Newsletter #6. AI investment activity, a platform perspective

This week’s newsletter highlights investments in multiple data science platforms ($100M round of DataRobot, etc.), cybersecurity (Arctic Wolf’s $45M round, etc.), as well as AI startups working in manufacturing ($179M round of Bright Machines, etc.) and ones that are enabling smart cities ($65M invested in Carbon Lighthouse, and others).

If you want to get this newsletter in your mailbox — subscribe.

If you want to skip my analysis and jump straight to data, go to the end of the note to find a table and a link on a Google spreadsheet, that contains data on featured companies and links to sources.

Newsletter 22.10.2018–28.10.2018

Key themes

• AI automation — 3 companies build tools for data scientists, one of them is DataRobot, that raised a $100M round;

• Industry 4.0 — Bright Machines raised $179M to make manufacturing robots smarter;

• Smart City and Smart Home/Office — 4 companies that help with analysing foot traffic, managing office space, increasing energy efficiency, etc.;

• Cybersecurity — 5 startups that cover various aspects of security, e.g. APIs, networks, and others.

Drivers behind AI startups

• Process improvement via automation — Computer vision and drones help insurance companies to assess damage (BetterView ) and telcos to monitor infrastructure (Red Mountain Scientific). Bright Machines upgrades manufacturing robots and ‘is going from dumb, blind and costly robots to ones that are sensor rich, have computer vision, machine learning and are adaptable’;

• It’s interesting that the void to improve processes and complex systems by insights is noticeable in pretty much any industry. Placer.ai helps retailers to analyse foot traffic. While ToolSense promises tools manufacturers ‘valuable insights in contractor’s tool-usage based on the analysis of real-time sensor data’ (Pic. 1.);

Pic. 1. ToolSense’s offering

• Cybersecurity seems to be a very demanded piece of infrastructure. Arctic Wolf closed a $45M round while Versive and ZoneFox were acquired;

• Tools — AI startups try to automate data science routines in one way or another. DataRobot automates ‘the entire modelling lifecycle’ and makes ‘coding and machine learning skills are completely optional’. While Ople ‘delivers production-grade AI models in as little as minutes, already deployed and ready to make predictions’ (Pic.2).

Pic. 2. How Ople.ai DS platform works

Other startups focus on a certain element of the routine. For example, Infoworks ‘automates the creation and operation of big data workflows from source to consumption’.

Moreover, specialised data science tools emerge. For example, XtalPi partners with Amazon Web Services and Alibaba Cloud, and offers a platform that is ‘…optimized to deploy up to 1,000,000 cores of computing power for maximum security, scalability, flexibility, and efficiency’ of R&D;

• The need for AI-powered products — Blue Vision Labs, that wanted to bring ‘collaboration’ to the AR experiences (Pic. 3) in ‘…multi-player games, on-street navigation apps, social media applications and education’ was acquired by Lyft and became a part of its product. It’s likely Lyft will repurpose Blue Vision’s tech to ’…building the best maps at scale to support our [Lyft’s] autonomous vehicles, and then localization to support our stacks’ .

Pic. 3. Blue Vision Labs experience

Tech platforms

As usual, we have multiple data and algorithms platforms (see the Google spreadsheet for details), but I would love to highlight Carbon Lighthouse, that ‘… optimizes the controls governing the building equipment’ and in a sense is a platform for various sensors and systems. The company installs ‘… 50–300 sensors per building to gather more building-specific data’, then models ‘100s of 1000s equipment + controls scenarios to identify the best ROI solution’, deploy required systems, and then manages them accordingly.

Business platforms

I’ve identified four business platforms among AI startups that are featured in this newsletter:

• WireWheel deals with data privacy management in a very holistic way and builds workflows that allow not only manage data privacy internally, but also to collaborate with vendors and partners;

• VergeSense via its sensors and the platform serves real estate investors, property managers, and workplace tenants. Placer.ai also helps multiple types of users — retailers, shopping centres and brokers;

• ToolSense uses data from hand-operated power tools to help both, original equipment manufacturers (OEMs) and their users. OEMs ‘gain valuable insights in contractors’ tool-usage based on the analysis of real-time sensor data’, while their clients enjoy reduced maintenance and repair logistics.

More examples on building tech and business platforms as well as data on featured startups are below in the Chart 1.

Chart 1. AI companies that disclosed funding/exits during the week 22.10.2018–28.10.2018, $. Click to enlarge.

Data is here (Google spreadsheet)

***

All data is from open sources and all conclusions/ideas/analysis are built only on publicly available information. For data sources see the Google spreadsheet.

*A company is defined as a platform in tech sense if someone can build up on it. I identify a comapny as a tech platform purely based on public sources, so if I misunderstood your startup, please do let me know.

**A company is positioned across a value chain and is considered as a ‘business platform’ if it has distinctive offers for several elements of this value/supply chain. If I misunderstood the value proposition of your startup, please do let me know.

This newsletter does not intend to cover all AI transactions, but covers just four themes in a limited set of geographies.