Lucidworks raises $100M to expand in AI-powered search-as-a-service for organizations

If the sheer amount of information that we can tap into using the internet has made the world our oyster, then the huge success of Google is a testament to how lucrative search can be in helping to light the way through that data maze.

Now, in a sign of the times, a startup called Lucidworks, which has built an AI-based engine to help individual organizations provide personalised search services for their own users, has raised $100 million in funding. Lucidworks believes its approach can produce better and more relevant results than other search services in the market, and it plans to use the funding for its next stage of growth to become, in the words of CEO Will Hayes, “the world’s next important platform.”

The funding is coming from PE firm Francisco Partners​ and ​TPG Sixth Street Partners​. Existing investors in the company include Top Tier Capital Partners, Shasta Ventures, Granite Ventures and Allegis Cyber.

Lucidworks has raised around $200 million in funding to date, and while it is not disclosing the valuation, the company says it been doubling revenues each year for the last three and counts companies like Reddit, Red Hat, REI, the US Census among some 400 others among its customers using its flagship product, Fusion. PitchBook notes that its last round in 2018 was at a modest $135 million, and my guess is that is up by quite some way.

The idea of building a business on search, of course, is not at all new, and Lucidworks works in a very crowded field. The likes of Amazon, Google and Microsoft have built entire empires on search — in Google’s and Microsoft’s case, by selling ads against those search results; in Amazon’s case, by generating sales of items in the search results — and they have subsequently productised that technology, selling it as a service to others.

Alongside that are companies that have been building search-as-a-service from the ground up — like Elastic, Sumo Logic and Splunk (whose founding team, coincidentally, went on to found Lucidworks…) — both for back-office processes as well as for services that are customer-facing.

In an interview, Hayes said that what sets Lucidworks apart is how it uses machine learning and other AI processes to personalise those results after “sorting through mountains of data”, to provide enterprise information to knowledge workers, shopping results on an e-commerce site to consumers, data to wealth managers, or whatever it is that is being sought.

Take the case of a shopping experience, he said by way of explanation. “If I’m on REI to buy hiking shoes, I don’t just want to see the highest-rated hiking shoes, or the most expensive,” he said.

The idea is that Lucidworks builds algorithms that bring in other data sources — your past shopping patterns, your location, what kind of walking you might be doing, what other people like you have purchased — to produce a more focused list of products that you are more likely to buy.

“Amazon has no taste,” he concluded, a little playfully.

Today, around half of Lucidworks’ business comes from digital commerce and digital content — searches of the kind described above for products, or monitoring customer search queries sites like RedHat or Reddit — and half comes from knowledge worker applications inside organizations.

The plan will be to continue that proportion, while also adding in other kinds of features — more natural language processing and more semantic search features — to expand the kinds of queries that can be made, and also cues that Fusion can use to produce results.

Interestingly, Hayes said that while it’s come up a number of times, Lucidworks doesn’t see itself ever going head-to-head with a company like Google or Amazon in providing a first-party search platform of its own. Indeed, that may be an area that has, for the time being at least, already been played out. Or it may be that we have turned to a time when walled gardens — or at least more targeted and curated experiences — are coming into their own.

“We still see a lot of runway in this market,” said Jonathan Murphy of Francisco Partners. “We were very attracted to the idea of next-generation search, on one hand serving internet users facing the pain of the broader internet, and on the other enterprises as an enterprise software product.” 

Lucidworks, it seems, has also entertained acquisition approaches, although Hayes declined to get specific about that. The longer-term goal, he said, “is to build something special that will stay here for a long time. The likelihood of needing that to be a public company is very high, but we will do what we think is best for the company and investors in the long run. But our focus and intention is to continue growing.”


By Ingrid Lunden

RealityEngines.AI raises $5.25M seed round to make ML easier for enterprises

RealityEngines.AI, a research startup that wants to help enterprises make better use of AI, even when they only have incomplete data, today announced that it has raised a $5.25 million seed funding round. The round was led by former Google CEO and Chairman Eric Schmidt and Google founding board member Ram Shriram. Khosla Ventures, Paul Buchheit, Deepchand Nishar, Elad Gil, Keval Desai, Don Burnette and others also participated in this round.

