Hitting a 7-figure ARR by taking on deepfakes and fraud

In 2022, with increasing concerns about deepfakes, Daniyal Chughtai saw an opportunity that aligned perfectly with his background in AI and fintech. So, he got to work.

Three years later, Facia has a 7-figure ARR and it's growing quickly.

Here's Daniyal on how he did it. 👇

Doubling down on fraud protection

Coming from an Economics and Mathematics background, I understood the concept of Supply and Demand pretty well. I knew the demand for AI was growing exponentially, and thus graduated with an MSc in Big Data Science.

I worked in the fintech world and was fascinated by fraud detection. So, in 2022, I ended up cofounding Facia.

At Facia, we offer a suite of advanced facial recognition and deepfake detection solutions tailored to enhance security and user authentication across various sectors. Our flagship product, Pay with Face, enables users to authorize payments using facial recognition, eliminating the need for PINs and passwords, and streamlining the transaction process.

Our DeepFake Fraud Prevention, is pioneering the detection of manipulated media, helping organizations protect their reputations and secure their operations.

Additionally, our Facial Recognition Ticketing system allows seamless venue entry by verifying attendees through facial features, enhancing event security and efficiency. We also provide Passwordless Authentication, offering secure and convenient user access without the necessity of remembering complex passwords. ​

Currently, Facia specializes in DeepFake Fraud Prevention, Facial Recognition, and Liveness Detection solutions. Each product is designed to help businesses and governments protect against fraud, enhance security, and streamline authentication processes.

We have seen significant growth in these products, contributing positively to Facia’s financial performance. We have served more than 10M end users so far, and our ARR is between $2.5M and $3.5M.

Finding and validating the idea

Impersonation has been pretty prevalent in many industries throughout the world. However, the idea of detecting the authenticity of a person came into existence, when the mother of a colleague had to book a ride sharing app, and the person who was supposed to pick her up, was not the same person who arrived.

Now, being in South Asia, things could quickly escalate as an unknown person claiming to be the person who he isn't. We decided to launch a solution that would compel the companies to verify the users and remove the abusers of the system.

My vision aligned perfectly with my background in AI and machine learning, and it felt like a natural next step to tackle a pressing issue in the security landscape.

To validate the idea, we conducted extensive research on the potential impact of deepfake technology and liveness detection, talking to industry experts and testing early prototypes with a select group of businesses. The feedback was overwhelmingly positive, and we realized the technology had far-reaching applications beyond just deepfake detection.

Building real-time liveness detection

Building Facia's initial product took around six months, balancing development with regular work hours. We focused on a lean approach, prioritizing essential features to quickly deliver value. Funding came from internal resources, and we kept costs low by using open-source tools.

I led the technical development, and a network of mentors helped us refine the product. The result was a solid MVP that allowed us to gather user feedback for future iterations.

Our tech stack is primarily built in Python, which powers most of our backend infrastructure, especially the machine learning and deep learning components of our system.

Over time, our stack has evolved as we scaled and encountered new challenges. One major technical challenge has been ensuring both speed and accuracy in liveness detection across diverse environments and devices. Achieving real-time performance without compromising detection precision has pushed us to continually refine our models and infrastructure.

Balancing security with UX

One of the biggest challenges we've faced at Facia has been balancing security with user experience. It's one thing to build a highly secure system, but it's another to make sure it’s also smooth and intuitive for users.

Striking that balance took a lot of trial and error — early versions of our liveness detection felt too rigid, and we had to refine them without compromising accuracy. We also underestimated how much edge cases — like varying lighting, camera quality, or user behavior — would affect model performance, which led to countless iterations and testing cycles.

Charge early

Facia operates on a B2B SaaS model, generating revenue through usage-based pricing and custom enterprise plans. Our customers pay based on the number of facial authentications or API calls they process, with flexible tiers for startups, mid-size companies, and large enterprises.

We also offer white-label solutions and integration services for businesses that require more tailored deployments.

We started charging early, right after validating our MVP with pilot users. Our first paying customers were companies in the fintech and government sectors looking to prevent identity fraud. Revenue has steadily grown as we added new features like passive liveness detection and real-time deepfake spotting.

Strategic partnerships and strong client retention have helped maintain positive cash flow and healthy margins.

Growing to 200+ B2B customers

Cold outreach, LinkedIn networking, and forums

We attracted our first users through cold outreach, LinkedIn networking, and industry forums focusing on businesses facing deepfake and identity-fraud challenges. Instead of a hard sell, we led with problem-solving, which helped us land our initial enterprise customers.

Word of mouth

From there, word-of-mouth and case studies helped build trust and open more doors.

SEO

Our SEO-optimized landing page and thought leadership content helped us grow organic traffic steadily.

Strategic partnerships

We also formed strategic partnerships with cybersecurity firms, allowing us to scale without massive ad spend.

If I had to start over, I’d focus on building strategic partnerships earlier on. In the beginning, we were heavily focused on product development and didn’t realize how much easier market validation and expansion could’ve been with the right partners.

That delay slowed our growth in some areas. It also taught us the importance of not just building great tech, but also building the right relationships around it.

Live demos

Live demos played a key role seeing our tech in real time built confidence fast.

Listen to your customers

Today, we’ve grown to over 15,000 monthly visitors, processed millions of API calls, and serve 200+ B2B clients. The key? Solve one real problem really well, and keep listening to your users.

Product-founder fit

One of the most helpful things throughout this journey has been timing — the rise of deepfakes and growing concerns around digital identity fraud created a clear demand for the solutions we were building. That market urgency worked in our favor and helped us gain early traction.

On top of that, having a strong technical foundation in AI and machine learning allowed me to build and iterate quickly, which was critical in the early stages.

Another major advantage has been surrounding myself with the right people - mentors, advisors, and a team that believed in the mission.

Pick a niche

Pick a niche. Instead of trying to solve a broad problem, start by focusing on a smaller, well-defined one. But make sure it’s a real, painful problem that people are actively looking to solve.

That clarity helps with everything: product development, messaging, marketing, and even sales.

A common mistake I see is building too much before talking to users. Don’t wait for a “perfect” product — launch early, get feedback, and iterate fast.

Also, don’t underestimate the power of distribution. A good product needs a clear path to reach its audience. If you're looking for a resource, The Mom Test is a great book for learning how to talk to users without bias.

What's next?

Our goal is to make Facia the global standard for deepfake detection and biometric verification. We're working to enhance our models to detect even the most sophisticated deepfakes in real time, across diverse platforms and environments. On the product side, we aim to expand integrations and improve passive liveness detection to make verification even smoother for users.

We’re targeting strong growth in both revenue and market reach, especially across fintech, healthcare, and government sectors. To get there, we’re doubling down on R&D, strategic partnerships, and expanding our team. One of the biggest challenges ahead is staying ahead of the constantly evolving deepfake landscape — but that’s exactly the kind of problem we’re built to solve.

You can follow along on LinkedIn. And check out Facia.