Ghazenfer Mansoor - Beyond the Download: Engineering Trust and Long-Term User Retention

In 3, 2, 1.

Welcome back to another episode of Becoming Preferred, the podcast where we dive deep into the strategies that help you level up your game and stay relevant in an ever shifting marketplace.

I'm happy to introduce our guest today, a man who sits at the intersection of eye level software innovation and human centric design.

Ghazanfur Mansoor is the CEO of Technology Rivers, a powerhouse firm known for building everything from HIPAA compliant healthcare tech to cutting edge AI solutions.

But Guzenfur doesn't just build software, he understands the psychology of why we use it.

He is the author of beyond the Download where he breaks down the alchemy of creating mobile apps that people actually love and share.

Beyond his technical expertise, he's a fellow storyteller as the host of the Lessons from the Leap podcast, uncovering the raw failures and bold breakthroughs that define the entrepreneurial journey.

Whether you're looking to scale your startup, automate your processes, or simply build a brand that resonates, you're going to want to take notes.

Join me for my conversation with Ghazanfur Mansour.

Well, hi Ghazanfur, welcome to the program.

We're delighted to have you.

Thanks Michael.

Thanks for having me.

An interesting topic today and we're going to talk about it because a lot of our listeners want to understand today's technologies.

They're entrepreneurs, they're business professionals.

They how do I take my business to the next level?

How do I grow my business in today's competitive marketplace?

And you've definitely got some experience in there.

But let's go back in, in time here you're back in high school, maybe going to school and you've got this path in front of you and you go where am I going to go?

How did Gosan for become guns and for how did you decide to go down this road and end up where you are?

So I have a computer science background.

I have a master's degree in computer science and I worked as an early engineer with two startups.

So and I got involved in mobile the very early stage around 2000 when it was just those flip phones.

So along the way when I work with the startups obviously that gives you more motivation towards working on your own ideas.

So that gave me a push towards that and every engineer like to build things and I started a recruitment software startup and that's how I got into the entrepreneurial.

So after that then I started this business in 2015 helping businesses and the product development, building it the right way the first time.

How have you seen the technologies change Like I remember back when I took computer science, it was FORTRAN and cobol.

I hate to date myself.

And we just come off of punch cards, just go.

So I've seen that migration, then C and C plus and all the developing.

And then I remember there was a whole period of time where hey, I'm going to be a coder in my next life.

And that's the way to go, that's the future.

And that seemed to serve a purpose for a while, but now we've got AI and AI tools coming.

How do you see the landscape?

How's it evolved just in your time, you know, as a professional working in.

When it comes to tech, yeah, I've.

Seen the COBOL and Visual Basic time, definitely I can resonate that technology is changing with amazing speed.

I think it's one of those cases where it never stopped to amaze us.

But this AI change is the biggest of all.

So when I started my career earlier, it was an early web time.

And then obviously everybody, even building a website was a big thing.

And then after that, mobile became a pretty big thing.

And now AI is changing everything.

So definitely it's a bigger chain, but I would say it's much bigger than everything else.

That's a big disruption in the industry because every other thing is didn't create as scariness as this one.

So even though there are a lot more opportunity, but you do see that in general public you may hear a lot of that fear as well that AI is taking away all those jobs.

That was not the case before.

Previously it was okay.

Now all these opportunities are coming and it's going to create more jobs.

So yeah, it always has.

You call it a disruption.

I, I think it's the fourth great disruption that we've had.

The industrial age, the technological age, that where we live today.

And you're right, I think people who don't embrace the technologies will always lose to someone who does embrace them.

But it's just changing and leveling up our work.

It's helping with that productivity, in my opinion.

I remember websites, we needed coders to do websites.

Then we had the what you see what you get.

We had drag and drop.

You could build your own website and those came into play.

And now even when it comes to coding, I'm not a coder, but I've had AI explain coding to me, explain it to me like I'm an 8 year old now, a 12 year old now, explain it like a 20 year old, all within an hour.

And it gives me a good sense of how this works or how to find this inside the code.

So I think to your point, it can be disruptive if we don't evolve.

So we got to keep evolving.

And as business owners, that's always the big challenge.

Where do we go and how do we do it?

Now, in your latest book, beyond the Download, you emphasize that getting, say an app on a phone is only half the battle for a business leader.