The fact that the service was able to raise from this rather prominent group of investors clearly shows that its overall thesis resonates. The company, which doesn’t have a product yet, tells me that it specifically wants to help enterprises make better use of the smaller and noisier datasets they have and provide them with state-of-the-art machine learning and AI systems that they can quickly take into production. It also aims to provide its customers with systems that can explain their predictions and are free of various forms of bias, something that’s hard to do when the system is essentially a black box.

As RealityEngines CEO Bindu Reddy, who was previously the head of products for Google Apps, told me the company plans to use the funding to build out its research and development team. The company, after all, is tackling some of the most fundamental and hardest problems in machine learning right now — and that costs money. Some, like working with smaller datasets, already have some available solutions like generative adversarial networks that can augment existing datasets and that RealityEngines expects to innovate on.

Reddy is also betting on reinforcement learning as one of the core machine learning techniques for the platform.

Once it has its product in place, the plan is to make it available as a pay-as-you-go managed service that will make machine learning more accessible to large enterprise, but also to small and medium businesses, which also increasingly need access to these tools to remain competitive.


By Frederic Lardinois

Automation Hero picks up $14.5 million led by Atomico

Automation Hero, formerly SalesHero, has secured $14.5 million in new funding led by Atomico, with participation by Baidu Ventures and Cherry Ventures. As part of the deal, Atomico principle Ben Blume will join the company’s board of directors.

The automation startup launched in 2017 as SalesHero, giving sales orgs a simple way to automate back office processes like filing an expense report or updating the CRM. It does this through an AI assistant called Robin — “Batman and Robin, it worked with the superhero theme, and it’s gender neutral,” cofounder and CEO Stefan Groschupf explained — that can be configured to go through the regular workflow and take care of repetitive tasks.

“We brought computers into the workplace because we believed they could make us more productive,” said Groschupf. “But in many companies, people spend a lot of time entering data and doing painful manual processes to make these machines happy.”

The idea was to give salespeople more time to actually do their job, which is selling to clients. If all the administrative and repetitive ‘paperwork’ is done by a computer, human employees can become more productive and efficient at skilled tasks.

By weaving together click robots, Automation Hero users can build out their own workflows through a no-code interface, tying together a wide variety of both structured and unstructured data sources. Those workflows are then presented in the inbox each morning by Robin, the AI assistant, and are executed as soon as the user gives the go ahead.

After launch, the team realized that other types of organizations, beyond sales departments, were building out automations. Insurance firms, in particular, were using the software to automate some of the repetitive tasks involved with filing and assessing claims.

This led to today’s rebrand to Automation Hero.

Groschupf said that by automating the process of filling out a single closing form, it saved one insurance firm’s 430 sales reps 18.46 years per year.

Automation Hero has now raised a total of $19 million.

“We’re really excited with Atomico to bring on a great VC and good people,” said Groschupf. “I’ve raised capital before and I’ve worked with some of the more questionable VCs, as it turns out. We’re super excited we’ve found an investor that really bakes important things, like a diversity policy and a family leave policy, right into the company’s investment agreement.”

Though he didn’t confirm, it’s likely that Groschupf is referring to KPCB, which has run into its fair share of controversy over the past few years and was an investor in Groschupf’s previous startup, Datameer.


By Jordan Crook

Databricks raises $250M at a $2.75B valuation for its analytics platform

Databricks, the company behind the Apache Spark big data analytics engine, today announced that it has raised a $250 million Series E round led by Andreessen Horowitz. Coatue Management, Microsoft and NEA, also participated in this round, which brings the company’s total funding to $498.5 million. Microsoft’s involvement here is probably a bit of a surprise, but it’s worth noting that it also worked with Databricks on the launch of Azure Databricks as a first-party service on the platform, something that’s still a rarity in the Azure cloud.

