RBC Capital Markets

- Lead the requirements gathering and creation of data quality rules for a new regulatory initiative (T+1).
- Manage 2 contract workers in drafting requirements, reviewing data lineage and creating be-spoke data quality rules across multiple quality 
 dimensions.
- Built a Tableau report all data quality controls created with meta-data on schema and other useful data elements to help reduce the time in the drafting of new data controls by 20%
- Built a Python script to Extract Deeply Nested JSON from S3 and Tranform into a CSV format for easy processing and analysis. (Avg size of data set is 600MB across 10 different instances with over 10 different critical data elements)

Senior Data Analyst

• Resolved a data quality issue of missing maturity dates on outstanding loans with a material impact of 122 billion USD.
• Collaborated with finance and regulatory reporting stakeholders to implement business requirements, resulting in a 25% increase in data quality and adherence to compliance guidelines as part of an enhanced enterprise data governance framework.
• Utilized Tableau to create an intuitive schema and data quality report, leading to a 35% enhancement in departmental rule design efficiency at RBC Capital Markets.
• Engineered data pipeline processes using Airflow and Python, reducing data processing times by 30% for faster insights delivery and improved efficiency in strategic decision-making.
• Collaborated with Regulatory Reporting and Finance IT stakeholders to enhance data quality and efficiency using DBT, resulting in a 25% reduction in data-related errors.
• Developed SQL data models on a cloud data warehouse and administered a Dremio data lake, handling over 10TB of data.

Data Analyst

Data Achievers

•  Worked with a variety of clients to support business intelligence reporting and data quality assurance.
•  Monitored product performance and customer satisfaction metrics to identify areas of improvement, resulting in a 40% increase in user engagement and a 25% increase in revenue.  
• Developed and maintained product roadmaps and feature backlogs, resulting in a 30% increase in team efficiency and a 20% increase in project success rate. 
• Created and maintained product dashboards and reports for job seekers, resulting in a 25% reduction in decision-making time and a 15% increase in data accuracy.

Data Scientist

TALENT™ (Ontario Tech Talent)

• Supported a self-paced, self-directed skills development experience (“Self Paced Experience”) for students looking to upskill in data analytics;
• Asynchronously engaged with learners (e.g. discussion forum moderation) via email, chat, or LMS like Canvas
• Provided 1:1 learner support every week; offered as virtual appointments booked through Calendly
• Effectively and efficiently verified customer skill acquisition by way of reviewing all artifacts and other work products presented for verification by learners against approved standards
• Recorded skills verification decisions in TALENT’s system of records

Skill Coach

• Demonstrated exceptional communication skills and exercised diligence in identifying delinquent loans, credit cards, credit lines, or mortgages, and referred clients to Collections.
• Established and maintained relationships with individual or business customers by offering value through every interaction.
• Identify fraudulent transactions at the first point of contact and work with AML and Fraud teams to investigate the transaction to ensure minimal client impact.
• Actioned solutions relevant to client banking needs, with a 30% action rate weekly, to support team goals.

Client Data Analyst

IKEA

- Enhanced order fulfillment Tableau dashboards, improving operational visibility and increasing efficiency by 15%. 
- Analyzed operational data to optimize processes, leading to a 25% improvement in service levels. 
- Collaborated with teams to implement data-driven strategies, reducing inventory errors by 20%

Operations Analyst Intern

Walmart Canada

•	Provided exceptional customer service to clients on the sales floor, ensuring that all their questions are answered promptly and efficiently.
•	Acted as the first point of contact to customers' challenges with finding merchandise on the sales floor.
•	Contributed to team success by supporting colleagues with their workload to effectively deliver on customer promises.
•	Built and maintained a strong relationship with colleagues to improve the quality of work and increase customer satisfaction.
•	Managed safety risk on the sales floor by proactively identifying and removing potential safety issues.

Fulfillment Associate

Fond Regional Pour La Promotion de la Sante du Centre (FRPSC)

• Implemented an analytical framework that reduced transportation costs by 20% by determining the best delivery route for urgent medicine delivery across geographical regions.
• Built dashboards using Tableau to help track Key Performance Indicators (KPIs) thereby increasing visibility on key business metrics.
• Conceptualized a new service delivery design which when implemented improved service levels by 25%.
• Analyzed 6 business processes to collect measurable data.
• Improved the existing demand forecasting software performance by modeling data sources to be compatible with schema.
• Maintained inventory ERP system by ensuring consistency between inventory on-hand and inventory in the system

Logistics Analyst

Cam-Green

• Planned and developed content strategies.
• Built strong relationships with project team members and stakeholders to ensure the achievement
of set goals.
• Assessed business opportunities and develop strategies to attract venture capital.
• Responsible for drafting a business plan for validating the business model.
• Provided consultation on planning and starting new businesses.
• Prepared business proposals and reports for presentation to stakeholders.
• Conceived and implemented a sales development program for the launch of the project which led
to a 110% achievement of the sales target.
• Developed media campaigns across social media outlets and campus bulletins which galvanized
support from key government officials.
• Worked closely with the project manager to ensure that sales and marketing efforts are consistent
with project goals.
• Complaint with established guidelines set by the University Board sponsoring entrepreneurial
initiatives.