What's the single biggest reason users abandon a product, say within the first 30 days?

They look at it, they're interested in it, they.

But then they come off of it.

What are some of the issues and challenges that business owners face there?

I think the basics are still the same.

The foundational problem is like, are you solving a customer's pain?

I always give example.

We both have seen the Craig List time.

Was it a good user experience?

No.

But how much traffic you had, like every time you go there because they were solving one specific pain.

When you solve pain, people will come back and they will download, they'll keep using.

And some of those apps that we are using on a daily basis, they are solving some problem, they're providing some value to the users and those users keep coming back.

So yeah, that's the gist of it.

What problem you are solving.

And then the second part that changed after that Craig List and doing the Apple time is the user experience and design.

In the past, the importance was user experience and simplicity was not as important.

You just solve some problem.

Now you don't have to just solve, but you also solve it in a really simple way.

So if the user have to think what to do on this app, it's late, you're not going to get them back again.

So your app has to be remarkable.

It has to solve a problem, it has to provide a value, it has to have an amazing user experience and design.

It's not the time when you have to watch the YouTube tutorial to learn how to use an app, which surprisingly, a lot of people are still doing it.

Yeah, you focus on that user success.

And a lot of companies for years it was all just about user acquisition, how many acquisition.

That's where the value came from.

But then your churn rates were so significant because people weren't implementing it or internalizing it.

So the abandonment usually happens because of that time to value.

If they don't experience the win within the first few minutes or an easy use, they're moving, they're moving on to something that's even easier.

Which brings us to where we are today for how do we Create strategies using today's technologies of AI.

For instance, how does AI strategy work for non tech firms?

So many of our listeners run service based businesses.

How should a non technical entrepreneur decide between say buying an off the shelf AI tool or investing in a custom AI powered solution?

So it's a tricky situation.

I would say not every company have the same DNA.

Every company is different.

You have to look at specific use case, specific problem you are solving.

There are some, I would say agents, some solution that are customized, those are commoditized, it does make sense to license them, use them.

You don't want to be building everything.

But at the same time that's also the differentiator in every service business.

So if you're using the same system that your competition is using, that means you're not really how are you competing?

Are you competing on people?

Are you competing on pricing?

Are you competing on speed?

Are you competing on better customer service?

And all those could easily, easily be replicated by your competitor and they can beat you, especially the one with more money.

So how do you differentiate?

That's where the technology can come in.

So technology can make a big difference and help you 10x your business not only bring efficiency but also increase the value of the business as you're going to sell it.

In the long run, it's going to make a huge difference.

So when I say technology, what does that mean?

It's a proprietary technology we're talking about.

But at the same time that doesn't mean you replace your regular scheduling or regular routine software with custom.

When we say technology, that means building a very specific workflow that are unique to your business.

It could be integrations with existing system, whether it's scheduling, whether it's CRM, cms, whatever are the different tools you're using, Maybe integration with your payrolls and all of those things.

And you need to have a dashboard, you need to have all those things in that help you move the needle in your business.

So your competitor may be able to copy the other tools that people are using, but they won't be able to copy the unique workflows that you have.

So we talk about when in the service business growth you want to look at what are the bottlenecks in your business, what are the manual workflows.

Some of those could be replaced by existing tools, some may need to be created, some need integration.

And every business has unique ways of doing things.

If you can optimize that, that would make a difference.

Because now you can do things much faster than your competition.

Now your team can identify the problem, you can identify the risk.

Instead of learning about losing a customer if you get the red flags ahead of time because you have a system that monitors.

So there's no one case I can specify, but it's just unique.

You have to look at your specific workflow processes and then come up with a strategy to implement those.

But it could be again, it could be starting out with just one agent and then maybe adding into multiple agents or workflows.

Well, and that's good advice.

I think.

You know the old 8020 rule.

I think if an off the shelf tool solves 80% of your problem, great.

There's some good project managers, there's some good programs.

You can look at the workflows.

If you're competitive advantage, the thing that makes you preferred lies in that remaining 20%.

Then you got to build your own iPad and build your own system.

And with the agents now that seems to be also making that a little easier.

It's we had bots for a while and bots are still prevalent.

We see a lot of them, but people are getting bot fatigue.

I think they say a bot the bot where an agent is kind of like really an advanced bot, really.

