With a pandemic raging across many parts of the world, many companies have customer service agents spread out as well, creating a workforce management nightmare. It wasn’t easy to manage and route requests when CSAs were in one place, it’s even harder with many working from home.

To help answer that problem Salesforce is developing a new product called Service Cloud Workforce Engagement. Bill Patterson, EVP and General Manager for CRM Applications at Salesforce points out that with these workforces spread out, it’s a huge challenge for management to distribute work and keep up with customer volume, especially as customers have moved online during COVID.

“With Service Cloud Workforce Engagement, Salesforce will arm the contact center with a connected solution — all on one platform so our customers can remain resilient and agile no matter what tomorrow may bring,” Patterson said in a statement.

Like many Salesforce products, this one is made up of several key components to deliver a complete solution. For starters, there is Service Forecast for Customer 360, a tool that helps predict workforce requirements and uses AI to distribute customer service requests in a way that makes sense. This can help in planning at a time with a likely predictable uptick in service requests like Black Friday or Cyber Monday, or even those times when there is an unexpected spike.

Next up is Omnichannel Capacity Planning, which helps managers distribute CSAs across channels such as phone, messaging or email wherever they are needed most based on the demand across a given channel.

Finally, there is a teaching component that helps coach customer service agents to give the correct answer in the correct way for a given situation. “To increase agent engagement and performance, companies will be able to quickly onboard and continually train agents by delivering bite-size, guided learning paths directly in the agent’s workspace during their shift,” the company explained.

The company says that Service Cloud Workforce Engagement will be available in the first half of next year.

For much of its existence, Salesforce was a cloud service on its own with its own cloud resources available for its customers, but as the company and cloud computing in general has evolved, Salesforce has moved some of its workloads to other clouds like AWS, Azure and Google. Now, it wants to allow customers to do the same.

To help facilitate that, the company announced Hyperforce today at its Dreamforce customer conference, a new architecture designed from the ground up to help customers deliver workloads to the public cloud of choice.

The idea behind Hyperforce is to enable customers to take all of the data in what Salesforce calls Customer 360 — that’s the company’s detailed view of the customer across channels, Salesforce products and even other systems outside the Salesforce family — and be able to store that in whichever public cloud you want in whatever region you happen to operate. For now, they are in India and Germany, but there are plans to add support for 10 additional countries over the next year.

Company president and CTO Bret Taylor introduced the new approach. “We call this new capability Hyperforce. Simply put, we’ve been working to enable us to deliver Salesforce on public cloud infrastructure all around the world,” Taylor said.

Holger Mueller, an analyst at Constellation Research, says the underlying architecture running the Salesforce system is long overdue for an overhaul. At over 20 years old, it’s been around a long time now, but Mueller says that it’s about more than modernizing. “The pandemic requires SaaS vendors to move their offerings from their own data centers to [public cloud] data centers, so they can offer both architectural and commercial elasticity to their customers,” he said.

Mueller added that by bringing Salesforce data into the public cloud, besides the obvious data sovereignty issues it solves, it bring all of the advantages of using public cloud resources.

“Salesforce can now offer both architectural and commercial elasticity to their customers. Commercial elasticity matters a lot to CIOs and CTOs these days because when your business slows down, you pay less, and when your business accelerates, then you can afford to pay more,” he said. He says that Salesforce is bringing an early generation SaaS product and pulling it into the modern age, something that is imperative at this point in the company’s evolution.

But while moving forward, Taylor was careful to point out that they rebuilt the system in such a way as to be fully backwards compatible, so you don’t have to throw out all of the applications and investment you’ve made over the years, something that most companies couldn’t afford to do.”For you developers out there, This is the most remarkable thing. It is 100% backwards compatible, your apps will work with no changes and you can benefit from all of this automatically,” he said.

The company will be rolling out Hyperforce over the next year and beyond as it opens in more regions.

ServiceNow, the cloud-based IT services company, is making a significant acquisition today to fill out its longer-term strategy to be a big player in the worlds of automation and artificial intelligence for enterprises. It is acquiring Element AI, a startup out of Canada.

Founded by AI pioneers and backed by some of the world’s biggest AI companies — it raised hundreds of millions of dollars from the likes of Microsoft, Intel, Nvidia and Tencent, among others — Element AI’s aim was to build and provision AI-based IT services for enterprises, in many cases organizations that are not technology companies by nature.

