Nov
01
2018
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Neo4j nabs $80M Series E as graph database tech flourishes

Neo4j has helped popularize the graph database. Today it was rewarded with an $80 million Series E to bring their products to a wider market in what could be the company’s last private fundraise.

The round was led by One Peak Partners and Morgan Stanley Expansion Capital with participation from existing investors Creandum, Eight Roads and Greenbridge Partners. Today’s investment exactly doubles their previous amount bringing the total raised to $160 million.

Neo4j founder and CEO Emil Eifrem didn’t want to discuss valuation, calling it essentially a vanity metric. “We’re not sharing that. I never understood that. It’s just weird bragging rights. It makes no sense to customers, and makes no sense to anyone,” he said referring to the valuation.

Graph view of Neo4j funding rounds. Graphic: Neo4j

When you bring a company like Morgan Stanley on as an investor, it could be interpreted as a kind of signal that the company is thinking ahead to going public. While Eifrem wasn’t ready to commit to anything, he suggested that this is very likely the last time he raises funds privately. He says that he doesn’t like to think in terms of how he will exit so much as building a good company and seeing where that takes him. “If your mental framework is around building a great company, you’re going to have all kinds of options along the way. So that’s what I’m completely focused on,” Eifrem explained.

In 2016, when his company got a $36 million Series D investment, Eifrem says that they were working to expand in the enterprise. They have been successful with around 200 enterprise customers to their credit including Walmart, UBS, IBM and NASA. He says their customers include 20 of the top 25 banks and 7 of the top 10 retailers.

This year, the company began expanding into artificial intelligence. It makes sense. Graph databases help companies understand the connections in large datasets and AI usually involves large amounts of data to drive the learning models.

Two common graph database use case examples are the social graph on a social site like Facebook, which lets you see the connections between you and your friends or the purchase graph on an Ecommerce site like Amazon which lets you see if you bought one product, chances are you’ll also be interested in these others (based on your purchase history and what other consumers have done who have bought similar products).

Eifrem wants to use the money to expand the company internationally and provide localized service in terms of language and culture wherever their customers happen to be. As an example, he says today European customers might get an English speaking customer service agent if they called in for help. He wants to provide service and the website in the local language and the money should help accomplish that.

Neo4j was founded in 2007 as an open source project. Companies and individuals can still download the base product for free, but the company has also built a successful and growing commercial business on top of that open source project. With an $80 million runway, the next stop could be Wall Street.

Sep
20
2018
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AI could help push Neo4j graph database growth

Graph databases have always been useful to help find connections across a vast data set, and it turns out that capability is quite handy in artificial intelligence and machine learning too. Today, Neo4j, the makers of the open source and commercial graph database platform, announced the release of Neo4j 3.5, which has a number of new features aimed specifically at AI and machine learning.

Neo4j founder and CEO Emil Eifrem says he had recognized the connection between AI and machine learning and graph databases for a while, but he says that it has taken some time for the market to catch up to the idea.

“There has been a lot momentum around AI and graphs…Graphs are very fundamental to AI. At the same time we were seeing some early use cases, but not really broad adoption, and that’s what we’re seeing right now,” he explained.

AI graph uses cases. Graphic: Neo4j

To help advance AI uses cases, today’s release includes a new full text search capability, which Eifrem says has been one of the most requested features. This is important because when you are making connections between entities, you have to be able to find all of the examples regardless of how it’s worded — for example, human versus humans versus people.

Part of that was building their own indexing engine to increase indexing speed, which becomes essential with ever more data to process. “Another really important piece of functionality is that we have improved our data ingestion very significantly. We have 5x end-to-end performance improvements when it comes to importing data. And this is really important for connected feature extraction, where obviously, you need a lot of data to be able to train the machine learning,” he said. That also means faster sorting of data too.

Other features in the new release include improvements to the company’s own Cypher database query language and better visualization of the graphs to give more visibility, which is useful for visualizing how machine learning algorithms work, which is known as AI explainability. They also announced support for the Go language and increased security.