Social Media Manager

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

I love to identify hidden business opportunities that lie in data and bring them to life with compelling visualizations.  I spend time setting clear goals, plans of action, and risk mitigation strategies for data analytics projects before even collecting data. 

I am a data analytics professional with a strong background in supply chain and marketing. I am passionate about data engineering. I love a good journey🚘, be it a journey in an RV truck across country, a learning journey📝 , or a customer journey with multiple touch points, or a data pipeline from source system to end user application. I am always curious to know what I can learn about a customer's journey to ensure it is seamless.😊

At RBC I have developed my customer-focused problem-solving mindset at the awareness stage and consideration stages of the business's marketing funnel. I worked closely with partners across the delivery channels (branch and telephone) to deliver exceptional customer service to clients to meet their banking needs. 

At RBC Capital Market, as a Senior Data Analyst, I own the process of ensuring data sent to report owners are fit for purpose (without data quality issues). Build data quality rules using SQL. Build Tableau dashboards to monitor data quality of critical data elements (CDE's) in production 

My experience working with cross-functional teams and creating a sense of synergy in the achievement of a common goal has proven to be a valuable value add. I helped in the resolution of data quality issues by proposing changes to how data is transformed both upstream and downstream.

I am always on the lookout for technologies that improve my workflow; I will take a week or so and dive deep into a data technology to see if it’s a good fit for a side project I'm working on and put aside if not. 

When I'm not working I am with my family making sure we are living every moment and making it count. You can also find me with my headsets listening to the next big rising artist of our generation and exploring new genres of music - I am a big lover of Afrobeats and Cameroonian cuisine 🇨🇲 

---
Current Status: Helping folks get their First Data Analyst roles - DM me to learn more

Peter Ndiforchu

I’ll be honest: the hardest skill I had to learn in data had nothing to do with data.

It wasn’t SQL.
It wasn’t Excel.
It wasn’t even storytelling with dashboards.

It was learning to ask the right questions.

Early on, I’d rush to analyze data — build visuals, crunch numbers, automate reports — only to realize I didn’t actually understand what problem I was solving.

That’s when it clicked:
👉 Great analysts don’t just answer questions.
They help clarify them.

Now, before I open any tool, I ask myself:

What decision will this data inform?

Who needs this insight, and why?

What does success really look like?


Once I learned that skill, everything changed.

🎯 Your turn:
What was the hardest skill you had to learn early in your data journey?

---
📌 Some of the hardest skills in data are the most basic skills. RSVP for our upcoming event on Saturday 08/10 to learn more about how to stand out with these skills in a competitive job market: https://luma.com/wphk134i

#52DaysDataChallenge

I help mid/senior level, non-technical professionals get their first data analyst role | Senior Data Analyst @Autodesk |Top rated Data Career Coach on topmate.io

You don’t need a data job to start thinking like an analyst.

A few years ago, I wasn’t a “Data Analyst.”
I was working in warehouse operations and customer service — managing shipments, handling client issues, and keeping things running smoothly.

Back then, I didn’t even know “data analytics” was a career path.
But I noticed something:

Every decision —
How much to stock, when to reorder, which customers to prioritize — was driven by data.

And that curiosity changed everything.

I started asking:
🧠 “What story is this data telling us?”
📊 “How can I use it to make smarter decisions?”

That’s when I began my transition — not by quitting overnight, but by learning step by step:
✅ Mastered Excel and dashboards
✅ Took online data courses after work
✅ Practiced by analyzing data from my own job
✅ Built small projects and shared them online

Slowly, I went from tracking numbers to telling stories with data.
And that shift opened doors to a brand-new career.

If you’re in a non-data role right now, remember that you’re not starting from zero.

You’re bringing problem-solving, people skills, and business insight that analysts use every day.

You just need to layer on the tools and mindset.

So tell me!

💬 What did you do before data?

#52DaysDataChallenge

Most data analysts didn’t take the straight road. They built their careers from the detours.

Every week in this challenge, I meet people who stepped into data through the side doors: 

→Retail floors
→Call centers
→Warehouses
→Operations teams
→Customer service desks
→Every job that required GRIT before SQL.

Some of the most resourceful and business-savvy analysts I know didn’t start with a technical background. 

They started with:

→People 
→Problems
→Messy processes
→High-pressure decisions. 