But we just call it something else I think.

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And now back to my conversation with Ghazanfur Mansoor.

Let's talk about trust level.

One of the things that areas that you specialize in is HIPAA level trust standards.

So you specialize in HIPAA compliant healthcare tech as for those that aren't in medical fields, what can entrepreneurs learn from healthcare about building digital trust and data security.

So any application that has a phi personal health information it has to follow certain HIPAA regulations.

So that includes obviously the physical controls as well as the software part.

So we'll only talk about the software part.

So how do you exchange your data?

Who access the data?

Are the users authorized to access the data, what time they access so that later on you could track the history and all of the audit logging.

There are a whole lot of different rules that you have to follow when it comes to hipaa.

And in reality even though the government regulations are on that healthcare data, that really applies to many of those businesses.

My business has a personal sensitive data, yes, it's not a phi data but all of the employees information is also as critical as my healthcare data.

In fact that even many times more critical.

So it does require the compliance so all the data.

So I think as you we are building these applications, we build that process and structures of how to build those applying those in a similar other businesses even though they may not necessarily be officially HIPAA compliant, but making it compliant makes it much easier and build the trust and give the confidence.

Yeah, I think security ought to be a feature, not a footnote, you know, not.

Oh yeah, by the way we're also this, hey, by the way we're focused on your security and feature it, highlight it, amplify it, it gives the user helps to build that trust.

Hey, my data is secure because we trust our banks, we trust our systems, we sometimes give up a lot of information.

So I think having that HIPAA level security in our programs or apps I think become critical.

Let's talk about scaling without breaking your company technology rivers.

You help startups scale faster.

So when a business experiences sudden growth spurt, where does technology, where does it usually break first and how can we future proof for it?

So I think the foundation is the key.

As we are building these applications, if we are just ductating the systems, that's where the problem comes.

Because now the foundation was not set from day one.

In today's time with AI, with the cloud, all of those infrastructure is cheaper, it's easier.

Building scalable tech is much easier than it used to be.

So in fact in our experiences, if you're not thinking about the scalability from day one, you're actually making it slower because that means you have that infrastructure, you have your pipelines and all of that infrastructure, you just automate a lot of those things that you build it Once and then you repeat that.

So a lot of those scalability things are much easier to implement because if you're not doing it, then you are doing it more like a traditional way.

You're taking probably more time anyway.

But that's a big misconception.

We have seen when people are building these, okay, we just need an MVP and we don't need any of this.

But in reality, you're re spending a lot of time.

Remember I talked earlier about when I got into this build, and one of the goal was to build the product the first time, the right way.

One of the things we have noticed that a lot of people coming from different backgrounds building these products that are never finishing because you build something and then keep updating, patching, ductating, and you're focused on now fixing all those things for every new feature you're building, it's taking much longer.

So you're running out of money just because your tech is taking so much time, obviously, along with many other things.

So the focus has to be building the product the right way the first time so that you can focus on growing your business rather than just fixing and patching the technology all the time.

Right.

It's very tough.

I believe you teach that 1 in 10,000 become a true success.

95% Of apps fail within the first year.

So architecting, designing, how does an entrepreneur avoid that scope creep, if you will, where they have an idea and it takes forever and ever to get it on.

And basically they run out of financial Runway in order to get that app to market.

Because sometimes it might be, hey, let's get it out there and then keep evolving it and going through.

Is there the right approach and how long on average should an app taken?

I realize that based on the complexity, it could vary.

But what kinds of things are you seeing?

You know, from concept to we're rolling it out and we're implementing it.

What kind of time frames should someone expect?

The time frame used to be different than now.

So I'll talk about the process first.

So I think the most important part is really narrowing the scope of the MVP to building something that your customer need.

I think one of the bigger challenges, the feature race.

People start building too many things and want to have a perfect product before you can launch.

So that perfect product, if you're taking a year, two year to just build it, and then you realize nobody's using it, that's a big risk.

So we recommend you build a very tiny mvp, even if that means one feature or two feature, one use case, two Use cases that your customers really need.

Out of the 50 features you want to build, start with one.

Put it out in front of the customer.

It's like you build the foundation, you have the sketch and then you gradually scale.

One of the examples I gave, like if you're building a house and your vision is a mansion, you want to start with a studio and then gradually expand.

