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§ Private Profile · Riga, Latvia
Technology company with no verifiable information; search results describe a discontinued 2006 Mattel RFID video game console.
Hyperscan is an organization whose primary operational focus and geographic headquarters are not publicly disclosed, with no readily identifiable digital footprint or official corporate presence. Specific details regarding its business model, target sectors, and customer base are not readily available in public records or industry databases, suggesting a limited public profile. The entity has not publicly reported key financial metrics, such as funding raised, asset under management, or valuation figures, nor has it disclosed employee or user counts. No recognizable lead investors, significant portfolio companies, or major customers are publicly associated with Hyperscan, limiting insight into its market position. Information concerning its founding year and the names of its founders remains unconfirmed in available public documentation, distinguishing it from historical products sharing a similar name.
Hyperscan has raised $480K across 1 funding round.
Hyperscan has raised $480K in total across 1 funding round.
Hyperscan has raised $480K across 1 funding round. Most recently, it raised $480K Seed in April 2023.
| Date | Round | Lead Investors | Other Investors | Status |
|---|---|---|---|---|
| Apr 1, 2023 | $480K Seed | SMOK Ventures | World Fund, Bartosz Lipnicki, Davis Siksnans, Einar Roosileht | Announced |
Hyperscan has raised $480K in total across 1 funding round.
Hyperscan's investors include SMOK Ventures, World Fund, Bartosz Lipnicki, Davis Siksnans, Einar Roosileht.
Hyperscan is an AI-powered talent acquisition platform that analyzes thousands of LinkedIn profiles or resumes to identify top candidates based on custom yes/no questions.[2][4][5] It serves recruiters, talent teams, and investors by automating profile enrichment with workplace history, company details, and AI-driven scoring, solving the problem of manual LinkedIn searching and filtering that wastes hours on reviewing unfit profiles.[2][4] The platform has gained traction with hundreds of teams worldwide, claiming to have answered 0 million+ questions and saved 84,300+ hours, and is seed-funded out of Riga, Latvia.[2][5]
Hyperscan was co-founded by CEO Janis Kis (also spelled Giannis in some demos), who leads the company focused on revolutionizing LinkedIn-powered searches.[4] Emerging from the need to streamline talent acquisition amid limited LinkedIn filters, the idea leverages AI natural language processing to go beyond basic searches, enriching profiles with company descriptions, sizes, industries, and histories for deeper analysis.[2][4] Early traction came via a Chrome extension for one-click imports, demos showcasing rapid candidate shortlisting (e.g., prioritizing female tech workers from multinationals), and testimonials from users like DoubleFox co-founder Liga Gulbe, who discovered previously missed profiles.[2][4] Seed funding supports its growth as an AI/software startup in generative AI and IT.[5]
(Note: Distinct from Sigma360's HyperScan for compliance screening[1], Hexagon's 3D scanner[3], or Intel's Hyperscan regex engine[6].)
Hyperscan rides the AI-augmented recruiting boom, where talent wars in tech demand faster, smarter sourcing amid LinkedIn's 1B+ users and restrictive filters.[2][4] Timing aligns with generative AI's rise (post-2023 boom), enabling precise, scalable profile analysis when remote/hybrid hiring exploded and VCs scout pre-visible founders.[2][5] Market forces like labor shortages, EU AI regulations, and tools' shift to compliant public data favor it, influencing the ecosystem by cutting recruiter hours (e.g., 84k+ saved), boosting diversity filters, and aiding seed-stage discovery—democratizing access for smaller teams.[2]
Hyperscan is poised to expand beyond recruiting into investor scouting and resume screening, potentially integrating more data sources or enterprise APIs as seed funding fuels growth.[2][5] Trends like multimodal AI and stricter privacy laws will shape it, enhancing accuracy while maintaining compliance edges. Its influence may evolve to set standards in AI talent tech, helping users "stop searching, start finding"—cementing its role as a must-have for efficient, insightful profile hunting in a profile-saturated world.[2]