The mindset came first.
The data skills came later. 

Live #3 will spotlight the career journeys that rarely get told:

→The self-taught
→The late starters
→The career shifters
→The “I didn’t study this” crowd


If you have ever wondered whether your background is an advantage waiting to be reframed, this session will reveal exactly how that transformation happens.

I want your input:

👉 Who should I invite next
Tag someone with an unexpected path into data
Or share the story you want to see featured

Let’s turn this Live into the most relatable conversation of the challenge.

The real test of my data career is not SQL or dashboards.

It is explaining my job to my family around the world.

I’ll jump on a call excited to share something meaningful about a new insight or a successful analysis.

Before I get two sentences in, someone immediately says,
“Peter, you are good with computers. My WhatsApp is acting strange.”

I have explained my work so many times.
Analytics. Forecasting. Helping teams make decisions.

Yet the official family definition has settled at:
“Peter works with numbers and computers. Ask him.”

My mom once introduced me to someone at church with pure confidence.
“My son works in technology. If your phone is not behaving, he can fix it.”

My brother once asked if I could check his internet history from Canada.
He really believed I had that level of power.

And whenever the holidays come around, one relative always waits for the right moment.
“Peter, since you understand computers, my WiFi has been slow since 2019. Can you take a look?”

At this point, I save my explanations for work.
With family, I just help where I can and smile while doing it.

Family: “So you are I.T.?”
Me: “Not exactly”
Family: “Great. The remote stopped working.”

Anyone else living this experience? 😅

#52DaysDataChallenge

🎙️ Essential Data Skills for Career Switchers

When I first started learning data, I had no idea where to begin.
I spent months jumping between Excel tutorials, SQL videos, and random courses — but nothing connected.
It wasn’t until I understood which skills actually mattered that things finally started to click.

That’s why I’m hosting Live #2 of the #52DaysDataChallenge:
👉 “Essential Data Skills for Career Switchers”

In this session, I’ll share:
✅ The foundational data skills every career switcher should master first
✅ Real examples of how these skills show up in the workplace
✅ The biggest learning traps that waste time and delay progress
✅ A simple roadmap to go from confused beginner to confident analyst

👥 Tag someone who’s learning data right now!
Let’s make this journey easier — together.

Darshan Patel, Bukola Oyinlola and 89 other attendees

🎙️ Essential Data Skills for Career Switchers

I didn’t start in data.
And most people who end up in analytics… didn’t either.

When someone asks, “How do I transition into Data Analytics from my old job?”
I always think back to the moments that quietly shaped my own journey.

Most of us made the switch long before we realized we were “doing data.”
We were the ones fixing broken processes…
…tracking numbers when nobody asked…
…building little spreadsheets to make life easier for our team…
…spotting patterns in chaos that no one else noticed.

Your old job didn’t hold you back.
It trained you.

→Teachers already understand assessments and learning outcomes.
→Bankers understand risk, trends, and financial behavior.
→Retail workers understand customer patterns.
→Healthcare workers understand process flow and documentation.
→Engineers understand systems thinking.
→Marketers understand experimentation and attribution.

Every role comes with analytical gold—you just need to translate it.

Here’s the part most people miss:

You don’t transition by abandoning who you were.
You transition by repackaging what you already did.

If you’re trying to break in:
• Identify the analytical parts of your old role.
• Show how you improved something with numbers.
• Highlight moments where you solved problems with data, even informally.
• Build 2–3 small portfolio projects that mirror the jobs you’re applying for.
• Tell your story as someone who’s already been using data… just without the title.

The door into analytics isn’t as far as it looks.
You’ve likely been standing in front of it for years.

What did you do before data?
Share below — your story might inspire someone else.

#52DaysDataChallenge

💻 If Excel had feelings…

It would probably file an HR complaint by now. 😂

Because let’s be honest

We’ve all done some questionable things to our poor spreadsheets:

1️⃣ Renamed the same file 8 times: “Final.xlsx” → “Final2.xlsx” → “Final_ThisOne.xlsx” → “Final_FINAL.xlsx”
2️⃣ Used “Merge & Center” like it’s a design tool 🎨
3️⃣ Written a formula so long even Excel gave up: #NAME?
4️⃣ Pretended we knew why the pivot table disappeared 😅
5️⃣ Watched Excel freeze… and prayed for our unsaved work 🙏
6️⃣ Copied data from a PDF like we were performing surgery with a spoon 🥲

Yet through it all, Excel stays loyal.
No subscription drama. No attitude. Just pure spreadsheet magic.

If Excel could talk, what do you think it would say after seeing your last file? 👀

#52DaysDataChallenge

It wasn’t love at first sight.
At first, data looked complicated. 

Rows. 
Columns. 
Chaos.