As Databricks also today announced, its annual recurring revenue now exceeds $100 million. The company didn’t share whether it’s cash flow-positive at this point, but Databricks CEO and co-founder Ali Ghodsi shared that the company’s valuation is now $2.75 billion.

Current customers, which the company says number around 2,000, include the likes of Nielsen, Hotels.com, Overstock, Bechtel, Shell and HP.

While Databricks is obviously known for its contributions to Apache Spark, the company itself monetizes that work by offering its Unified Analytics platform on top of it. This platform allows enterprises to build their data pipelines across data storage systems and prepare data sets for data scientists and engineers. To do this, Databricks offers shared notebooks and tools for building, managing and monitoring data pipelines, and then uses that data to build machine learning models, for example. Indeed, training and deploying these models is one of the company’s focus areas these days, which makes sense, given that this is one of the main use cases for big data, after all.

On top of that, Databricks also offers a fully managed service for hosting all of these tools.

“Databricks is the clear winner in the big data platform race,” said Ben Horowitz, co-founder and general partner at Andreessen Horowitz, in today’s announcement. “In addition, they have created a new category atop their world-beating Apache Spark platform called Unified Analytics that is growing even faster. As a result, we are thrilled to invest in this round.”

Ghodsi told me that Horowitz was also instrumental in getting the company to re-focus on growth. The company was already growing fast, of course, but Horowitz asked him why Databricks wasn’t growing faster. Unsurprisingly, given that it’s an enterprise company, that means aggressively hiring a larger sales force — and that’s costly. Hence the company’s need to raise at this point.

As Ghodsi told me, one of the areas the company wants to focus on is the Asia Pacific region, where overall cloud usage is growing fast. The other area the company is focusing on is support for more verticals like mass media and entertainment, federal agencies and fintech firms, which also comes with its own cost, given that the experts there don’t come cheap.

Ghodsi likes to call this “boring AI,” since it’s not as exciting as self-driving cars. In his view, though, the enterprise companies that don’t start using machine learning now will inevitably be left behind in the long run. “If you don’t get there, there’ll be no place for you in the next 20 years,” he said.

Engineering, of course, will also get a chunk of this new funding, with an emphasis on relatively new products like MLFlow and Delta, two tools Databricks recently developed and that make it easier to manage the life cycle of machine learning models and build the necessary data pipelines to feed them.


By Frederic Lardinois

Former Facebook engineer picks up $15M for AI platform Spell

In 2016, Serkan Piantino packed up his desk at Facebook with hopes to move on to something new. The former Director of Engineering for Faceboook AI Research had every intention to keep working on AI, but quickly realized a huge issue.

Unless you’re under the umbrella of one of these big tech companies like Facebook, it can be very difficult and incredibly expensive to get your hands on the hardware necessary to run machine learning experiments.

So he built Spell, which today received $15 million in Series A funding led by Eclipse Ventures and Two Sigma Ventures.

Spell is a collaborative platform that lets anyone run machine learning experiments. The company connects clients with the best, newest hardware hosted by Google, AWS and Microsoft Azure and gives them the software interface they need to run, collaborate, and build with AI.

“We spent decades getting to a laptop powerful enough to develop a mobile app or a website, but we’re struggling with things we develop in AI that we haven’t struggled with since the 70s,” said Piantino. “Before PCs existed, the computers filled the whole room at a university or NASA and people used terminals to log into a single main frame. It’s why Unix was invented, and that’s kind of what AI needs right now.”

In a meeting with Piantino this week, TechCrunch got a peek at the product. First, Piantino pulled out his MacBook and opened up Terminal. He began to run his own code against MNIST, which is a database of handwritten digits commonly used to train image detection algorithms.

He started the program and then moved over to the Spell platform. While the original program was just getting started, Spell’s cloud computing platform had completed the test in under a minute.

The advantage here is obvious. Engineers who want to work on AI, either on their own or for a company, have a huge task in front of them. They essentially have to build their own computer, complete with the high-powered GPUs necessary to run their tests.