Terms of the deal are not being disclosed, a spokesperson told TechCrunch, but we now have multiple sources telling us the price was around $500 million. For some context, Element AI was valued at between $600 million and $700 million when it last raised money, $151 million (or C$200 million at the time) in September 2019.

Even at $500 million, this deal would be ServiceNow’s biggest acquisition, although it would be a sizeable devaluation compared to the startup’s last price at fundraising.

A spokesperson confirmed that ServiceNow is making a full acquisition and will retain most of Element AI’s technical talent, including AI scientists and practitioners, but that it will be winding down its existing business after integrating what it wants and needs.

“Our focus with this acquisition is to gain technical talent and AI capabilities,” the spokesperson said. That will also include Element AI co-founder and CEO, JF Gagné, joining ServiceNow, and co-founder Dr. Yoshua Bengio taking on a role as technical advisor.

Those who are not part of those teams will be supported with severance or assistance in looking for other jobs within ServiceNow. A source estimated to us that this could affect around half of the organization.

The startup is headquartered in Montreal, and ServiceNow’s plan is to create an AI Innovation Hub based around that “to accelerate customer-focused AI innovation in the Now Platform.” (That is the brand name of its automation services.)

Last but not least, ServiceNow will start re-platforming some of Element AI’s capabilities, she said. “We expect to wind down most of Element AI’s customers after the deal is closed.”

The deal is the latest move for a company aiming to build a modern platform fit for our times.

ServiceNow, under CEO Bill McDermott (who joined in October 2019 from SAP), has been on a big investment spree in the name of bringing more AI and automation chops to the SaaS company. That has included a number of acquisitions this year, including Sweagle, Passage AI and Loom (respectively for $25 million, $33 million and $58 million), plus regular updates to its larger workflow automation platform.

ServiceNow has been around since 2004, so it’s not strictly a legacy business, but all the same, the publicly traded company, with a current market cap of nearly $103 billion, is vying to position itself as the go-to company for “digital transformation” — the buzz term for enterprise IT services this year, as everyone scrambles to do more online, in the cloud and remotely to continue operating through a global health pandemic and whatever comes in its wake.

“Technology is no longer supporting the business, technology is the business,” McDermott said earlier this year. In a tight market where it is completely plausible that Salesforce might scoop up Slack, ServiceNow is making a play for more tools to cover its own patch of the field.

“AI technology is evolving rapidly as companies race to digitally transform 20th century processes and business models,” said ServiceNow Chief AI Officer Vijay Narayanan, in a statement today. “ServiceNow is leading this once-in-a-generation opportunity to make work, work better for people. With Element AI’s powerful capabilities and world class talent, ServiceNow will empower employees and customers to focus on areas where only humans excel – creative thinking, customer interactions, and unpredictable work. That’s a smarter way to workflow.”

Element AI was always a very ambitious concept for a startup. Dr. Yoshua Bengio, winner of the 2018 Turing Award, who co-founded the company with AI expert Nicolas Chapados and Jean-François Gagné (Element AI’s CEO) alongside Anne Martel, Jean-Sebastien Cournoyer and Philippe Beaudoin, saw a gap in the market.

Their idea was to build AI services for businesses that were not tech companies in their DNA, but would still very much need to tap into the innovations of the tech world in order to continue growing and remaining competitive with said tech companies as the latter moved deeper into a wider range of industries and the companies themselves required increasing sophistication to operate and grow. They needed, in essence, to disrupt themselves before getting unceremoniously disrupted by someone else.

And on top of that, Element AI could work for and with the tech companies taking strategic investments in Element AI, as those investors wanted to tap some of that expertise themselves, as well as work with the startup to bring more services and win more deals in the enterprise. In addition to its four (sometimes fiercely competitive) investors, other backers included the likes of McKinsey.

Yet what form all of that would take was never completely clear.

When I covered the startup’s most recent tranche of funding last year, I noted that it wasn’t very forthcoming on who its customers actually were. Looking at its website, it still isn’t, although it does lay out several verticals where it aims to work. They include insurance, pharma, logistics, retail, supply chain, manufacturing, government and capital markets.

There were some other positive points. Element AI also played a strong ethics card with its AI For Good efforts, starting with work with Amnesty in 2018 and most recently Mozilla. Indeed, 2018 — a year after Element AI was founded — was also the year AI seemed to hit the mainstream consciousness — and also started to appear somewhat more creepy, with algorithmic misfires, pervasive facial recognition and more “automated” applications that didn’t work that well and so on — so launching an ethical aim definitely made sense.