My journey started far from spreadsheets, on retail floors, in warehouse stores, and on countless customer service calls.

I didn’t have dashboards or SQL then, just conversations.
But every interaction had something in common: patterns.

I began noticing how certain products sold faster at specific times.

How customers’ frustrations often pointed to deeper issues.

How one small change in process could ripple across an entire team.

Back then, I didn’t call it data.
I called it paying attention to detail.

Over time, I realized those observations were data.
And once I learned how to analyze them, everything changed.

Data stopped being numbers.
It became stories, truths, insights waiting to be uncovered.

That’s what made me fall in love with data.
Its power to give meaning to everyday moments.
To show patterns in chaos.
To help people see what they couldn’t before.

💬 Now I’m curious.
What made you fall in love with data?
Was it a project, a person, or that one “aha” moment when it all clicked?

Someone needs to hear your story today.

Day 17 #52DaysDataChallenge

The most underrated data skill isn’t SQL, Python, or dashboards.

It’s empathy.

I’ve spent years across retail floors, warehouses, and corporate boardrooms… and one pattern always stands out:

The analysts who rise the fastest aren’t just technically strong.

They care enough to understand the human on the other side of the data.

Empathy helps you:
• Ask sharper questions because you understand the real pain behind the request
• Build dashboards people actually use
• Translate complexity without making stakeholders feel small
• Design solutions that fit the workflow, not fight it
• Notice when a number feels “off” because you understand the story of the process

People think empathy is “soft.”
In reality, it’s a strategic advantage.

Every insight we deliver impacts someone’s job, decision, or customer.

When you remember that, your work becomes sharper, more relevant, and significantly more trusted.

The most underrated data skill: empathy.
Agree?

If you took away these 5 tools, my entire week would fall apart.

Here are the core platforms I rely on as a Senior Data Analyst — and why they matter for real business impact:

1. SQL
My decision engine.
This is where I validate narratives, challenge assumptions, and uncover the signals leaders don’t see yet. When the story is unclear, SQL brings the truth to the surface.

2. dbt Labs
Where chaos becomes structure.
Testing, documentation, lineage, and version control — this is how analytics scales beyond a single analyst. dbt turns insights into reliable systems the whole business can trust.

3. Power BI / Looker 
The boardroom translator.
Dashboards aren’t about charts. They’re about influence.
This is where I refine the message, highlight the “why it matters,” and drive decisions that move revenue, cost, or risk.

4. Cursor
The accelerator I didn’t know I needed.
Reusable SQL templates, automated documentation, workflow shortcuts — it’s helped me reclaim hours and reinvest them into deeper, strategic analysis.

5. Jira/Confluence by Atlassian
The operational glue.
Intake, alignment, prioritization, cross-functional visibility — without these, even the best insights get lost in the shuffle. These two keep the work moving and the team aligned.

These tools cover the full lifecycle:

Question → Data → Transformation → Story → Delivery.

Which one should I demo next?

#52DaysDataChallenge

5 Essential Data Analytics Workflow Tools 

Some days, being a Data Analyst feels like being a translator…between English and ‘Data.’

Stakeholder: “I want a dashboard to show user satisfaction — so we can see how pleased our users are with our product.”

Me: “Absolutely! I can build that. Are there any specific KPIs that interest you?”

Stakeholder: “You mean… metrics?”

Me: “Kind of! Metrics are the raw numbers like number of promoters or rate of positive feedback. KPIs are the key ones that tell us if we’re actually meeting our goals like NPS or CSAT.”

Stakeholder: “Got it. Then I’d like to see our NPS and CSAT.”

Me: “Perfect! Those are great KPIs for user satisfaction. What grain would you like to see on the dashboard visuals?”

Stakeholder: “Grain? Like... rice? We’re not an agricultural SaaS, you know.”

Me (internally): Deep breath. Smile. You’re the data whisperer, remember? 😅

Stakeholder: “Not sure what you mean, but I trust your expertise.”

Me: “Fantastic. I’ll take it from here.”

That conversation reminded me that Data Analysts are part translator, part therapist, part magician.

You’re not just building dashboards.
You’re bridging two worlds: business and data.

The best dashboards aren’t the flashiest,  they’re the ones your stakeholders actually understand.

So the next time someone says:

“We need a dashboard!”

Ask them:

→ What decision will this dashboard help you make?

→ Which KPIs matter most?

→ What level of detail (not rice 😄) do you want?

That’s how you turn confusion into clarity — and earn the words every analyst loves to hear:

 “I trust your expertise.” 🙌

#52DaysDataChallenge

Every data pro you admire once stood where you are — at the bottom of the same ladder.

When I first stepped into data analytics, I thought there were only two levels:
✅ “Beginner”
✅ “Expert”

Turns out… there’s an entire ladder in between.
And understanding it changed how I learned, applied, and grew.