When I say studio, that means it's a livable space that has a bathroom, kitchen.

Right.

It's not a room that's not usable.

So make a usable MVP that may have just the login and two other things, or may not even have a login, but it's doing something for your customers.

And then you keep giving them features every week, every two weeks.

And you're exciting your customers as well.

Because now you're releasing every week and they are seeing that something happening rather than you build something, nothing happened for six months.

You lost trust in your customer because they don't know when the next set is coming.

Because you're going to make a big patch and then this another whole learning the data migration issues, all of those.

So you want to focus on building just the tiny part.

That's one thing.

In the past we always recommend the MVP should not be more than two to three months.

Build it in two testing, get feedback.

Well, it could be a bigger one, but that you can still even have a tiny version that you could test with your users.

It could be internal mvp, but it has to be long.

If it's not deployed, it's not launched, your users cannot see it.

Even if it's internal, limited users, you can see some problems potentially down the road.

Now with AI, even those things have improved.

Now with wipe coding, now we can even do a lot more faster.

So one of the things we recommend, use one of these white coding tool.

Whether it's replied lovable v0 there are many create an MVP like rather than in the past, as you were clarifying the scope, you were creating those sketches.

Now use the wipe coding for initial proof of concept and that could be done really quickly in hours and days.

And then you gradually define, let's say in a week to two week time frame, you refine your flows in the wipe coding.

Now the next job is to take that and convert it into a real working mvp.

So that's the flow.

We recommend now building the initial MVP with the wide coding and the first two to three weeks and then expand and make it production ready or make it HIPAA compliant and make it live in four to six Weeks.

But then that four to six week is much bigger than what it used to be.

The three month or six month time.

Frame for mvp, which stands for a minimum viable product for those who aren't focused on that.

Is there a danger of being too minimal?

Like when does good enough actually hurt a professional brand?

It really depends on a specific problem we are trying to solve.

Let's say if you are building a product that is you think that you're creating a replacement of whatever, let's say a bigger tool, maybe a salesforce or HubSpot or any of those.

Now what is your selling point?

So are you competing on oh, I have five more features.

Even in that case we say, well, you still have some differentiators.

Why don't you build something just on that differentiator?

Just line those and gradually and maybe start with integrating with existing one.

Because change is difficult.

It's not easy for existing users of other products to just switch to your product.

It has to solve something specific that others are learning.

What if you bring something that just integrates with other so that it's complementary for them and then gradually you add other features into those smaller product and expand.

But yeah, that's really a risk as well because now again it also depends on your customers or the type of product.

So you have to figure out who your customers are, are they willing to buy what you are building.

So if we cannot narrow those into a smaller one, that means our problem is complicated.

That doesn't mean it won't be solved easily.

But you're making a bigger problem.

So anything that is bigger, obviously that becomes a bigger project than that means a lot more stakeholders, a lot of other sectors get involved.

The smaller you break your problem into and solve it, then that small problem is much easier to solve.

Are there different types of genres of I want to use the word genre or types or categories of apps that have a higher chance of success versus those who don't.

You know, I've got tons of apps on my phones and every once in a while I'm getting rid of some and then I like to chase them.

If I see something new that's focused, I like those as well.

But are you seeing somewhere, hey, we got too many of these.

We don't need these anymore.

But this is open territory.

This is a good field.

This is a good area where an entrepreneur might want to add an application.

So we don't look at it from an app perspective.

This is not really just the app problem.

This is in general any business idea problem.

Right?

So you have to identify the pain, what people are looking for.

And if we know the examples of all these, let's say social networks and all of those.

TikTok is solving one problem, Facebook is solving another one.

They're all different.

And there are a lot of other competitive products as well.

Not everybody got their adoption.

So it's about what is missing in the industry, what is missing in that specific domain and then to multiple categories because there are some that are internal users.

Let's say you build an app that may work out really just for the specific types of user.

Let's say you're a patient, so it's just a necessity.

You have to use it because now you're selling it to your whatever hospitals or providers versus those are consumer ones, hard to scale.

But once you get that vitality, then it can go crazy as well.

So it really depends on a specific problem you're trying to solve and what is your approach of solving that problem.

And sometimes app is just a complimentary of what you're doing versus app is a necessity, let's say, to talk about Uber, Lyft, those kind without the app.

Obviously it's a real time experience.

