or just unsure what a question in the form really means.

This is exactly why the phone call refuses to die. 

Real conversation is the only low-friction interface where a patient can say, "I’m not sure about my insurance, but I have a photo of the card," and actually get help.

So, if your "digital transformation" is just moving paper friction to a screen, you’ve just changed the medium —replaced a clipboard with a glass wall. 

But the patient is still on the other side, confused.

The goal should be to build systems that meet patients where they already are. 

That’s what makes the difference between an intake process that helps and one that quietly becomes a hurdle.

If your AI still needs a human to finish the job, you haven’t fixed the problem; you’ve just moved it around.

This is the biggest gap I’m seeing as AI “adoption” accelerates.

Especially in clinics and other ops-heavy environments, it often looks like this:
→ The bot answers a question, then tells the customer to call support anyway.
→ The AI “flags” an issue, but someone still has to log in, click through three screens, and actually fix it.
→ The assistant “captures” intent, but can’t push the update into your EHR, CRM, or billing system.

Although it looks like AI, it’s just an extra step inserted between the problem and the human who was going to solve it anyway.

That level of implementation might have passed in 2024.
But 2026 won’t be that forgiving.

Think about how work actually flows in a clinic:

A call comes in 
↳ it triggers a scheduling change
↳ which affects availability
↳ which requires a follow-up
↳ and needs confirmation
↳ then updates another system
↳ which notifies another person.

Today, humans manually glue this together.
Tomorrow, voice agents will.

Not as “AI assistants,” but as 𝐚𝐥𝐰𝐚𝐲𝐬-𝐨𝐧 𝐨𝐩𝐞𝐫𝐚𝐭𝐨𝐫𝐬 that understand context, intent, and downstream impact.

That’s how we’re thinking about voice at Rifa AI: 

As a coordination layer that actually finishes the job, instead of fancy interface or automation.

If the last couple of years were about proving AI could talk, the next few will be about proving it can work effectively.

So, what’s the most frustrating "loop" in your business that still requires a human to close it?

AI in healthcare ops fails the moment it tries to be a doctor.

That’s where most fear comes from.
People imagine AI diagnosing patients, making judgment calls, crossing lines it shouldn’t.

That’s not where it belongs.

Voice AI fits much earlier in the workflow.
Before the doctor, the nurse, and the judgment even enter the picture.

I’m talking about:
→ 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐒𝐜𝐡𝐞𝐝𝐮𝐥𝐢𝐧𝐠: Negotiating times between a patient’s life and a provider’s EHR.
→ 𝐋𝐨𝐨𝐩 𝐂𝐥𝐨𝐬𝐢𝐧𝐠: Reaching out the moment labs are ready to get that review call on the books.
→ 𝐈𝐧𝐭𝐚𝐤𝐞 𝐂𝐥𝐞𝐚𝐧𝐮𝐩: Gathering insurance data and consent before the patient even walks through your door.

These are operational problems. 

When Voice AI handles this layer cleanly, front desks stop firefighting, calls don’t pile up, patients actually show up on time, and staff aren’t exhausted.

But the moment the conversation moves from operations to care, the stakes change.

This is where precise judgement and empathy enter the picture, and that’s a line Voice AI shouldn’t cross.

AI shouldn't be diagnosing symptoms, delivering life-altering news, or "winging it" when a patient's case gets emotional. 

Patients want a system that works so well they get diagnosed quickly by a real doctor, not a "Digital Doctor."

So, healthcare doesn’t need AI pretending to think.
It needs AI to remove friction and to run the admin rails quietly in the background.

That boundary is the whole point.

If you’re an ops leader, what’s the one task you’ll keep "human-only" no matter how smart the tech gets?

Copilots are disappointing until you fix these 3 things.


Everyone's rushing to deploy them — voice agents, chat tools, AI assistants, and they sound brilliant in demos. But two weeks in, the ops team is frustrated.

Your team handed it a broken workflow. And then blame the AI for not working.

If your scheduling process is unclear, or handoffs between staff are messy, a copilot doesn't fix that. It just executes the confusion faster and more visibly.

I watched this happen in a clinic recently.

They deployed a voice agent for appointment scheduling. It works great for simple cases. 
Then someone calls with an edge case — out-of-network insurance, or a reschedule that needs manager approval. 

Now the AI freezes because staff don't know who owns it , and the patient is on hold with uncertainty.

That's not an AI failure; that's a workflow failure.

If you want copilots to work, stop obsessing over the tech and fix three things first:

1/ 𝐌𝐚𝐩 𝐭𝐡𝐞 𝐚𝐜𝐭𝐮𝐚𝐥 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰. Don't automate what you wish happened. Automate what actually happens — the callbacks, exceptions, and insurance clarifications. 

2/ 𝐃𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐡𝐚𝐧𝐝𝐨𝐟𝐟 𝐭𝐫𝐢𝐠𝐠𝐞𝐫. When does AI stop, and a human takes over? If that line is fuzzy, trust breaks.

3/ 𝐓𝐞𝐬𝐭 𝐭𝐡𝐞 𝐰𝐞𝐢𝐫𝐝 𝐬𝐭𝐮𝐟𝐟. Happy paths are easy. Real value shows up when the system handles different accents, all edge cases, and queries from confused patients.


Copilots amplify whatever you give them. Isn’t it?

The healthcare industry automates the loudest problem, not always the right one.


In most clinics, "automation" is treated like a generic vitamin—something you just take to feel better.

→ Front desk drowning in calls? Automate scheduling.
→ No-shows killing revenue? Automate reminders.

But in reality, automation is a high-stakes trade-off.
Every time you automate a workflow, you are making one of the two hard choices:
1. Is the problem worth solving perfectly?
2. Or am I okay ignoring it now?

If you automate front-desk calls just to "cut costs," your agenda is 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲. But if that bot frustrates a patient in pain, you’ve sacrificed 𝐏𝐚𝐭𝐢𝐞𝐧𝐭 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞. 

And instead of removing friction, you've just shifted the burden onto the person who actually needs care and support, 

So, before you automate any workflow, ask yourself: "𝐖𝐡𝐚𝐭 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐡𝐢𝐝𝐝𝐞𝐧 𝐚𝐠𝐞𝐧𝐝𝐚 𝐡𝐞𝐫𝐞?" 

Are you trying to hide from your patients to save a buck?
Or are you trying to be more available for the moments that actually require a human?

What’s the "loudest" problem you experience right now, and is it actually the one that needs to be automated?

230 million people are now asking ChatGPT health questions every week.
That’s roughly 3% of the human population turning to an LLM before a doctor.

Let that sink in.

Why? 

Because getting answers from the actual healthcare system is still hard, slow, or intimidating.

And today, if you and I have a concern, we want an answer NOW.

You don’t want to:
↳ Call the clinic.
↳ Hold for 15 minutes.
↳ Leave a voicemail.
↳ Wait 24 hours for a callback just to schedule an appointment for next Tuesday.

So, naturally, you go to ChatGPT because it responds in 5 seconds.

For clinic owners and operators, that's the bar now.
Your competitor isn't another provider, but the speed and accessibility that an LLM offers.

And if 230M people are willing to trust an LLM with their clinical symptoms (which is risky), they are more than ready to use Voice AI to handle their "boring" logistics—scheduling, follow-ups, and insurance.

We don't need to turn AI into a doctor. We need to use it to fix the access barriers that are driving patients away from doctors in the first place.

The demand is already here. Are you building the infrastructure to meet it, or are you still putting them on hold?

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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);}Peter Ndiforchu's LinkedIn Performance

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