Here’s how I now see the Data Career Ladder 👇

1️⃣ Data Curious — You’re exploring what data even is. Watching YouTube videos, reading posts, trying to understand what analysts do.

2️⃣ Data Learner — You’ve picked your tools (Excel, SQL, maybe Python). You’re following tutorials, taking courses, and building your foundation.

3️⃣ Data Doer — You’re applying skills on small projects. You clean messy spreadsheets, build dashboards, or analyze a dataset from Kaggle.

4️⃣ Data Storyteller — You connect insights to impact. You can explain “what the data means” in a way business people understand.

5️⃣ Data Strategist — You go beyond reports. You influence decisions, set up frameworks, and help define what to measure in the first place.

💡 Every step builds on the last — and you can’t skip steps.

When I recognized where I was on this ladder, I stopped comparing myself to others…
and started focusing on the next rung.

---
Your turn:
👉 Where are you on this ladder right now?
👉 What’s one skill helping you climb to the next level?

#52DaysDataChallenge

The Data Career Ladder

Sharing with my network! I've once interviewed for this role and they are a solid team I must say!

Célia Bru is a great data leader!

📈 We're hiring a Senior Data Analyst at KOHO! 📈 

My team needs someone rare: someone who lives at the intersection of deep business thinking and hands-on technical mastery.

You'll own data models in DBT, and create dashboards, but that's just the baseline. What really matters? You understand what drives our business forward and can draw clear connections between technical work and strategic business outcomes. 

The cherry on top: You would get to work with the most caring and fun team I know! ❤️ 

#hiring #dbt #dataanalytics

View the open to hiring details of Célia

The Power of Storytelling in Data Careers. 

This one skill has helped me grow my data analytics career in so many unexpected ways.

What's that one unexpected skill you didn't know would serve you well in your career?

#52DaysDataChallenge

Dashboards behave perfectly… until they sense a stakeholder within 10 feet.

Have you ever tried to convince a toddler to sit still for ONE photo?

That's my relationship with my dashboards before a stakeholder demo

You line up the filters.
You fix the labels.
You triple-check the KPIs.

And for a brief, glorious moment… everything behaves.
Until—
You hit Publish and the dashboard decides to express its true personality.

Suddenly:
→A number you didn’t touch is different.
→A filter you tested 12 times stops working.
→A chart decides today is the day it wants to be “creative.”

And then a stakeholder pops in like:
“Is the data… supposed to do that?”

If you’ve ever had this love–hate relationship, you already know the feeling.

Describe this with ONE emoji.
(I’m ready for the chaos.)

Who can relate?

#52DaysDataChallenge

Unconventional Paths Into Data Analytics 

Most analysts didn’t start in data.
They came from retail, banking, customer service, logistics, hospitality, and so many other places that taught them how to solve real problems long before they touched SQL.

This Live breaks down how people with unconventional backgrounds successfully pivot into analytics — and how your past experience might already give you an edge.

In this session, you’ll learn:
• How to spot “data moments” in your past roles
• Why non-traditional paths often create stronger analysts
• Real stories of pivots that worked
• Practical steps to position yourself for a data role

Date: November 22nd, 2025
Time: 12:15 PM EST

If you’re transitioning into data or guiding others on the journey, this one is for you.

Join us and bring your questions.

Rinu Francis, Bukola Oyinlola and 30 other attendees

Unconventional Paths Into Data Analytics 

If your insight doesn’t change a decision, it’s not an insight. It’s a recap.

How do you write insights that make execs listen?

Most analysts write insights to explain data.
The best analysts write insights to move decisions.

Here’s the pattern I’ve seen across product, finance, and capital markets:

1. Lead with the headline, not the homework.
Your first sentence should answer: “Why does this matter right now?”

2. Translate metrics into money, risk, or time.
Executives think in trade-offs and outcomes—speak their language.

3. Create tension before you reveal the punchline.
Show the gap between the current state and the opportunity.
Tension fuels attention.

4. Strip out every detail that doesn’t change the recommendation.
If it doesn’t move the decision, it doesn’t belong in the slide.

5. End with a clear call: decide, approve, prioritize, or pivot.
Your insight should point to an action—not another meeting.

This simple narrative shift consistently takes conversations from:
“Nice dashboard.”
to
“Okay—what do we do next?”

What’s your storytelling trick when presenting insights?

#52DaysDataChallenge

How Communicate Actionable Insights to Execs

Some days the hardest part of being an analyst isn’t the data… it’s the tools.

We all have that one platform that tests our patience, derails our flow, or sends us straight to Google with a desperate “why isn’t this working?”

So let’s find out:

Which tool frustrates you the most?