With Spell, the newest GPUs from NVIDIA and Google are virtually available for anyone to run their test.

Individual users can get on for free, specify the type of GPU they need to compute their experiment, and simply let it run. Corporate users, on the other hand, are able to view the runs taking place on Spell and compare experiments, allowing users to collaborate on their projects from within the platform.

Enterprise clients can set up their own cluster, and keep all of their programs private on the Spell platform, rather than running tests on the public cluster.

Spell also offers enterprise customers a ‘spell hyper’ command that offers built-in support for hyperparameter optimization. Folks can track their models and results and deploy them to Kubernetes/Kubeflow in a single click.

But, perhaps most importantly, Spell allows an organization to instantly transform their model into an API that can be used more broadly throughout the organization, or or used directly within an app or website.

The implications here are huge. Small companies and startups looking to get into AI now have a much lower barrier to entry, whereas large traditional companies can build out their own proprietary machine learning algorithms for use within the organization without an outrageous upfront investment.

Individual users can get on the platform for free, whereas enterprise clients can get started for $99/month per host you use over the course of a month. Piantino explains that Spell charges based on concurrent usage, so if the customer has 10 concurrent things running, the company considers that the ‘size’ of the Spell cluster and charges based on that.

Piantino sees Spell’s model as the key to defensibility. Whereas many cloud platforms try to lock customers in to their entire suite of products, Spell works with any language framework and lets users plug and play on the platforms of their choice by simply commodifying the hardware. In fact, Spell doesn’t even share with clients which cloud cluster (Microsoft Azure, Google, or AWS) they’re on.

So, on the one hand the speed of the tests themselves goes up based on access to new hardware, but, because Spell is an agnostic platform, there is also a huge advantage in how quickly one can get set up and start working.

The company plans to use the funding to further grow the team and the product, and Piantino says he has his eye out for top-tier engineering talent as well as a designer.


By Jordan Crook

TechSee nabs $16M for its customer support solution built on computer vision and AR

Chatbots and other AI-based tools have firmly found footing in the world of customer service, used either to augment or completely replace the role of a human responding to questions and complaints, or (sometimes, annoyingly, at the same time as the previous two functions) sell more products to users.

Today, an Israeli startup called TechSee is announcing $16 million in funding to help build out its own twist on that innovation: an AI-based video service, which uses computer vision, augmented reality and a customer’s own smartphone camera to provide tech support to customers, either alongside assistance from live agents, or as part of a standalone customer service ‘bot.’

Led by Scale Venture Partners — the storied investor that has been behind some of the bigger enterprise plays of the last several years (including Box, Chef, Cloudhealth, DataStax, Demandbase, DocuSign, ExactTarget, HubSpot, JFrog, and fellow Israeli AI assistance startup WalkMe) the Series B also includes participation from Planven Investments, OurCrowd, Comdata Group and Salesforce Ventures. (Salesforce was actually announced as a backer in October.)

The funding will be used both to expand the company’s current business as well as move into new product areas like sales.

Eitan Cohen, the CEO and co-founder, said that the company today provides tools to some 15,000 customer service agents and counts companies like Samsung and Vodafone among its customers across verticals like financial services, tech, telecoms and insurance.

The potential opportunity is big: Cohen estimates that there are about 2 million customer service agents in the US, and about 14 million globally.

TechSee is not disclosing its valuation. It has raised around $23 million to date.

While TechSee provides support for software and apps, its sweet spot up to now has been providing video-based assistance to customers calling with questions about the long tail of hardware out in the world, used for example in a broadband home WiFi service.

In fact, Cohen said he came up with the idea for the service when his parents phoned him up to help them get their cable service back up, and he found himself challenged to do it without being able to see the set top box to talk them through what to do.

So he thought about all the how-to videos that are on platforms like YouTube and decided that there was an opportunity to harness that in a more organised way for the companies providing an increasing array of kit that may never get the vlogger treatment.

“We are trying to bring that YouTube experience for all hardware,” he said in an interview.

