FOTOhub and EVIDION: two products, one thesis I've been building for two years

Illustration generated by AI (GPT Image 2 model on the FOTOhub.app platform). Disclosed in line with Article 50 of the AI Act.

I currently run two parallel technology ventures out of Bydgoszcz that, at first glance, seem to belong to entirely different market categories, generative AI for creators on one side, cybersecurity and govtech on the other, but which in reality grow out of the same engineering philosophy I've applied consistently since day one as a founder: building durable infrastructure, not point tools chasing whatever trend happens to be hot this quarter. The first is FOTOhub, a Creative AI OS integrating more than 200 AI models into a single working environment for creators, agencies, and developers. The second is EVIDION, an evidence and governance layer for organizations deploying AI systems, which I'm building in direct response to the tightening regulatory requirements coming out of the EU AI Act and Poland's own AI Systems Act.

FOTOhub: from an image generator to an operating system for creators

I founded FOTOhub on September 11, 2025, and in under a year the platform went through exactly the transformation I'd planned for it from the start: from a straightforward AI image-generation tool into a full-fledged Creative AI OS, an operating system for digital creativity. That repositioning wasn't cosmetic; it reflected real product development, because from day one I designed FOTOhub as an orchestration layer, never as just another disposable image generator. Today the platform integrates more than 200 AI models from over 10 providers, including proprietary models I've developed under the FOTOhub-SDK brand, alongside models from Google, OpenAI, ByteDance, and Stability AI, spanning image, video, and audio generation plus workflow automation.

User growth has come in at a pace that I'd rank, even setting aside my own initial business projections, among the fastest in the region for this product category. In March 2026 I reported more than 460,000 registered users and over 11 million pieces of generated content alongside closing a $5.1 million seed round. By July 2026, the user base had crossed 750,000 across more than 40 countries, which is what let me stake out FOTOhub's new market position as Europe's leading Creative AI Platform.

The fundraising track record I've built includes five capital rounds closed within a single year, which is unusual in itself for the Polish startup market. I closed a PLN 3.5 million pre-seed round in January 2026. In February 2026, I closed a $6.1 million seed round led by DGE3 Investment VC with four participating investors. The March seed round, worth $5.1 million with three strategic investors, I directed straight into developing my own proprietary AI models, FOTOcore, GABRIEL, IDA Creative, and Laura Coder, alongside expanding GPU infrastructure. In June 2026, I closed a $2.3 million follow-on round from DGE3, topped up by a $110,000 grant from Amazon Web Services under its startup support program. Total FOTOhub funding, per my July 2026 figures, came to $8.6 million across five rounds, with the company valued at roughly $180 million in Q2 2026. Some third-party trackers cite total funding of $11.3 million or $9.0 million instead, a discrepancy that comes down to differences in how various analytics platforms aggregate individual rounds.

On the horizon, I have a PLN 100 million Series A planned for Q4 2026 or Q1 2027, led by DGE3, earmarked for scaling GPU infrastructure, expanding into German-speaking markets, the UK, and the US, further developing my proprietary AI models, and growing the team. I've mapped out the planned use of those funds as follows: 40 percent toward GPU and cloud infrastructure, 30 percent toward model and platform R&D, 20 percent toward international expansion, and 10 percent toward operations and talent.

FOTOhub's standing in industry rankings is unusually strong for a company at this stage. As of July 2026, the platform ranked 360th on Crunchbase's CB Rank among Generative AI/Tech companies, with a Growth Score of 85 and a Heat Score of 96, signaling a high level of activity and attention around the company during that period. I'm personally listed as a founder in the top 1 percent of Crunchbase's global index, and among the top 10 fastest-growing startups in Poland according to independent industry rankings.

The business model I designed runs on seven subscription tiers, from a free plan offering fifty monthly credits, through Starter, Medium, Professional, and Business tiers, up to a Team plan built for collaborative work and an Enterprise plan with unlimited usage and custom pricing. The business case behind consolidating AI models into a single platform came down to a simple observation: digital creators typically juggle five to eight separate subscriptions across tools like Midjourney, Runway, Canva, and Suno, racking up a combined cost of $80 to $120 a month and forcing constant context-switching between platforms. I built FOTOhub to solve exactly that, folding the same capabilities into one environment for a fraction of the combined price.

EVIDION: an evidence layer for organizations deploying AI systems

The second venture I run alongside FOTOhub is EVIDION, listed on my founder profile on Crunchbase under the CyberSec GovTech category. The starting point for building EVIDION is the structural shift in the regulatory landscape around artificial intelligence across the EU and in Poland specifically, spanning both the EU AI Act and Poland's own AI Systems Act, which establishes the Committee for the Development and Safety of Artificial Intelligence as the national AI market-surveillance authority, set to become formally operational in November 2026.