I’m running this poll because every frustration has a story behind it — the refresh that never finishes, the merge conflict that ruins your morning, the notebook that suddenly refuses to run the same cell it ran an hour ago.

Vote — then share the specific tool and one thing that really gets under your skin.

Your comment might help someone realize they’re not alone… or point them to a solution you wish you had sooner.

What’s the one tool that tests your patience the most?

#52DaysDataChallenge

Data without context is noise.
Context without curiosity is wasted.

Lately I’ve noticed something across teams I support:

People aren’t short on dashboards.
They’re short on situational awareness.

You can have perfect SQL, clean models, and beautifully designed visuals—
but if you don’t understand the “why behind the numbers,” your insights never move the room.

Executives don’t act because you showed a metric.
They act because you showed meaning.

The strongest analysts I know do three things exceptionally well:

1. Anchor every number to a business moment
A spike doesn’t matter until you explain what triggered it.

2. Pull in what’s not in the data
Market dynamics, customer sentiment, operational constraints, timing.

3. Translate insights into consequences
“If we don’t fix this, here’s what happens.”

That’s when noise becomes narrative.
And narrative becomes action.

What context are you missing lately?

Read any ERD and join tables like a pro — SQL fundamentals

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

I just saw Ankit's post about Topmate hitting profitability, and I couldn’t scroll past without smiling.

To the world, "profitability" is a financial metric. To me, having been in the trenches with Ankit and Dinesh, I know it’s a badge of pure grit.

I’ve said this before, and I’ll say it again: My time as Head of Growth at topmate.io was my real MBA.

It was the most intense classroom I’ve ever stepped into. Working with both of you gave me my earliest instincts for product, growth, and just... how to operate in absolute chaos without losing clarity.

A lot of the DNA I carry today as a founder comes straight from those months. The pace, the difficult decisions, and the relentless execution.

You don’t forget teams like that.

Seeing them hit profitability is a reminder of what consistent execution looks like when most people would have given up.

Huge congratulations to Ankit, Dinesh, and the entire team.

This win is well-earned. So proud to have been a small part of this journey.

Everyone’s busy calling themselves “AI-first.”
But your customers don’t care about it. 

When we first started pitching to healthcare clients, I thought leading with “AI-powered” would make their eyes light up. Instead, it usually made them lean back in their chairs, because for most healthcare stakeholders, AI-first sounds risky.

They’re not thinking about the model you used.
They’re thinking: what happens if it fails? Will it violate compliance? Leak data? Or give the wrong answer to a patient?

So now, I start those conversations differently.
About what we’ve done to make sure AI actually works when it’s supposed to.

Every voice agent we build passes through a simple checklist before going live:
→ Does it log every interaction for audit trails?
→ Does it know when to stop and escalate to a human?
→ Does it meet every compliance protocol, even if no one’s watching?

Once that foundation is clear, then the conversation about automation begins.

AI might get you in the door, but risk management keeps you in the room.
That’s what actually builds long-term trust.

I predict AI will vanish by 2030.
And I say that as someone who spends every single day building with it.

Not literally, but in how we talk about it. 
Five years ago, AI was a buzzword.
Today, it’s a feature.
Five years from now, it’ll just be invisible infrastructure.

No one will brag about “AI-first” anymore, just like no one says “internet-first” today. Because the best technology always disappears behind the experience.

When we deploy voice agents in hospitals, no patient ever says, “Wow, this AI bot is amazing.”

They just say,
“Finally… someone actually picked up.”
That’s the direction we’re headed. AI won’t live in headlines or product launches.

It’ll live inside workflows, quietly fixing things that humans don’t have the time or patience to.

So, here’s what I’m betting on:
𝟭/ 𝗔𝗜 𝘄𝗶𝗹𝗹 𝘀𝘁𝗼𝗽 𝘀𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝘀𝗺𝗮𝗿𝘁 𝗮𝗻𝗱 𝘀𝘁𝗮𝗿𝘁 𝗯𝗲𝗶𝗻𝗴 𝗱𝗲𝗽𝗲𝗻𝗱𝗮𝗯𝗹𝗲.
We’ve hit peak hype, and the next winners won’t be the flashiest demos; they’ll be the ones that never break in production. 

𝟮/ 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝗹𝗹 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝗺𝗼𝗮𝘁.
In healthcare, reliability is the foundation. The companies that bake guardrails in from day one will outlast the ones chasing speed.

𝟯/ 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 𝘄𝗶𝗹𝗹 𝗱𝗶𝘀𝗮𝗽𝗽𝗲𝗮𝗿.
Typing, clicking, searching; all of it will fade into natural conversations. Your voice will become the universal remote for how you access systems.

And maybe that’s the irony:
AI will be everywhere, precisely because it will feel like it’s nowhere.
By 2030, the best AI won’t feel like AI at all, it’ll just feel human.

