Personal blog of Yzmir Ramirez

Data platform Splunk today announced that it has acquired two startups, Plumbr and Rigor, to build out its new Observability Suite, which is also launching today. Plumbr is an application performance monitoring service, while Rigor focuses on digital experience monitoring, using synthetic monitoring and optimization tools to help businesses optimize their end-user experiences. Both of these acquisitions complement the technology and expertise Splunk acquired when it bought SignalFx for over $1 billion last year.

Splunk did not disclose the price of these acquisitions, but Estonia-based Plumbr had raised about $1.8 million, while Atlanta-based Rigor raised a debt round earlier this year.

When Splunk acquired SignalFx, it said it did so in order to become a leader in observability and APM. As Splunk CTO Tim Tully told me, the idea here now is to accelerate this process.

Image Credits: Splunk

“Because a lot of our users and our customers are moving to the cloud really, really quickly, the way that they monitor [their] applications changed because they’ve gone to serverless and microservices a ton,” he said. “So we entered that space with those acquisitions, we quickly folded them together with these next two acquisitions. What Plumbr and Rigor do is really fill out more of the portfolio.”

He noted that Splunk was especially interested in Plumbr’s bytecode implementation and its real-user monitoring capabilities, and Rigor’s synthetics capabilities around digital experience monitoring (DEM). “By filling in those two pieces of the portfolio, it gives us a really amazing set of solutions because DEM was the missing piece for our APM strategy,” Tully explained.

Image Credits: Splunk

With the launch of its Observability Suite, Splunk is now pulling together a lot of these capabilities into a single product — which also features a new design that makes it stand apart from the rest of Splunk’s tools. It combines logs, metrics, traces, digital experience, user monitoring, synthetics and more.

“At Yelp, our engineers are responsible for hundreds of different microservices, all aimed at helping people find and connect with great local businesses,” said Chris Gordon, Technical Lead at Yelp, where his team has been testing the new suite. “Our Production Observability team collaborates with Engineering to improve visibility into the performance of key services and infrastructure. Splunk gives us the tools to empower engineers to monitor their own services as they rapidly ship code, while also providing the observability team centralized control and visibility over usage to ensure we’re using our monitoring resources as efficiently as possible.”

Directly, a startup whose mission is to help build better customer service chatbots by using experts in specific areas to train them, has raised more funding as it opens up a new front to grow its business: APIs and a partner ecosystem that can now also tap into its expert network. Today Directly is announcing that it has added $11 million to close out its Series B at $51 million (it raised $20 million back in January of this year, and another $20 million as part of the Series B back in 2018).

The funding is coming from Triangle Peak Partners and Toba Capital, while its previous investors in the round included strategic backers Samsung NEXT and Microsoft’s M12 Ventures (who are both customers, alongside companies like Airbnb), as well as Industry Ventures, True Ventures, Costanoa Ventures and Northgate. (As we reported when covering the initial close, Directly’s valuation at that time was at $110 million post-money, and so this would likely put it at $120 million or higher, given how the business has expanded.)

While chatbots have now been around for years, a key focus in the tech world has been how to help them work better, after initial efforts saw so many disappointing results that it was fair to ask whether they were even worth the trouble.

Directly’s premise is that the most important part of getting a chatbot to work well is to make sure that it’s trained correctly, and its approach to that is very practical: find experts both to troubleshoot questions and provide answers.

As we’ve described before, its platform helps businesses identify and reach out to “experts” in the business or product in question, collect knowledge from them, and then fold that into a company’s AI to help train it and answer questions more accurately. It also looks at data input and output into those AI systems to figure out what is working, and what is not, and how to fix that, too.

The information is typically collected by way of question-and-answer sessions. Directly compensates experts both for submitting information as well as to pay out royalties when their knowledge has been put to use, “just as you would in traditional copyright licensing in music,” its co-founder Antony Brydon explained to me earlier this year.

