TopK Raises USD 5.5M to Tackle ChatGPT-Era Challenges with AI-Native Hybrid Search Engine for Enterprises

• TopK, a Czech AI-native enterprise search startup, raised USD 5.5M Seed funding
• Hybrid retrieval solves legacy search limitations by blending vector search, keyword matching, and filtering for speed and precision
• Plans include hiring more engineers, enhancing enterprise features, and expanding security, compliance, and multimodal support

This July, TopK—the Czech developer of AI search engine for enterprises—closed its Seed round of USD 5.5M. The funding comes from such well-known VC firms as Earlybird (invested in FintechOS, for example), KAYA VC, and Irregular Expressions.

To Transform Search with a Smarter Layer

TopK’s founders, Marek Galovič (CEO) and Jerguš Lejko (CTO) have known each other since high school. They had been working on various large-scale systems and AI tooling both together and separately, and their shared experience of building search and AI systems made them acutely aware that traditional search architectures weren’t designed for the emerging demands of AI-native applications.

Believing that they were well‑positioned not only to see the gap but to build a better, unified search layer, they founded TopK in 2024. They chose the name ‘TopK’ as a recognized term in information retrieval referring to the top 1,000 most relevant results.

How AI Boom Highlighted Search Infrastructure Challenges

Indeed, the rise of ChatGPT and more similar LLMs has apparently driven enterprise demand for vector databases—the kind of databases that store data as math-based points (vectors) so computers can quickly find things that are similar or related, like matching images, texts, or sounds. This revealed limitations in traditional search systems. Companies struggle with fragmented infrastructure, performance bottlenecks, and inflexible ranking that can’t adapt to specific needs.

‘Companies are increasingly hitting the limits of today’s search infrastructure. TopK represents a fundamental shift, combining keyword and vector search natively to meet the demands of the AI era. Marek and Jerguš bring deep technical insight and a clear understanding of developer pain points,’ Earlybird’s general partner Dr Andre Retterath confirms.

The Power of Hybrid Retrieval

TopK’s founding duo created their solution to solve the mentioned challenges by allowing developers and AI agents to quickly search both structured and unstructured data, offering high-speed performance and customizable relevance control.

Essentially, TopK’s hybrid retrieval combines vector search, keyword matching, and advanced filtering in one platform, enabling teams to fine-tune queries, optimize rankings, and handle large-scale production workloads—eliminating the need to use multiple separate tools.

‘Traditional search systems often have to send multiple queries behind the scenes. This makes them slower, costlier—and, most importantly, it can weaken the quality of results. We’ve built a smarter search engine that combines the best of both worlds: semantic search—which understands meaning—and classic database tools like keyword lookup, date-based filtering, numerical filtering, and more,’ Mr Lejko states.

Customized Relevance for Scale and Speed

‘Our solution employs ‘adaptive ranking’—sending relevance feedback back to the search engine. It’s a combination of learning-to-rank approaches and model adaptation to optimize the relevance of results for the customer’s domain,’ Mr Galovič adds.

According to the team, TopK tackles massive scale—billions of documents—with sub‑100 ms latency by using a clean architecture that separates write, compaction, and query roles. Writes go to a write-ahead log (WAL); a compactor builds optimized indexes; queries are routed to executors that fetch from object storage, avoiding compute-storage coupling and reducing operational complexity.

To be more specific, the company published a case study showing TopK’s true‑hybrid retrieval outperforming Reciprocal Rank Fusion by 4–7.8% in nDCG@10—a metric of ranking quality—thanks to score-aware ranking and dynamic weight blending.

Mr Galovič underscores that TopK is built with enterprise standards: it is SOC 2 Type I compliant, offers a 99.9% availability SLA, and supports flexible deployment (VPC or public cloud). While the team doesn’t detail encryption, audit trails, and governance controls publicly yet, TopK commits to expanding such features as part of its enterprise roadmap.

TopK debuted its product at Data Council AI Launchpad in Bay Area in May this year, introducing a cloud-native, true‑hybrid search engine supporting vector search, keyword matching, filters, and custom scoring in a single query.

Strategic Investment for Ambitious Growth

‘TopK has built the core of their database to deliver the fastest and most cost-effective hybrid search solution on the market – all with a small but exceptionally capable team,’ KAYA VC’s general partner Karel Zheng states. His team firmly believes that the investment will allow the TopK team to accelerate the development, deliver the enterprise-grade features, and succeed on a global scale.

Specifically, the startup plans to use the new funding injection on hiring engineering talent and accelerate the development of enterprise-grade features, including security, compliance, advanced query processing, natural language understanding, and multimodal data support.

Hybrid search solutions like TopK’s are crucial for enterprises navigating the complexities of today’s data, as they unify semantic understanding with precise keyword filtering to deliver faster, more relevant results. By combining these capabilities in a single platform, businesses get empowered to overcome the limitations of legacy systems, reduce infrastructure complexity, and better tailor search outcomes to their specific needs. Thus, enterprises unlock the full potential of AI-driven applications, enhancing productivity and decision-making at scale.