In an analysis I published on this blog in July 2026, following the announcement that high-risk system obligations had been pushed back sixteen months, from August 2, 2026, to December 2, 2027, I described EVIDION as an evidence and governance layer for organizations implementing AI systems. I built the product concept around three architectural pillars. The first is continuous oversight of AI systems running inside an organization, understood not as a one-off audit performed periodically, but as real-time monitoring of compliance status and risk tied to every deployed system. The second pillar is verifiable accountability documentation, covering cryptographically secured evidentiary integrity, tamper-resistant timestamps, and a full chain of custody for every piece of evidentiary material connected to decisions made by AI systems or decisions about their deployment. The third pillar is a centralized, audit-ready repository of compliance evidence, which I designed to replace the scattered compliance documentation spread across dozens of separate systems and spreadsheets that, in practice, makes it hard to demonstrate compliance quickly whenever a regulator comes calling.

The business case for EVIDION, which I laid out in that same analysis, comes down to turning regulatory compliance from a stated intention into something an organization can actually hand over to an auditor or regulator on request, without weeks of ad hoc documentation scrambling. That approach maps directly onto the real inspection powers granted to Poland's AI regulator, which include remote inspections with response windows of fourteen to thirty days and an active duty to cooperate on the part of the inspected entity, meaning in practice an organization needs structured compliance documentation ready at all times, not just for an annual review.

A separate product offering I describe on my own website covers an AI-powered case management and file analysis system operating under the same EVIDION brand, reflecting my decision to extend the evidence-layer concept beyond AI compliance and into the legal sector and public administration. That direction grows out of a broader thesis behind the whole project: the demand for structured, cryptographically secured, fully reconstructible evidentiary infrastructure reaches far beyond the narrow segment of tech companies deploying AI systems, extending into law firms, courts, and public institutions grappling with the same underlying problem of trust in evidentiary and documentary material.

Why both ventures grow out of the same engineering logic

Even though FOTOhub and EVIDION operate in entirely separate market categories, one in the consumer and business segment of generative AI for creators, the other in the regulatory and institutional segment of cybersecurity and govtech, they share the same architectural decision I make regardless of what industry I happen to be building in: constructing a centralized orchestration layer that spans multiple data sources or models at once, rather than a narrow tool solving a single, isolated problem. For FOTOhub, that layer is a single API and billing system handling more than 200 AI models from over 10 providers, which by definition required me to take a systemic approach to managing quality, cost, and compliance across every one of those models, including the synthetic-content labeling mechanisms mandated by EU AI transparency regulations. For EVIDION, that layer is a centralized, cryptographically secured evidence registry, which I'm designing to replace the fragmented compliance documentation currently scattered across dozens of incompatible internal systems inside an organization.

That same engineering philosophy, building an orchestration layer over a fragmented ecosystem of tools or data sources, shows up in both of my ventures regardless of the fact that they address completely different business and regulatory problems. For FOTOhub, it means replacing the fragmentation of the generative AI tooling market, where a creator has to juggle five to eight separate subscriptions, with one unified workspace. For EVIDION, it means replacing the fragmentation of regulatory compliance documentation, scattered across risk registers, policies, contracts, and incidents held in separate systems, with a single, coherent evidence graph resistant to after-the-fact reconstruction under the pressure of a regulatory inspection or a legal dispute.

Key metrics for both ventures at a glance

The regulatory backdrop tying both of my ventures together

Putting FOTOhub and EVIDION side by side only makes full sense in light of the regulatory shifts I've been tracking and responding to simultaneously across the EU and Poland throughout 2026. Article 50 of the EU AI Act, in force since August 2, 2026, introduces an obligation to label AI-generated synthetic content and to inform users when they're interacting with an AI system, which directly affects platforms like FOTOhub, generating image, video, and audio content at scale for hundreds of thousands of my users. In parallel, Poland's AI Systems Act, signed by the president on July 24, 2026, establishes the Committee for the Development and Safety of Artificial Intelligence as the national AI market-surveillance authority, armed with inspection powers covering remote audits, requests for access to IT systems and cloud data, and administrative sanctions. These two parallel regulatory tracks are creating a market for a product like EVIDION, while at the same time forcing me to build AI-transparency mechanisms directly into FOTOhub's architecture, not as a bolt-on deployed under regulatory pressure, but as an integral part of the system from the moment I first design it.