Graph databases are growing increasingly important as we look to find connections between data. The most common use case is the knowledge graph, which is what lets us see connections in a huge data sets. Common examples include who we are connected to on a social network like Facebook, or if we bought one item, we might like similar items on an ecommerce site.

Other use cases include connected feature extraction, a common machine learning training techniques that can look at a lot of data and extract the connections, the context and the relationships for a particular piece of data, such as suspects in a criminal case and the people connected to them.

Neo4j has over 300 large enterprise customers including Adobe, Microsoft, Walmart, UBS and NASA. The company launched in 2007 and has raised $80 million. The last round was $36 million in November 2016.

Apr
21
2018
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In the NYC enterprise startup scene, security is job one

While most people probably would not think of New York as a hotbed for enterprise startups of any kind, it is actually quite active. When you stop to consider that the world’s biggest banks and financial services companies are located there, it would certainly make sense for security startups to concentrate on such a huge potential market — and it turns out, that’s the case.

According to Crunchbase, there are dozens of security startups based in the city with everything from biometrics and messaging security to identity, security scoring and graph-based analysis tools. Some established companies like Symphony, which was originally launched in the city (although it is now on the west coast), has raised almost $300 million. It was actually formed by a consortium of the world’s biggest financial services companies back in 2014 to create a secure unified messaging platform.

There is a reason such a broad-based ecosystem is based in a single place. The companies who want to discuss these kinds of solutions aren’t based in Silicon Valley. This isn’t typically a case of startups selling to other startups. It’s startups who have been established in New York because that’s where their primary customers are most likely to be.

In this article, we are looking at a few promising early-stage security startups based in Manhattan

Hypr: Decentralizing identity

Hypr is looking at decentralizing identity with the goal of making it much more difficult to steal credentials. As company co-founder and CEO George Avetisov puts it, the idea is to get rid of that credentials honeypot sitting on the servers at most large organizations, and moving the identity processing to the device.

Hypr lets organizations remove stored credentials from the logon process. Photo: Hypr

“The goal of these companies in moving to decentralized authentication is to isolate account breaches to one person,” Avetisov explained. When you get rid of that centralized store, and move identity to the devices, you no longer have to worry about an Equifax scenario because the only thing hackers can get is the credentials on a single device — and that’s not typically worth the time and effort.

At its core, Hypr is an SDK. Developers can tap into the technology in their mobile app or website to force the authorization to the device. This could be using the fingerprint sensor on a phone or a security key like a Yubikey. Secondary authentication could include taking a picture. Over time, customers can delete the centralized storage as they shift to the Hypr method.

The company has raised $15 million and has 35 employees based in New York City.

Uplevel Security: Making connections with graph data

Uplevel’s founder Liz Maida began her career at Akamai where she learned about the value of large data sets and correlating that data to events to help customers understand what was going on behind the scenes. She took those lessons with her when she launched Uplevel Security in 2014. She had a vision of using a graph database to help analysts with differing skill sets understand the underlying connections between events.

“Let’s build a system that allows for correlation between machine intelligence and human intelligence,” she said. If the analyst agrees or disagrees, that information gets fed back into the graph, and the system learns over time the security events that most concern a given organization.

“What is exciting about [our approach] is you get a new alert and build a mini graph, then merge that into the historical data, and based on the network topology, you can start to decide if it’s malicious or not,” she said.

Photo: Uplevel

The company hopes that by providing a graphical view of the security data, it can help all levels of security analysts figure out the nature of the problem, select a proper course of action, and further build the understanding and connections for future similar events.

Maida said they took their time creating all aspects of the product, making the front end attractive, the underlying graph database and machine learning algorithms as useful as possible and allowing companies to get up and running quickly. Making it “self serve” was a priority, partly because they wanted customers digging in quickly and partly with only 10 people, they didn’t have the staff to do a lot of hand holding.

