Businesses sit on a lot of unstructured data and often don’t have the ability to get much out of it.
Now imagine having a way to store data and being able to ask it questions, for example, “When did ABC Company sign their first contract with us?” or “Show me videos containing blue skies”.
That’s what SeMI Technologies built with Weaviate, a vector search engine. It is a unique type of AI-based database using machine learning models that produce vectors, also called integrations, hence the name vector search engine, said Bob van Luijt , CEO and co-founder of SeMI.
He explained that vector search engines are nothing new – Google Search is an example of a solution built on top of a vector search engine. However, SeMI’s goal is to make this technology commonplace and has an open source business model so anyone can use it.
Van Luijt gave my colleague, Alex Wilhelm, some under-the-hood insight into the tech last year by creating a semantic search engine that answers questions about 2021 Techcrunch articles.
“Anyone can use technology, and we have tools and services for businesses that need it,” van Luijt added. “We don’t create or distribute the actual models – it’s something that companies like Huggingface or OpenAI do, or companies create models themselves. But having the models is one thing, using them to power your production search and recommendation systems is another, and that’s exactly what Weaviate solves.
Since founding the company in 2019 with CTO Etienne Dilocker and COO Micha Verhagen, van Luijt has seen SeMI’s technology inspire more than 100 use cases, including startups, like Keenious or Zencastr, creating new businesses based on the new possibilities that a vector search engine offers them, and uses where the results provided by Weaviate directly help people, for example in the medical field.
Some of van Luijt’s personal favorites were those he thought were more “esoteric”, including vectoring and researching the human genome, mapping the entire world into vectors, or so-called graph embeddings, which can be easily searched through with Weaviate, like a demo SeMI created on graph embeddings from Meta Researches.
SeMI raised a $1.2 million seed in August 2020 from Zetta Venture Partners and ING Ventures and since then has been on the radar of venture capital firms. Since then, its software has been downloaded nearly 750,000 times, a growth of around 30% per month. Van Luijt did not elaborate on the company’s growth metrics, but said the number of downloads can be correlated to sales of enterprise licenses and managed services. Additionally, the spike in usage and understanding of Weaviate’s added value has resulted in an increase in all growth metrics and the exhaustion of its seed funding.
Although seed funding was gone, the company was not actively seeking new funding. However, when the SeMI co-founders had conversations with Cortical Ventures, a new fund from former Datarobot founders and New Enterprise Associates (NEA), van Luijt said the companies showed them how they would support the business. .
“It was really ‘pinch my arm jaw-dropping’ awesome,” he added. “Everything they have done in the past, the teams supporting us, was exactly what we were looking for, and I can say from very recent experience that all the incredible stories are true.”
These conversations led to NEA and Cortical co-leading a new $16 million Series A funding round.
SeMI intends to deploy the new funding to hire US and European talent and double its open source community for Weaviate and vector research in general. It will also place more emphasis on bringing to market and products around the open source core, and taking its first steps into researching where machine learning overlaps with computing.
Meanwhile, van Luijt thinks we’re looking at the next wave of database technology that started with the SQL wave that ushered in big winners, like Oracle and Microsoft, followed by a second wave that was the basic wave of data without SQL, with winners like MongoDB and Redis.
“We are now on the verge of a new generation of databases, those that are AI-driven, and Weaviate is an example of that,” he added. “We need to educate the market not only about Weaviate, but about vector search databases, or AI-first databases for that matter. This is an extremely exciting thing to do because machine learning brings something uniquely awesome to the table. For example, having your database answer natural language questions on millions, if not billions, of documents, or “understand” what’s in millions of photos or videos. »