The thinking is that this will become a bigger opportunity over time as more services get digitised, the cost of components continues to come down and everything becomes “hardware.”

“Tech may become more of a commodity, but customer service does not,” he added. “Solutions like ours allow companies to provide low-cost technology without having to hire more people to solve issues [that might arise with it.]”

The product today is sold along two main trajectories: assisting customer reps; and providing unmanned video assistance to replace some of the easier and more common questions that get asked.

In cases where live video support is provided, the customer opts in for the service, similar to how she or he might for a support service that “takes over” the device in question to diagnose and try to fix an issue. Here, the camera for the service becomes a customer’s own phone.

Over time, that live assistance is used in two ways that are directly linked to TechSee’s artificial intelligence play. First, it helps to build up TechSee’s larger back catalogue of videos, where all identifying characteristics removed with the focus solely on the device or problem in question. Second, the experience in the video is also used to build TechSee’s algorithms for future interactions. Cohen said that there are now “millions” of media files — images and videos — now in the company’s catalogue.

The effectiveness of its system so far has been pretty impressive. TechSee’s customers — the companies running the customer support — say they have on average seen a 40 percent increase in customer satisfaction (NPS scores), a 17 percent decrease in technician dispatches, between 20 and 30 percent increase in first call resolutions, depending on the industry.

TechSee is not the only company that has built a video-based customer engagement platform: others include Stryng, CallVU and Vee24. And you could image companies like Amazon — which is already dabbling in providing advice to customers based on what its Echo Look can see — might be interested in providing such services to users across the millions of products that it sells, as well as provide that as a service to third parties.

According to Cohen, What TechSee has going for it compared to those startups, and also the potential entry of companies like Microsoft or Amazon into the mix, is a headstart on raw data and a vision of how it will be used by the startup’s AI to build the business.

“We believe that anyone who wants to build this would have a challenge making it from scratch,” he said. “This is where we have strong content, millions of images, down to specific model numbers, where we can provide assistance and instructions on the spot.”

Salesforce’s interest in the company, he said, is a natural progression of where that data and customer relationship can take a business beyond responsive support into areas like quick warranty verification (for all those times people have neglected to do a product registration), snapping fender benders for insurance claims, and of course upselling to other products and services.

“Salesforce sees the synergies between the sales cloud and the service cloud,” Cohen said.

“TechSee recognized the great potential for combining computer vision AI with augmented reality in customer engagement,” said Andy Vitus, Partner at Scale Venture Partners, who joins the board with this round. “Electronic devices become more complex with every generation, making their adoption a perennial challenge. TechSee is solving a massive problem for brands with a technology solution that simplifies the customer experience via visual and interactive guidance.”


By Ingrid Lunden

‘Software robot’ startup UiPath expands Series C to $265M at a $3B valuation

UiPath, a startup that works in the growing area of RPA, or robotic process automation — where AI-based software is used to help businesses run repetitive or mundane back-office tasks, to free up humans to tackle more sophisticated work — has raised money for the third time this year. The company is today announcing that it has closed out its Series C at $265 million — $40 million higher than the amount it said it was aiming for two months ago.

UiPath is now disclosing new investors in the round — namely, IVP, Madrona Venture Group and Meritech Capital — plus secondary sales for employees to give them liquidity, which made up the difference. The company has confirmed to me that the transactions were done at the same valuation as the rest of the Series C, at $3 billion. The Series C is still led by CapitalG and Sequoia Capital as before.

For some context, earlier this year, the company also raised a Series B of $153 million at a $1.1 billion valuation.

UiPath’s strong valuation hike and the rapid pace of its funding come at a time when both the company and its rivals are all growing quickly, as enterprises rush to capitalise on the rise of artificial intelligence in the workplace. In the case of RPA, the promise is that it will help bring down the cost of doing business and improve organizations’ efficiency. UiPath’s mantra is to provide “one robot for every person,” essentially doubling a company’s workforce without the need to hire more people.

