Before venture capital had a word for "technical founder-friendly," Kirill Kirykov was already living the concept. His fund pitch isn't slides and TAM estimates - it's a decade of production code, a principal architect's instinct for what actually ships, and a co-founder's memory of what it costs to get to product-market fit on borrowed time.
Kirykov's career trajectory reads like a map of every technology wave that mattered. He graduated from Kharkiv National University with a Master's in Mathematics and Computer Science, moved into enterprise software at Luxoft and Harman, then co-founded 4IRE Labs at a moment when "blockchain company" was either visionary or laughable, depending on who you asked. For over a decade, 4IRE Labs made the case for visionary. The firm shipped 100+ applications, built enterprise software for Chrysler and Ferrari, and - long before ESG tech became a fundraising category - delivered what is credited as the world's first blockchain-based platform for validating green bonds and reporting their environmental impact. Open-source Ethereum tooling for the Aragon Foundation. Production-grade finance infrastructure. At a Ukrainian software house. In the 2010s.
That's the operating pattern that defines Kirykov: early, specific, and technically rigorous. Not "interested in blockchain." Building the thing before the category exists. It's a pattern he repeated in 2020 when he co-founded Datrics.ai alongside Volodymyr Sofinskyi and Anton Vaisburd. Datrics solves a friction that every enterprise data team knows intimately: the gap between the people who ask business questions (CFOs, operations leads, department heads) and the people who can answer them (data engineers, analysts with SQL fluency). Datrics makes AI analytics low-code enough that a CFO can query their own data in natural language - no database access required, no data team intermediary needed.
Y Combinator thought this was worth backing. Datrics entered YC's Winter 2021 batch, one of the most competitive cohorts the accelerator had run. Kirykov relocated to San Francisco. The city was still mid-pandemic ghost town when YC W21 kicked off, but the demo day pipeline held - and Datrics raised. Kirykov had crossed from operator to founder-backed-by-the-best. The next move was inevitable.
Building the Fund
In December 2021, Kirykov co-founded SID Venture Partners with seven other Ukrainian IT executives: Andrii Lazorenko, Anton Vaisburd, Dmitry Vartanian, Illia Polosukhin, Valery Krasovsky, Veronica Korzh, and Vladimir Beck. The firm's founding thesis was specific and unusual. Most seed funds are built around a single GP's network and sector conviction. SID was built around a collective - eight operators who had collectively run software companies across Eastern Europe and North America, who understood Ukrainian technical talent from the inside, and who wanted to create venture infrastructure that reflected how builders actually think.
A fund by IT geeks, for IT geeks - the first Ukrainian high-tech VC built from inside the tech community it serves.SID Venture Partners founding thesis
SID's sweet spot is seed to Series A, with a $100K-$5M investment range and a $1.5M average ticket. The $15M AUM is intentionally focused - it's a fund size that demands discipline and forces selection. The investment thesis spans AI, Data, Deep Tech, B2B, FinTech, AutoTech, Web3, and Blockchain, which sounds broad until you realize that every sector on that list maps directly to something Kirykov or his co-GPs have built. This is not a thesis assembled from conference panels. It's a thesis assembled from scar tissue.
Four years in, SID's portfolio has reached 29 companies. Recent investments include Zibra, NewHomesMate, Limitless Labs, Field Complete, and Famcare Technologies. The fund made eight new investments in 2025 alone. And in October 2024, the first major exit arrived: Elai.io, an AI video generation platform, was acquired by Panopto. First-exit validation for a thesis that bet on deep-tech founders building production AI tools before "generative AI" was a dinner party topic.
Both Sides of the Table
What makes Kirykov unusual in the GP roster isn't the YC pedigree (though that helps) or the blockchain background (though that differentiates). It's that he's still operating. Datrics.ai is live, fundraising, and expanding - most recently into generative AI healthcare coding automation, automating the complex translation work between clinical documentation and billing codes. Kirykov is simultaneously the founder presenting at demo day and the investor sitting across from founders at partner meetings. He knows what it feels like to need a check written, and he knows what it costs when the wrong check writer shows up.
That dual perspective is exactly what SID's portfolio founders describe as the fund's edge. The technical due diligence isn't checkbox-level architecture questions. It's a principal architect reading your stack choices and understanding what they mean about your team's trade-offs. It's the difference between a VC who has heard of Kubernetes and a VC who has managed agile teams of 30 and shipped production blockchain infrastructure. When Kirykov's fund invests, founders get access to 200+ service business customers across 11 countries as distribution partners, a CEE talent pool for engineering support, and 13 GPs with combined experience across 50+ deals in North America and Europe.
The Architecture of a Career
Kirykov's career has a consistent structural signature: build in the space between what's theoretically possible and what enterprises are willing to deploy. At 4IRE Labs, that was production blockchain when enterprises were still skeptical. At Datrics, that's enterprise AI analytics when IT departments were still insisting only data engineers could touch the infrastructure. At SID, that's backing founders doing the same thing - arriving at the answer before the question is commonly asked.
The math background matters here more than it might appear. A mathematician who moves into software development and then into venture capital is not going the usual route. But mathematical training builds a specific kind of thinking: rigorous pattern recognition, comfort with abstraction, and an intuition for whether a model is actually sound or just superficially convincing. These are precisely the faculties that separate technical investors who can evaluate a startup's architecture from those who cannot.
San Francisco, 2025. Kirykov is running two companies simultaneously - a VC firm and a Y-Combinator-backed AI startup - while the technology waves he's been surfing for fifteen years are finally breaking into the mainstream. The green bonds came before climate tech. The blockchain infrastructure came before DeFi. The enterprise AI came before GPT-4 made it a household discussion. The pattern holds. The next bet is already being placed.