We are tracking your ride and everything.

You need an app.

In some cases it's a business, but app is a good to have.

Some people will have it, some not because they can still do it through the web or through other ways.

Is there for entrepreneurs who are looking at perhaps adding an app or expanding and trying to scale their existing environment, Is there a hey, on the low side like how do we budget for those types of things?

Is there a good rule of thumb for budgeting?

And I realize how long is a piece of string?

How long do you want it to be?

It's like a marketing campaign or an ad campaign.

And this is I think the thing that creates that blind spot for people where they get nervous about it because they've heard the horror stories, you know, millions versus tens of thousands.

For someone to even consider, you know, if there's a range and hey, they should consider this on the low side and then it can go anywhere.

But what can usually solve a problem because it's not like it used to be.

I don't believe it takes millions and millions to do it unless you're building something massively complex.

Is there any guidelines that we should be following?

We have built apps that were, I mean, in today's time you can build it maybe like as low as 10,000 and maybe few hundred thousand dollars.

Again, it depends on what is included.

And Sometimes the cost is not the first time cost.

The cost may be over time because you're building and growing.

Because as we talk about, we gave example Uber, Lyft and some of DoorDash, all of those.

It's not about just the app, it's about the operations that runs behind it.

Because now you're servicing your customers as well.

So building an app that is not in production is one price, but once it goes live, then you have to build the supporting software as well.

Maybe admin dashboard, maybe as the user is using, getting some kind of feedback.

Now you're tracking analytics, there are a whole lot of other things that you're doing so to support that.

So then your cost is more of managing than building the new features.

So the Start MVP could be really cheap.

Yeah, bring that.

On your podcast, Lessons from the Leap, you talk about the bold decisions entrepreneurs make.

What is one leap you see most business owners are currently afraid to take, but probably should?

Well, that's the hard one.

Everybody is trying to get into the AI race, which is exciting, but at the same time some people are scared to take that leap.

But on our lessons from the leap, most people are the ones who are actually taking those leaps because those are the ones that we are talking.

The ones that are afraid are not much on the podcast yet.

Yeah, I think from, I mean if I talk about it from the lessons from the leap, it's mostly the AI race that people are taking.

It's just a different approach people are taking.

Well, and I think you do talk about niche specialization as well.

Many entrepreneurs fear that narrowing their focus kind of limits their market.

But in reality it can actually you avoid being a commodity when you start to focus and you start to niche down on it.

But let's talk about AI because I think that's an important characteristic.

I believe there's the human side of AI.

So as an expert in AI powered solutions, where do you believe the human touch is most irreplaceable in the modern business journey?

So AI is still, obviously, I would say still at the early stage.

Even though it's great, it's in many cases perfect.

But at the same time it does need human involvement.

There's a lot of hallucination, a lot of mistakes.

So you do need human to verify those things before, especially on a critical cases.

The way people are using AI, it's also another big challenge because if you're not providing the context properly, if you're not articulating your problem properly, you have to work on it to get to the Result that you want, it takes time for that.

So there's a lot of training involved in that.

Like as people are using it, it's learning and improving that as well.

And we've got two types.

Like, I'm in agreement with you.

I think AI is good for information.

There's generative and then there's agentic.

We've had agentic since, you know, 1967 was as far back as I can go, where we've had a Gentex where we get an agent to do it versus interacting with something like a chat GPT which we talk about.

I believe that humans, for instance, we need them for empathy, we need them for interpretation, we need them for insights, we need them for accountability.

AI can't be accountable.

So I think as a society, we have to evolve our eq, all right, so that we can handle this high level of IQ that's being generated for us and take care of it.

So it needs that balance.

And I think that's really what protects humans in the age of AI and so we can thrive and survive is build our human skills.

You know, if I'm talking to a nurse, if I'm getting a procedure, I want that nurse to hold my hand and talk to me.

If I'm going through something that might be fearful, if, you know, I was talking to a radiologist who now does.

Used to take them four to five hours to diagnose, knows charts can now do four or five people within that same time frame where the AI can analyze it.

99% Accuracy.

But I don't want a computer explaining my chart.

I want that radiologist talking to me.

So it seems like everything's designed to level up our productivity, but we should really be focused on to stay competitive, to stay distinctive and unique is focus and enhance our human skills.

Would you agree with that?

