Meet FalkorDB, Winner of Startups of The Year 2024 in Tel-Aviv/Graph Database for GenAI

The special startups of the 2024 win series win are a celebration of all tech champions this year. You've achieved this! The Hakernoon community can't wait to learn more about your journey!

Tell us about you.

Falkordb delivers an accurate, multi-lease rag solution enabled by a low-lattency, scalable graph database technology.

Our solution has developed goal for development teams working on complex, coherent data — structured or unquestioned — in real-time or interactive user environments

Our mission is clear: to empower organizations with tools that make it accurate, accurate, and seamlessly integrated with advanced AI flows. With Falkordb's graphrag capabilities, businesses may be confident to deploy LLM-based applications that meet their needs for accuracy, speed, and scalability.

Tell us how your start -up is changing the world.

Falkordb is an open resource, ultra-low latency graph database designed for high-throughput, real-time workloads. Our mission is straightforward: make enough graph-native infrastructure enough to handle AI today, cybersecurity, and complex data systems without compromise. We see a future where the connected data is not a bottleneck – this is the backbone of intelligent systems.

FALKORDB has been established to meet a critical challenge of eliminating a large language model (LLM) -based application: the distrust and reliability of existing solutions. Businesses often struggle with these issues, as even the leading vector technologies and database search failed to deliver the high accuracy required for grade-enterprise applications. For example, Microsoft's “Hybrid+Semantic Ranker” has achieved only 75% accuracy until September 2023 – a level that is insufficient for many cases of business use. Our approach is based on cutting research and practical changes.

What sets you from the competition?

FALKORDB⁠ is the first queryable database of owner to use widespread matrices⁠ to ​​represent the adjacent matrix⁠ in graphs and linear algebra⁠ to be in the graph. It uses AVX (Advanced Vector Extension) to accelerate performance and eliminate the need for complex batch processing jobs.

We designed Falkordb from the first principles-using sparse matrice and linear algebra instead of traditional pointer-based storage. That gives us 496x faster query performance, 11x higher throughput, and 6x lower memory use compared to incumbents such as Neo4J. But despite the benchmarks, about the simplicity-removal of the need for complex tuning or batch processing to achieve real-time performance.

What does it mean for you to win this title?

The whole team celebrated this win, very well to us. This has proven that there is a demand for the re -imagination of graph databases. This recognition is not just about Falkordb-the signals that developers and architects are ready for infrastructure that do not force trade-offs between performance and flexibility.

What do you like about your team, and why do you set up for this mission?

We are a team of those who think of the system level-the engineers who don't care much about the trends and more about solving computational problems. Everyone here understands that performance is not a feature; It is architecture. That mindset drives how we build, evaluate, and repeat. This is why we have made bigger players bigger.

In return -It, which milestone is the biggest point for your start?

When Enterprise AI teams began to replace their vector databases and traditional RAG stacks with Falkordb for graphrag implementation. That shift has proven that taking native indigenous people is not theoretical-it delivers measurable improvement in latency, accuracy, and cost. The product-market fit arrived when customers stopped asking “Why the graphs?” And began to ask “how fast can we move?”

What is an important lesson you learned this year that you will pass on to other startups?

Speed ​​in product and decision-wins. But speed without focus leads to noise. In 2024, we learned to say that not to feature requests that did not align our main vision. Staying opinion has helped us to deliver the value faster.

How can you imagine your industry emerging in the coming years, and how will your start?

Graphic databases will move from becoming a bacterial tool to critical ingredients to AI pipelines, cybersecurity frameworks, and real-time analytics. But performance ceilings will expose legacy designs. We stay in advance by continuing to optimize for compute efficiency and by incorporating the graph-native taking into emerging AI and security architecture.

How did you intend your company to embrace the responsibility of this title in 2025?

We will use the visibility to push for better standards around the graph performance transparency. So many sellers hid behind benchmarks that did not reflect the facts of production. We aim to rule open discussions on what modern graphs of the graph demand – and how to meet them.

What goals do you expect to accomplish in 2025?

• Expand Falkordb's adoption of AI-driven businesses replacing vector-based acquisition.
• Release the advanced cluster for the multi-lease, distributed by graph expansion.
• Increase our open-source community with contributions dedicated to real-world performance optimization.

2024 is crazy, with all new techs, and all geopolitic change. What are the effects of this on your start, and your industry as a whole? Be as short or as detailed as you want.

AI Hype has raised expectations around infrastructure, while cost pressure forces companies to re -imagine architectures. That worked in our favor – the years began to look for skinny, faster alternatives to legacy databases. Geopolitical shifts are highlighted of the need for safe, efficient, hosted solutions. We are adapted by focusing on performance, transparency, and providing businesses that are fully controlled by their data.