Boarding Pass“We’re transforming the market by making LLM deployment more practical and effective”
Boarding Pass
“We’re transforming the market by making LLM deployment more practical and effective”
FalkorDB has raised a total of $3 million to penetrate the $200 billion GenAI market
“The traditional market in which FalkorDB operates is the graph databases market, which was valued globally at over $3 billion and is expected to grow at a compound annual growth rate of 21.9% until 2030,” explained FalkorDB. “The market is rapidly evolving with increasing demand for solutions capable of managing complex relationships in data.”
FalkorDB is targeting a new market of large language models (LLMs), which is one of the fastest-growing markets in the technology sector with significant growth in the use of these models for content generation, customer service, and more. The company took part in CTech’s Boarding Pass series and shared that the global market for Generative AI was valued at $200 billion in 2023 and is expected to cross the trillion-dollar mark by 2030.
“A significant challenge in the field of large language models is the adoption of the technology in large organizations so that the models can assist in making rapid, high-reliability business decisions based on internal organizational information,” it added. “Using language models trained on internet data cannot provide a solution to this need because these models have not been exposed to the internal information of the organization and also suffer from "hallucinations" that undermine reliability and the adoption of the technology by organizations. The solution to this need is to make the up-to-date and relevant organizational information accessible to the model at runtime and base the answers generated by the model on this information. The field of solutions for this issue is called RAG (Retrieval-Augmented Generation).”
You can learn more about the company below.
Company Name: FalkorDB
Sector: AI & Databases
Product/Service description:
The company provides GraphRAG technology, which improves the performance of large language models by adding an external data source in the form of a knowledge graph. This allows the model to retrieve relevant information before generating a response, enhancing the model's ability to provide accurate and context-aware answers. The integration of large language models with knowledge graphs bridges the gap between the generative language capabilities of the models and the structured, detailed data stored in knowledge bases.
To facilitate a straightforward process, FalkorDB is developing a solution that fully automates the conversion of organizational information into graphs, a process that can otherwise be complex and challenging. The combination of FalkorDB with GraphRAG provides an innovative solution that leverages the benefits of both worlds—the ability to manage graph data efficiently and quickly, and the ability to integrate relevant external knowledge to improve decision-making processes and interactions with large language models.
The unique technological advantage of FalkorDB compared to the rest of the market is an exceptionally fast graph database that uses sparse matrices for graph representation and algebraic expressions computation for querying.
Founder Bios:
Roi Lipman, CTO and co-founder, conceived the idea for developing a database like FalkorDB more than 8 years ago. Roi is part of the team that brought the vision to reality and developed the technology behind FalkorDB.
Avi Avni, the chief architect and one of the founders of FalkorDB, is an expert in databases and has contributed to the development of programming languages such as C# and F#. In his spare time, Avi developed a database similar to FalkorDB and decided to join forces with his partners to create an innovative and fast product.
Dr. Guy Kurland, CEO and co-founder of FalkorDB, brings rich experience in developing high-speed databases. Guy has worked on developing high-speed databases in the past at several companies and has led development teams in the field.
The three entrepreneurs are alumni of Redis, a leading company in the field of fast data access that provides in-memory database solutions. Their experience at Redis helped them develop FalkorDB with an emphasis on speed and efficiency.
Year of Founding: 2023
Last Investment Round: $3 million
Last Investment Stage: Seed
Date of Last Investment: January 2023
Total investment to date: $3 million
Investors: Angular Ventures, Lead, K5 Tokyo Black; Aryeh Mergi - Co-Founder M-Systems, XtreamIO, Pliops; Jerry Dischler - President, Cloud Applications at Google; Eldad Farkash & Saar Bitner - Firebolt Co-Founders
Current number of employees: 7
Open positions: Core Developer; Cloud Architect/DevOps Engineer; Product Marketing Manager.
Website:
https://www.falkordb.com
How was the idea born?
The idea for FalkorDB was born from our observation that enterprises struggle to deploy LLM-based applications due to issues with trust and reliability in their results. We realized that even with the best vector/search database solutions, achieving high accuracy is a challenge.
The numbers speak for themselves. In September 2023, Microsoft published a report indicating that their “hybrid+Semantic Ranker” achieved the best results but still only managed about 75% accuracy. This level of accuracy is insufficient for enterprise applications.
Simultaneously, we noticed several academic papers published in 2023 suggesting that the optimal solution for RAG involves utilizing a Knowledge Graph (GraphRAG). This immediately resonated with us, as we already offer the best low-latency Graph Database on the market.
What is the need for the product?
Businesses require a reliable method to deploy LLM products with confidence, ensuring accuracy and transparency in the results. Integrating a low-latency Knowledge Graph (GraphRAG) helps manage complex data relationships efficiently, enhancing accuracy, reducing latency, and increasing trust in LLM outputs.
How is it changing the market?
FalkorDB is transforming the market by making LLM deployment more practical and effective for businesses. Addressing key issues of latency and trust empowers companies to leverage LLMs in more impactful ways, driving innovation and broader adoption of these advanced technologies. Additionally, FalkorDB overcomes a long-standing barrier to Knowledge Graph adoption by automating their creation using LLMs. This synergy between LLMs and Knowledge Graphs streamlines the process, making it easier for businesses to implement and benefit from sophisticated data relationships without the traditional complexity and manual effort.
How big is the market for the product and who are its main customers?
The market for FalkorDB is substantial and rapidly growing, driven by the increasing adoption of AI and LLM technologies across various industries. The global AI market is expected to reach hundreds of billions of dollars in the coming years, with a significant portion dedicated to LLM applications and data management solutions. FalkorDB's main customers include enterprises in sectors such as finance, healthcare, e-commerce, and technology, where the need for reliable, efficient, and interpretable data processing is critical.
Does the product exist already? If not - at what stage is it and when is it expected to hit the market?
The product exists and is already in production with tens of companies based on the free open-source version of FalkorDB. And we just launched our FalkorDB-hosted solution.
Who are the main competitors in this sector and how big are they?
The market for RAG solutions for LLM applications is growing rapidly. Recently, most of the database companies have added vector indexing support in order to provide solutions for RAG. This has resulted in a landscape where some vector-based solutions might downplay the limitations of their approach to win over customers.
What is the added value that the founders bring to the company and the product?
The founders of FalkorDB bring a wealth of experience to the table, having spent a combined total of roughly 50 years building low-latency databases. This deep understanding of the field positions them well to develop innovative solutions that push the boundaries of performance and efficiency.
What will the money coming in from the round be used for?
The funds will be used to accelerate the development of their GraphRAG solution and jumpstart their Go-To-Market (GTM) strategy.
In the "Startup Boarding Pass" section, CTech will cover the (relatively) small investments made in companies during the early stages of their existence - and the entrepreneurs and startups who have not yet had the opportunity to reveal their stories to the world. Please use the linked form and fill it out according to the guidelines. This form is intended for startups raising between $500,000 and $3 million from venture capital funds, angels, or official grants from Israeli and foreign institutions. If relevant, someone at CTech will be in touch for follow-up questions.