UiPath says that its current annual run rate is now $150 million, up from a $100 million ARR figure it put out just two months ago, with customers now numbering at 2,100 and including the US Army, Defense Logistics Agency, GSA, IRS, NASA, Navy, and the Department of Veterans Affairs. One source at the company tells me that it’s getting approached “almost daily” for more funding at the moment.

At the same time, the competitive landscape is most definitely heating up. We’ve heard that Automation Anywhere, which also just raised money — $250 million — earlier this year, may also be looking to raise more (we’re looking into it). And just earlier this week, we reported that another RPA player, Kofax, acquired a division of Nuance for $400 million to ramp up its image processing business.

“I am honored to have IVP, Madrona Venture Group and Meritech Capital as new investors in UiPath. Their leadership and guidance will no doubt help us continue to define and lead the Automation First era for customers everywhere. UiPath has had many funding options and I believe we have selected the investors that align best with our culture and beliefs. I am humbled as the syndicate of unquestionably top-tier venture capital firms who believe in UiPath and support our future,” said UiPath CEO and co- founder Daniel Dines said in a statement. “Additionally, it is a core UiPath principle to share the success of the company in a meaningful way with our hard-working and long-time employees and we were excited to be able to extend the opportunity, at their personal choice, to realize partial liquidity in this round.”

Updated with clarification about the employee liquidity sales and new investor names.


By Ingrid Lunden

Customer service ‘behavioral pairing’ startup Afiniti quietly raised $130M at a $1.6B valuation

Artificial intelligence touches just about every aspect of the tech world these days, aiming to provide new ways of making old processes work better. Now, a startup that has built an AI platform that tackles the ever-present, but never-perfect, business of customer service has quietly raised a large round of funding as it gears up for its next act, an IPO. Afiniti, which uses machine learning and behavioral science to better match customers with customer service agents — “behavioral pairing” is how it describes the process — has closed a $130 million round of funding ($75 million cash, $60 million debt) — a Series D that Afiniti CEO Zia Chishti says values his company at $1.6 billion.

If you are not familiar with the name Afiniti, you might not be alone. The company has been relatively under the radar, in part because it has never made much of an effort to publicise itself, and in part because the funding that it has raised up to now has largely been from outside the hive of VCs that swarm around many other startup deals that push those startups into the limelight.

At the same time, its backers make for a pretty illustrious list. This latest round includes former Verizon CEO Ivan SeidenbergFred Ryan, the CEO and publisher of the Washington Post; and investors Global Asset ManagementThe Resource Group (which Chishti helped found), Zeke Capitalas well as unnamed Australian investors.

The previous Series C round of $26.5 million, also has an interesting list of backers and also was not widely reported. They included McKinsey & Company, Elisabeth Murdoch, former Thomson Reuters CEO Tom Glocer, and former BP CEO John Browne, alongside Global Asset Management, The Resource Group, Seidenberg and Ryan.

That Series C was at a $100 million valuation, meaning that Afiniti’s valuation has increased more than 10 times in the last year on the back of 100 percent revenue growth each year over the last five.

That momentum led the company also to file confidentially for an IPO — although ultimately Chishti told TechCrunch that the company decided to raise privately at the potential IPO valuation since the money was easy to come by. (It’s also been one of the reasons he said he’s also rebuffed acquisitions, although at least one of the companies that’s approached him, McKinsey, now an investor.)

Now, Chishti — who is a repeat entrepreneur, with his previous company, Align Technology (which makes teeth alignment alternatives to braces), now at a $24 billion market cap — said that Afiniti has started to tip into profitability, so it seems the prospect of an IPO might be back on the table. That is possibly one reason that the company has started to speak to the press more and to make itself more visible.

Chishti and Afiniti are based out of the US, but it has roots into a range of local businesses globally in part by way of its well-connected team of advisors and local leaders. Among them, Princess Beatrice (or Beatrice York), currently 8th in line to the throne to succeed Queen Elizabeth, is the company’s vice president of partnerships. Alonso Aznar, the son of the former prime minister of Spain, runs Afiniti’s operations in Madrid.

