Company name: Grakn Labs – grakn.ai
Founders: Haikal Pribadi and Precy Kwan
Background: Haikal is a robotics and optimisation algorithm expert, and Precy is a chartered account with a background in finance, with particular expertise in cross-border financial service. They met as postgraduate students at the University of Cambridge.
Located: Holloway Road, London
What does your startup do?
At Grakn Labs, we build the GRAKN.AI database. Grakn is a hyper-relational database for knowledge engineering. It is rooted in Knowledge Representation and Automated Reasoning research, and provides the knowledge base foundation for intelligent/cognitive systems.
What is the problem you are addressing?
AI and Cognitive systems process knowledge that is too complex for current databases. This is because making intelligent inferences about the real world requires a sophisticated data model of that world and sophisticated tools to make use of the data. Unfortunately, existing databases are inadequate for this task. They render data too complex to model, too complex to query, and too expensive to analyse. Moreover, existing database query languages are too low level abstractions for effectively working with knowledge.
GRAKN.AI is a database designed specifically for knowledge engineering and building AI and cognitive applications. Grakn solves the challenge of working with complex data. Its query language, Graql, is highly expressive, allowing complex domains to be modelled. It furthermore provides automated reasoning of data points during runtime (OLTP) and automated distributed algorithms (BSP) as a language (OLAP). Its query language provides a strong abstraction over low-level constructs and complex relationships, making it easier to work with complex data.
We aim to disrupt the artificial intelligence industry and be the de facto database for knowledge engineering within 5 years. In essence, what Oracle did for Business Intelligence, Grakn will do for Artificial Intelligence.
What are the main industries and applications for Grakn.ai?
Because Grakn is a database, it is a horizontal technology that fits into any environment with complex, highly-interconnected data. In particular, Grakn is especially geared towards use cases in enterprise environments with complex data. We’ve had notable uptake in the financial service sector and the biomedical/life science sector, but Grakn’s value extends to any complex data use case, and therefore has huge potential in sectors such as manufacturing, cybersecurity, publishing, and retail.
In terms of particular applications, Grakn can be used as a backend for any knowledge-oriented system. Key examples being built by Grakn customers include GDPR-compliance management, operations optimisation systems, enterprise chatbots for customer service, Custom-360 and marketing analytics platforms, enterprise-wide knowledge bases, drug discovery engines, and financial risk analytics platforms
What is your funding situation?
We’ve been very well supported by an active angel investor in London, who supports high-impact, deep-technology companies.
How did Grakn.ai come about?
Haikal and Precy met as postgraduate students at the University of Cambridge. Haikal was pursuing an MPhil in Advanced Computer Science and Precy was pursuing an MPhil in Land Economy. They had discussed the idea of founding a company over the course of their studies, though initially pursued other opportunities after graduation. Haikal went to work at a major supply chain scheduling and optimisation firm in the Netherlands and Precy was working in real estate finance in Latin America.
In the course of his work as an optimisation algorithm specialist, Haikal continually encountered the problem of complex networks of data and the lack of effective data modelling and knowledge management/analytics tools to tackle this problem in the best technical manner. Haikal was in the process of preparing to pursue a DPhil to help address these issues when he met our angel investor, who convinced him to solve the problems on the ground–in a startup–rather than in academia. Haikal reached out to Precy, who saw a lot of synergies within the cross-border financial world she has expertise in. In particular, she saw many applications within the banks and asset management funds with whom she had been working. Very quickly, Precy and Haikal saw the greater implications of building the right tools to address data complexity, and the rest was history.