If that happens, what’s one part of your workflow you hope disappears first?

Building should feel hard.
But it shouldn’t feel blurry.

Most people assume burnout happens when you work too much.
In my experience, it’s when you don’t know what you’re working towards anymore.

The chaos creeps in quietly: 
→ Too many priorities
→ Too many opinions
→ Too many half-baked “let’s just test it” plans

Suddenly your calendar’s full, your brain’s fried, and you're still not moving.
And the worst part?
You start confusing activity with progress.

I’m not anti-hustle.
I’m anti-chaos.

The only thing that’s ever pulled me out of a spiral wasn’t rest—it was clarity.
Clarity of: 
→ What problem actually needs solving
→ What to drop (even if it’s shiny)
→ What not to chase this quarter

Because burnout isn’t just about energy.
It’s about direction.

And sometimes the most productive thing you can do is… stop and think.
That’s not a “growth hack.”
That’s survival.

Tag a founder who needs to hear this.

The first time I had to let someone go, I didn’t sleep that night.

I kept replaying the conversation in my head; the silence after I said their name, the way they nodded slowly, the awkward gratitude at the end.

Nothing prepares you for that moment.
When we hired our first person at Rifa AI, it felt like validation, like someone believed in what we were building.

But when it doesn’t work out, it hits differently.
You start asking yourself if you could’ve coached better, communicated sooner, or just seen it coming.

That’s when I learned something that never makes it into startup books:
Company culture isn’t defined by how you celebrate wins. It’s defined by how you handle endings.

Hiring is about hope.
Firing, when done right, is about honesty.
Both take a piece of you.

And both, in their own way, make you a better builder — and maybe, a better human.

When you’re a non-technical founder building a technical product, impostor syndrome moves in next door, rent-free.


There have been times when the team was deep in conversation about inference speeds and vector databases, and I’d be there, nodding, jotting notes, then Googling half the terms later, only to realize I still didn’t get all of it.

However, I’ve learned that it’s part of the job.
I’m lucky to have a co-founder who lives and breathes the tech side (that’s Sameer). My role has always been to connect what we’re building to what the customer actually feels.

So I stopped pretending to understand everything and started asking the simplest questions again.

Every time I asked a “dumb” question, the conversation got clearer for everyone in the room. We stopped over-explaining to each other and started building something users could actually use.

Over time, it has become my strength. I stopped trying to sound technical and focused on what I can understand and build with.

Yeah, impostor syndrome still drops by sometimes, but I’ve learned to make peace with it.

If you’re a non-technical founder quietly sitting through a technical meeting, feeling like an outsider, you’re not behind.

You’re just seeing the problem through a different lens.
And sometimes, that’s exactly what your team needs.

Everyone says “talk to your customers.”
I’m taking that advice one notch higher, and I’ll pay you for your time.
No clickbait. Stay with me till the end.

As we explore the healthcare vertical at Rifa AI, I’ve realised something important:
You can’t automate an industry you don’t understand.
You can’t sit behind dashboards and guess how frontline workflows operate.

So before we ship anything in healthcare, I want to learn from the people who actually hold the system together. The ones who deal with chaos, patient load, insurance calls, and operational bottlenecks every single day.

That’s why I want to speak with:
→ Front desk staff at hospitals/clinics
→ Patient coordinators
→ Medical receptionists
→ Nurses who work closely with ops/patient flow

I’m not looking for confidential data. I’m looking for operational reality. I want to know where the friction is, what the "unwritten rules" of your workflow are, and what tools drive you crazy.

And yes, I’m happy to compensate for your time.
It could be a paid virtual meeting or a conversation over a coffee.

If you work in US healthcare operations and are open to a 30-minute paid chat (or know someone who fits), drop me a DM, tag them below, or share this post.

If you’re planning to build a startup, here’s the part that no MBA or mentor ever tells you.

Last week, when Anisha Jain and the HIVE team at the University of Chicago asked me to speak to their students, I said yes instantly.

Because I remembered what it felt like to be in their shoes.
Hungry to build something, unsure where to start, and confused by all the startup terms around “AI,” “product-market fit,” “fundraising,” and “the right time.”

Back then, I thought progress would be about finding the perfect idea or learning the right framework. But, once you get past the excitement and the theory, you hit the part where nothing looks linear, certainty disappears, and you have to build even when your confidence doesn’t show up with you.

That’s the phase that actually teaches you what matters, and here’s what I learned during that phase:
1. Start with a problem that keeps bothering you.
You don’t need to know how to code on Day 1. What you really need is a problem you can’t ignore... something you’re willing to think about even when no one’s watching.

2. A co-founder is someone who believes in the same future you see.
Skills can be hired, but what you can’t hire is someone who shows up on hard days with the same energy as you do.