It can take as little as 100 experts, but potentially many more, to train a system, depending on how much the information needs to be updated over time. (Directly’s work for Xbox, for example, used 1,000 experts but has to date answered millions of questions.)

Directly’s pitch to customers is that building a better chatbot can help deflect more questions from actual live agents (and subsequently cut operational costs for a business). It claims that customer contacts can be reduced by up to 80%, with customer satisfaction by up to 20%, as a result.

What’s interesting is that now Directly sees an opportunity in expanding that expert ecosystem to a wider group of partners, some of which might have previously been seen as competitors. (Not unlike Amazon’s AI powering a multitude of other businesses, some of which might also be in the market of selling the same services that Amazon does).

The partner ecosystem, as Directly calls it, use APIs to link into Directly’s platform. Meya, Percept.ai, and SmartAction — which themselves provide a range of customer service automation tools — are three of the first users.

“The team at Directly have quickly proven to be trusted and invaluable partners,” said Erik Kalviainen, CEO at Meya, in a statement. “As a result of our collaboration, Meya is now able to take advantage of a whole new set of capabilities that will enable us to deliver automated solutions both faster and with higher resolution rates, without customers needing to deploy significant internal resources. That’s a powerful advantage at a time when scale and efficiency are key to any successful customer support operation.”

The prospect of a bigger business funnel beyond even what Directly was pulling in itself is likely what attracted the most recent investment.

“Directly has established itself as a true leader in helping customers thrive during these turbulent economic times,” said Tyler Peterson, Partner at Triangle Peak Partners, in a statement. “There is little doubt that automation will play a tremendous role in the future of customer support, but Directly is realizing that potential today. Their platform enables businesses to strike just the right balance between automation and human support, helping them adopt AI-powered solutions in a way that is practical, accessible, and demonstrably effective.”

In January, Mike de la Cruz, who took over as CEO at the time of the funding announcement, said the company was gearing up for a larger Series C in 2021. It’s not clear how and if that will be impacted by the current state of the world. But in the meantime, as more organizations are looking for ways to connect with customers outside of channels that might require people to physically visit stores, or for employees to sit in call centres, it presents a huge opportunity for companies like this one.

“At its core, our business is about helping customer support leaders resolve customer issues with the right mix of automation and human support,” said de la Cruz in a statement. “It’s one thing to deliver a great product today, but we’re committed to ensuring that our customers have the solutions they need over the long term. That means constantly investing in our platform and expanding our capabilities, so that we can keep up with the rapid pace of technological change and an unpredictable economic landscape. These new partnerships and this latest expansion of our recent funding round have positioned us to do just that. We’re excited to be collaborating with our new partners, and very thankful to all of our investors for their support.”

Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI.

Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one’s job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.

Arguably, AI’s highest use case currently is to add futuristic sheen to traditional security tools, rebranding timeworn approaches as trailblazing sorcery that will revolutionize enterprise cybersecurity as we know it. The current hype cycle for AI appears to be the roaring, ferocious crest at the end of a decade that began with bubbly excitement around the promise of “big data” in information security.

But what lies beneath the marketing gloss and quixotic lust for an AI revolution in security? How did AL ascend to supplant the lustrous zest around machine learning (“ML”) that dominated headlines in recent years? Where is there true potential to enrich information security strategy for the better – and where is it simply an entrancing distraction from more useful goals? And, naturally, how will attackers plot to circumvent security AI to continue their nefarious schemes?

How did AI grow out of this stony rubbish?

The year AI debuted as the “It Girl” in information security was 2017. The year prior, MIT completed their study showing “human-in-the-loop” AI out-performed AI and humans individually in attack detection. Likewise, DARPA conducted the Cyber Grand Challenge, a battle testing AI systems’ offensive and defensive capabilities. Until this point, security AI was imprisoned in the contrived halls of academia and government. Yet, the history of two vendors exhibits how enthusiasm surrounding security AI was driven more by growth marketing than user needs.