The company itself sits in the general area of CRM, and specifically among that wave of startups that are trying to build tools using AI and other new technology to improve on the old ways of getting things done (it’s not alone: just today we noted that People.ai raised $30 million for its own AI-based CRM tools).

Afiniti on one hand calls itself a traditional AI company, but on the other, its CEO laments how overused and hackneyed the term has become. “AI is just a bubble,” he said in an interview. “The intensity of interest in AI is unwarranted because nothing has changed. It’s the same algorithms and software, and we just have faster hardware now.”

In actual fact, what Afiniti does is supply an AI layer to a process that is otherwise “ninety-nine percent human”, in the words of Chishti. The company uses AI to analyse sales people’s performance with specific types of calls and situations, and also to analyse customers in terms of their previous interactions with a company. It then matches up customer service reps who it believes will be most compatible with specific customers.

Afiniti’s pricing model has been an important lever for getting its foot in the door with companies. The company does not price its service per-seat or even per-month, but on a calculation between how well the company does when its call routing and running through Afiniti, versus how much is sold when it does not.

“We run systems on for 15 minutes, off for 5 minutes, and we do that perpetually,” Chishti said. It integrates with a company’s CRM, sales and telephony systems at the back end, in order both to route calls but also to track when those calls result in a sale. “We count the revenues, calculate the delta, and we get a share of that delta.”

If that sounds like a tricky measure, it doesn’t to customers, it seems. The zero-cost-to-try-it model is how it has surmounted the hurdle of getting used by a number of large, often slow-moving carriers and other large incumbents. “It means we have to continuously prove our value,” Chishti added.

As one example of how this works out, he used the example of Verizon (which is the owner of TechCrunch, by way of Oath). “Say Verizon makes $120 billion in revenues in a year,” he said, “and $30 billion of that is in phone-based sales. Afiniti would make $600 million on that.” Times that by dozens of customers in 22 countries, and that may point to how the company has quietly reached the valuation that it has.

Beyond its core product, the company has dozens of patents and more in the application phase in the US and other jurisdictions.


By Ingrid Lunden

IBM launches cloud tool to detect AI bias and explain automated decisions

IBM has launched a software service that scans AI systems as they work in order to detect bias and provide explanations for the automated decisions being made — a degree of transparency that may be necessary for compliance purposes not just a company’s own due diligence.

The new trust and transparency system runs on the IBM cloud and works with models built from what IBM bills as a wide variety of popular machine learning frameworks and AI-build environments — including its own Watson tech, as well as Tensorflow, SparkML, AWS SageMaker, and AzureML.

It says the service can be customized to specific organizational needs via programming to take account of the “unique decision factors of any business workflow”.

The fully automated SaaS explains decision-making and detects bias in AI models at runtime — so as decisions are being made — which means it’s capturing “potentially unfair outcomes as they occur”, as IBM puts it.

It will also automatically recommend data to add to the model to help mitigate any bias that has been detected.

Explanations of AI decisions include showing which factors weighted the decision in one direction vs another; the confidence in the recommendation; and the factors behind that confidence.

IBM also says the software keeps records of the AI model’s accuracy, performance and fairness, along with the lineage of the AI systems — meaning they can be “easily traced and recalled for customer service, regulatory or compliance reasons”.

For one example on the compliance front, the EU’s GDPR privacy framework references automated decision making, and includes a right for people to be given detailed explanations of how algorithms work in certain scenarios — meaning businesses may need to be able to audit their AIs.

The IBM AI scanner tool provides a breakdown of automated decisions via visual dashboards — an approach it bills as reducing dependency on “specialized AI skills”.

However it is also intending its own professional services staff to work with businesses to use the new software service. So it will be both selling AI, ‘a fix’ for AI’s imperfections, and experts to help smooth any wrinkles when enterprises are trying to fix their AIs… Which suggests that while AI will indeed remove some jobs, automation will be busy creating other types of work.

Nor is IBM the first professional services firm to spot a business opportunity around AI bias. A few months ago Accenture outed a fairness tool for identifying and fixing unfair AIs.