Security Scorecard: Offering a way to measure security

The founders of Security Scorecard met while working at the NYC ecommerce site, Gilt. For a time ecommerce and adtech ruled the startup scene in New York, but in recent times enterprise startups have really started to come on. Part of the reason for that is many people started at these foundational startups and when they started their own companies, they were looking to solve the kinds of enterprise problems they had encountered along the way. In the case of Security Scorecard, it was how could a CISO reasonably measure how secure a company they were buying services from was.

Photo: Security Scorecard

“Companies were doing business with third-party partners. If one of those companies gets hacked, you lose. How do you vett the security of companies you do business with” company co-founder and CEO Aleksandr Yampolskiy asked when they were forming the company.

They created a scoring system based on publicly available information, which wouldn’t require the companies being evaluated to participate. Armed with this data, they could apply a letter grade from A-F. As a former CISO at Gilt, it was certainly a paint point he felt personally. They knew some companies did undertake serious vetting, but it was usually via a questionnaire.

Security Scorecard was offering a way to capture security signals in an automated way and see at a glance just how well their vendors were doing. It doesn’t stop with the simple letter grade though, allowing you to dig into the company’s strengths and weaknesses and see how they compare to other companies in their peer groups and how they have performed over time.

It also gives customers the ability to see how they compare to peers in their own industry and use the number to brag about their security position or conversely, they could use it to ask for more budget to improve it.

The company launched in 2013 and has raised over $62 million, according to Crunchbase. Today, they have 130 employees and 400 enterprise customers.

If you’re an enterprise security startup, you need to be where the biggest companies in the world do business. That’s in New York City, and that’s precisely why these three companies, and dozens of others have chosen to call it home.

Oct
24
2017
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New Neo4J platform gives developers a set of tools for building enterprise graph applications

 Neo4j builds tools for creating graph databases, and today at its GraphConnect conference in New York City, it announced a new platform for developers to build graph-based applications using a common set of services.
Emil Eifrem, Neo4j co-founder, says while the concept of graph databases has steadily gained popularity in recent years, the databases need to connect to various enterprise systems. Read More

Apr
11
2016
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DataStax adds graph databases to enterprise Cassandra product set

Social network graph coming off a tablet. When DataStax acquired Aurelius, a graph database startup last year, it was clear it wanted to add graph database functionality to its DataStax Enterprise product, and today it achieved that goal when it announced the release of DataStax Enterprise Graph. The new enterprise graph product has been fully integrated into the DataStax Enterprise product set, giving customers an integrated… Read More

Oct
21
2015
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Neo Technology Prepares To Take On All Comers In Graph Database Market

World map showing a series of network connections. Neo Technology, the makers of the Neo4j graph database, may not be a household name, but you very likely access graph database technology on daily basis, whether you know it or not — and it’s a market that growing by leaps and bounds and getting the attention of some of the biggest names in the business. Graph databases make logical connections for you. It’s what helps… Read More

Feb
03
2015
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DataStax Grabs Aurelius In Graph Database Acqui-Hire

Graph made from US 100 dollar bill with arrow pointing up. DataStax scored a $106M funding pay day last September, and today it announced it was using some of that money to acquire open source graph database company, Aurelius, along with its engineering talent.
No terms were disclosed, but all eight Aurelius engineers will be joining DataStax immediately. The new team will begin working on what they are calling “a massively scalable graph… Read More

Jan
15
2015
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Neo Technology Bags $20M As Graph Databases Get Hot

Graph Databases book from O'Reilly. Neo Technology, developers of the neo4j graph database have been growing steadily, and investors have noticed, rewarding them with a $20M Series C pay day. Creandum led the round joined by Dawn Capital and current investors Fidelity Growth Partners Europe, Sunstone Capital and Conor Venture Partners. The company’s last funding round was in November, 2012 for $11M, and this latest… Read More

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