But for all of that, it seems that there perhaps were not enough threads there to need a bigger cloth as a standalone business. Glassdoor reviews also speak of an endemic disorganization at the startup, which might not have helped, or was perhaps a sign of bigger issues.

“Element AI’s vision has always been to redefine how companies use AI to help people work smarter,” said Element AI founder and CEO, Jean-Francois Gagné in a statement. “ServiceNow is leading the workflow revolution and we are inspired by its purpose to make the world of work, work better for people. ServiceNow is the clear partner for us to apply our talent and technology to the most significant challenges facing the enterprise today.”

The acquisition is expected to be completed by early 2021.

DevOps continues to get a lot of attention as a wave of companies develop more sophisticated tools to help developers manage increasingly complex architectures and workloads. In the latest development, Databand — an AI-based observability platform for data pipelines, specifically to detect when something is going wrong with a datasource when an engineer is using a disparate set of data management tools — has closed a round of $14.5 million.

Josh Benamram, the CEO who co-founded the company with Victor Shafran and Evgeny Shulman, said that Databand plans include more hiring; to continue adding customers for its existing product; to expand the library of tools that it’s providing to users to cover an ever-increasing landscape of DevOps software, where it is a big supporter of open-source resources; as well as to invest in the next steps of its own commercial product. That will include more remediation once problems are identified: that is, in addition to identifying issues, engineers will be able to start automatically fixing them, too.

The Series A is being led by Accel with participation from Blumberg Capital, Lerer Hippeau, Ubiquity Ventures, Differential Ventures and Bessemer Venture Partners. Blumberg led the company’s seed round in 2018. It has now raised around $18.5 million and is not disclosing valuation.

The problem that Databand is solving is one that is getting more urgent and problematic by the day (as evidenced by this exponential yearly rise in zettabytes of data globally). And as data workloads continue to grow in size and use, they continue to become ever more complex.

On top of that, today there are a wide range of applications and platforms that a typical organization will use to manage source material, storage, usage and so on. That means when there are glitches in any one data source, it can be a challenge to identify where and what the issue can be. Doing so manually can be time-consuming, if not impossible.

“Our users were in a constant battle with ETL (extract transform load) logic,” said Benamram, who spoke to me from New York (the company is based both there and in Tel Aviv, and also has developers and operations in Kiev). “Users didn’t know how to organize their tools and systems to produce reliable data products.”

It is really hard to focus attention on failures, he said, when engineers are balancing analytics dashboards, how machine models are performing, and other demands on their time; and that’s before considering when and if a data supplier might have changed an API at some point, which might also throw the data source completely off.

And if you’ve ever been on the receiving end of that data, you know how frustrating (and perhaps more seriously, disastrous) bad data can be. Benamram said that it’s not uncommon for engineers to completely miss anomalies and for them to only have been brought to their attention by “CEO’s looking at their dashboards and suddenly thinking something is off.” Not a great scenario.

Databand’s approach is to use big data to better handle big data: it crunches various pieces of information, including pipeline metadata like logs, runtime info and data profiles, along with information from Airflow, Spark, Snowflake and other sources, and puts the resulting data into a single platform, to give engineers a single view of what’s happening and better see where bottlenecks or anomalies are appearing, and why.

There are a number of other companies building data observability tools — Splunk perhaps is one of the most obvious, but also smaller players like Thundra and Rivery. These companies might step further into the area that Databand has identified and is fixing, but for now Databand’s focus specifically on identifying and helping engineers fix anomalies has given it a strong profile and position.

Accel partner Seth Pierrepont said that Databand came to the VC’s attention in perhaps the best way it could: Accel needed a solution like it for its own internal work.

“Data pipeline observability is a challenge that our internal data team at Accel was struggling with. Even at our relatively small scale, we were having issues with the reliability of our data outputs on a weekly basis, and our team found Databand as a solution,” he said. “As companies in all industries seek to become more data driven, Databand delivers an essential product that ensures the reliable delivery of high-quality data for businesses. Josh, Victor and Evgeny have a wealth of experience in this area, and we’ve been impressed with their thoughtful and open approach to helping data engineers better manage their data pipelines with Databand.”

The company is also used by data teams from large Fortune 500 enterprises to smaller startups.