3. Investors look for clarity more than big pitches.
If you can explain what you're building, who it helps, and why now is the right moment, the conversation changes instantly.

4. Your early job will teach you more about building a company than any “startup guide.”
For me, most of my instincts came from Topmate, the messy days, the mistakes, the adrenaline, the long nights. That’s where I learned how startups actually work.

None of this shows up in the pitch decks or the headline interviews. But it’s exactly what determines whether you ship something real or just talk about it.

So, if you’re at that stage: excited, overwhelmed, curious, questioning everything, that’s exactly where most real journeys begin.

P.S. What’s one lesson your founder journey has taught you?

I thought I knew what leadership was.
Then I became a founder.

Turns out, leading one branch of the company and leading a company are two completely different things.

When I was working as a Growth Head, it was about ownership within a structure.
We had a clear direction, a set goal, and a team to rally.

Someone had already built the walls of the house; my job was to optimize the rooms, fix the wiring, and ensure growth. There was stability, even in chaos.

At Rifa, I quickly realized leadership from the ground up is a whole different game.

There is no existing structure. You are literally building the walls while simultaneously deciding what kind of house you even want to live in.

The shift is profound, and honestly, sometimes scary.
You stop being a functional head and become the emotional backbone of the entire operation. Every product pivot, every client bet, every tough hiring call has effects that you simply cannot delegate away.
 
They need someone who can admit, "Yes, this is fog," and still be the first one to take a step forward.

That's the real leadership muscle I had to build, and that’s the part most leadership books skip:

The silence that comes with being responsible for everything, and the humility it takes to show up every day, even when you don’t have all the answers.

If you've made the jump from employee to founder, what surprised you most about leadership?

People keep asking me, "When will your AI be smart enough to replace a nurse?"

I tell them that's the wrong question.

The right question is, "How can our AI be reliable enough to handle the 5 hours of administrative work that burns a nurse out every single day?"

The goal isn't to replace our heroes, it's to give them room to be human. Isn’t it?

You don’t need a dev team or a $50K budget to build a voice agent anymore.
Hard to believe, but that’s where we are now.

I’ve seen companies spend months wiring together APIs, vendors, and compliance checks just to make a phone line “talk.” By the time it goes live, the use case has already changed.

That’s the gap we’re closing with Rifa.
Building automation shouldn’t feel like building another product; it should feel like setting one up.

With our platform, you can design, test, and deploy a compliant AI voice agent in minutes. 
You don’t need to know coding. 
You can simply drag, drop, and go live on any phone number.

And it doesn’t stop when the call ends.

→ Your CRM updates automatically.
→ Follow-ups get scheduled.
→ Issues get flagged without anyone touching a dashboard.

Because real automation shouldn’t add another layer of complexity, it should remove it. That’s what we’re betting on at Rifa: making AI powerful and practical for the people who actually use it.

If building automation were this simple, what’s the first workflow you’d hand off?

We are building an AI Voice Agent to automate phone calls. But to get our first 10 customers, I had to make hundreds of phone calls myself.

That’s the irony of building in AI today. 
Everyone wants to automate everything, including product development and GTM.

But here’s the reality I’ve seen time and again, from leading growth to now building Rifa…
AI can automate workflows, but can’t automate trust.

And when you are selling into industries where a single mistake has consequences— healthcare, finance, compliance-heavy ops—that difference matters.

These buyers aren't looking for a "cool AI tool." 
They're worried because the risk surface has expanded:

_ "What if the response is wrong?"
– “Who is liable for the model’s error?”
– “How do I explain this to an auditor?”
– “What does this break in my existing workflow?”
– “What happens if the model behaves unpredictably?”

These aren’t objections you solve with a funnel, or a case study, or an automated sequence.
These are objections that need context, interpretation, and reassurance. 

And this is where old-school GTM suddenly becomes a superpower again because it helps buyers process risk at a human pace.

That’s why the AI companies that are winning right now have strong trust-building mechanisms:
• High-context conversations
• Non-scalable onboarding
• Long-form demos
• Domain fluency
• Founder-led selling in v1
• Field intelligence from real calls

Because GTM in AI is about risk translation before efficiency or growth. 

If your product is AI-first, your early GTM must be risk-first. 
And right now, risk is still interpreted by humans, not workflows.

See, AI will eventually sell AI.
We’ll get there.

But we're in the messy middle, where technology has leapfrogged, but buyer psychology hasn’t caught up.

The biggest mistake you’re making as a founder is trying to make your GTM move as fast as your tech. While in reality, it should match the pace of your buyer’s fear.

Don't let the hype trick you into hiding behind your screen.

P.S. Are you also seeing traditional channels outperform the automated ones right now?

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