How AiProof Simplifies Content Verification to Build Digital Trust

A long time ago, after Instagram appeared, I started thinking about where online media was heading. I became convinced that short-form video would become dominant long before TikTok, but at that time I did not have the experience or resources to build something around that idea. Later I explored automatic video clipping based on trends, but that also required more resources than I had. Eventually, I stepped back and asked myself a bigger question, what is one of the most important problems the internet will face in the near future? I realized that if people can no longer tell human-created content from AI-generated content, the internet could gradually become an endless stream of recycled synthetic material. I did not want that to be the future. I wanted to find a way to preserve trust and give people something more concrete than another guess about whether a file was made by AI. That thinking became the starting point for AiProof and the direction I decided to follow.

My first attempt at cold email outreach to media companies mostly failed. I tried to reach the right decision-makers, but the response was very low. Looking back, I think the problem was not only the targeting. I was still explaining AiProof too much from the technical side instead of making the value obvious. LinkedIn has worked better for me. It has helped me start real conversations, meet people in the industry, and understand how they see the problem. Some of those conversations have already changed the way I think about the product and its positioning. I am still very early in this process, and I am learning sales and marketing while building the product. After reworking the positioning and presenting AiProof more clearly, including on SaaSBrowser, I believe the next attempts will be much stronger. For me, the goal is not just to grow fast. The experience, relationships, and understanding of the market I gain along the way are also part of building the company.

The biggest issue so far is that the demo is still too technical. People need too much explanation before they understand what AiProof actually does and why it matters. That showed me that I need to spend more time on the product and business side, not only on the technology. The value should be clear within the first 30 seconds, without requiring someone to understand hashes, proof packages, or the underlying infrastructure. I am now simplifying the experience and focusing on a much clearer product flow, so a user can immediately understand what they can protect, what they can verify, and why the result is useful. I also realized that people should not have to study the technology before they feel the value. The product itself should explain the idea through the experience. My goal is that someone can open AiProof, try it without instructions, and quickly have that moment when they understand why origin verification could actually matter to them.

I think a lot of people and companies believe that better models and more computing power will always make it easy to tell AI-generated content from human-created content. I do not think it will be that simple because AI is developing just as fast, and sometimes faster, than detection methods. That is why I think we need more fundamental rules for digital trust. We need to know where a file came from, who registered it, and whether a later copy can be connected to a known original. Then we can make conclusions based on facts, not only on probabilities. Detection can still be useful, but I do not think trust should depend only on a system constantly trying to guess whether something was made by AI. I would rather start from something we actually know and verify from there. For me, it is similar to mathematics. You do not keep guessing the answer to an equation and checking if it looks right. You solve it from what you know.

I want to move step by step because my plans are much bigger than AiProof is today. My long-term goal is to build technology that could help platforms like YouTube, Spotify, and TikTok identify AI-generated content with high confidence and verify where it came from. But I know the hardest part is making the first real step. So first, I want to prove demand, improve the product, and learn from users. If the problem is real, the solution will follow.

1 live public MVP with proof creation and verification already working.

Before AiProof, I had already worked on my own products. I built SnipAl around automatic video processing and highlights, and later Volvex around automated trading and intelligent agents. Both projects taught me a lot about turning an idea into a real working system. At the same time, my main professional background was in software architecture, enterprise systems, and integrations, so technically I was not starting from scratch. What was new for me was the provenance market itself and the business side of building a global product. With AiProof, I had to learn not only how to build the technology but also how to understand the market, explain the idea simply, and find the people who really need it.

I spent too much time on the technical side and not enough on understanding the market and marketing, so now I have to catch up. Working in large companies shaped the way I think. I focused on architecture, processes, and making things work reliably, not on how to sell them. The positive side is that much of the technology is already built. Now I need to explain its value clearly and make it easier for people to understand why they need it. I see it like a restaurant. You can have great ingredients, a great kitchen, and a great chef, but that is not enough if the dish is presented badly. I learned that building the product and learning how to present and sell it have to happen together.