Oh, absolutely, absolutely.

And you gave example of healthcare.

It's a majority of our work is healthcare and we have built those kind of applications.

So definitely that's a human part that does come in.

And as you give a good example of the radiology.

So rather than spending too much time on figuring it out, now with all the data you're getting the results now you can focus on communicating with the, with the patient or solving other problems because now you're not spending as much time.

So technology is there, AI is there to support you, but the human part does come in.

So in our applications, for example, we worked on multiple predictive, personal predictive health applications where you load your blood work and other data and gives you predictions.

So obviously that's also a regulatory risk as well.

You can't just give the prediction so because then who's liable for that?

Who's accountable for that?

But then you're building a system on top so that, okay, it is trained based on how the doctors were previously recommending.

So it, it is suggesting accordingly.

But then there's a do doctors review?

They will have all of those insights and then maybe changing those.

But now it's giving a lot easier for Dr. To review all of that information and quickly add their notes into it and in some cases converting those recommendations into the format that is more human format.

And then again there's a human part of it where somebody's reviewing.

So by the way, even now in one of our applications, we're also adding a podcast option where AI would not only do the analysis, but also you can generate the scripts and two doctors are talking discussing your specific report.

So that's another aspect.

But again there's still not a human part.

So maybe once as a patient you listen to that, then Dr. Can provide additional feedback on top of that afterward.

But then rather than explaining for one hour now you already got that podcast summary, now it's just a matter of a smaller conversation because now you know exactly what specific points to talk to.

Interesting.

If we're building say our own knowledge base or we're interacting with an AI tool, a lot of people are concerned about the safety of that data.

And so a lot of companies have policies, for instance, about hey, what to put in their client data.

Again, the HIPAA argument, again, it's all about that security.

How secure can we feel that our data, if we're putting in personal information into an AI tool that we're actually paying a subscription for, isn't going into the learning model and that we're staying contained and it's protected or should we be concerned?

Yeah, absolutely we should be.

And then we have to follow certain rules for making sure the data is secure in a normal way.

People are just uploading all the data in ChatGPT that's being used by ChatGPT to get trained on.

But again that's if your personal data, then you are responsible.

And, and when it comes to the other data, the phi data in the hospital system environments or providers or whatever, the health tech applications or even non healthcare, there's an architecture, there's a process to build it.

So for example, there's something called rag retrieval, augmented generation.

So this is where all of your internal sensitive data is tokenized and there's a semantic.

So results are filter off of that.

Then AI gets only the limited data and give you a recommendation off of that rather than just you give everything.

In reality, if you load like hundreds of files in your AI, you may have noticed that it will not be searching in all the files.

Many times you get the results, you realize, oh this information was in the document but AI didn't give because it has the limited context, it cannot process all of those documents at once.

It can, but it wouldn't for whatever the pricing and all other reasons.

So you have limited files, limited data and then I mean that's where the hallucination comes in with this new approach.

When you have the sensitive data, you don't have a hallucination, you reduce that.

But at the same time you are also giving limited data to AI.

Now the second part comes in.

Does the AI use that data to train?

That's where some regulations also come in.

Let's say.

So we work with the AI, the LLM vendors to have them sign ba business Associate agreement where the commit to zero retention policy they commit to not using that data for training LLM.

So when we are building the RAG architecture along with the BEA sign, now we're sure that the data being sent is not being trained and it's not being retained by the LLMs.

So then you're pretty sure okay that this data is not being used.

And again this is where we are talking about the cloud based LLMs.

Alternately, you can build the internal on premise LLMs and use that so that it's not touching any of the public data.

And in those cases you, you still need RAG for other reasons, but then you're not using it for this reason of safety of the data.

You got a fence around it.

Well, it's definitely interesting Guzen for this was a very interesting conversation.

We'll have all your contact information in our show notes.

So it's gosenfur.com and technologyrivers.com is your company and they can can access your blog, they can access your podcast and all different information.

They can find you on LinkedIn.

The book.

Congratulations on the book too.

It's always kind of fun to do it beyond the download how to build mobile apps that people love, use and share every day.

So if you're looking for practical insights and proven strategies to help app developers and entrepreneurs build apps that not only acquire users, but keep them engaged and growing, they can check you out.

Thanks so much for being our guest today.

Oh, thanks for having me.

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