So with a major push towards automation across multiple industries there also looks to be a pretty sizeable scramble to set up and sell services to patch any problems that arise as a result of increasing use of AI.

And, indeed, to encourage more businesses to feel confident about jumping in and automating more. (On that front IBM cites research it conducted which found that while 82% of enterprises are considering AI deployments, 60% fear liability issues and 63% lack the in-house talent to confidently manage the technology.)

In additional to launching its own (paid for) AI auditing tool, IBM says its research division will be open sourcing an AI bias detection and mitigation toolkit — with the aim of encouraging “global collaboration around addressing bias in AI”.

“IBM led the industry in establishing trust and transparency principles for the development of new AI technologies. It’s time to translate principles into practice,” said David Kenny, SVP of cognitive solutions at IBM, commenting in a statement. “We are giving new transparency and control to the businesses who use AI and face the most potential risk from any flawed decision making.”


By Natasha Lomas

Microsoft launches new AI applications for customer service and sales

Like virtually every other major tech company, Microsoft is currently on a mission to bring machine learning to all of its applications. It’s no surprise then that it’s also bringing ‘AI’ to its highly profitable Dynamics 365 CRM products. A year ago, the company introduced its first Dynamics 365 AI solutions and today it’s expanding this portfolio with the launch of three new products: Dynamics 365 AI for Sales, Customer Service and Market Insights.

“Many people, when they talk about CRM, or ERP of old, they referred to them as systems of oppression, they captured data,” said Alysa Taylor, Microsoft corporate VP for business applications and industry. “But they didn’t provide any value back to the end user — and what that end user really needs is a system of empowerment, not oppression.”

It’s no secret that few people love their CRM systems (except for maybe a handful of Dreamforce attendees), but ‘system of oppression’ is far from the ideal choice of words here. Yet Taylor is right that early systems often kept data siloed. Unsurprisingly, Microsoft argues that Dynamics 365 does not do that, allowing it to now use all of this data to build machine learning-driven experiences for specific tasks.

Dynamics 365 AI for Sales, unsurprisingly, is meant to help sales teams get deeper insights into their prospects using sentiment analysis. That’s obviously among the most basic of machine learning applications these days, but AI for Sales also helps these salespeople understand what actions they should take next and which prospects to prioritize. It’ll also help managers coach their individual sellers on the actions they should take.

Similarly, the Customer Service app focuses on using natural language understanding to understand and predict customer service problems and leverage virtual agents to lower costs. Taylor used this part of the announcement to throw some shade at Microsoft’s competitor Salesforce. “Many, many vendors offer this, but they offer it in a way that is very cumbersome for organizations to adopt,” she said. “Again, it requires a large services engagement, Salesforce partners with IBM Watson to be able to deliver on this. We are now out of the box.”

Finally, Dynamics 365 AI for Market Insights does just what the name implies: it provides teams with data about social sentiment, but this, too, goes a bit deeper. “This allows organizations to harness the vast amounts of social sentiment, be able to analyze it, and then take action on how to use these insights to increase brand loyalty, as well as understand what newsworthy events will help provide different brand affinities across an organization,” Taylor said. So the next time you see a company try to gin up some news, maybe it did so based on recommendations from Office 365 AI for Market Insights.


By Frederic Lardinois

Microsoft and DJI team up to bring smarter drones to the enterprise

At the Microsoft Build developer conference today, Microsoft and Chinese drone manufacturer DJI announced a new partnership that aims to bring more of Microsoft’s machine learning smarts to commercial drones. Given Microsoft’s current focus on bringing intelligence to the edge, this is almost a logical partnership, given that drones are essentially semi-autonomous edge computing devices.

DJI also today announced that Azure is now its preferred cloud computing partner and that it will use the platform to analyze video data, for example. The two companies also plan to offer new commercial drone solutions using Azure IoT Edge and related AI technologies for verticals like agriculture, construction and public safety. Indeed, the companies are already working together on Microsoft’s FarmBeats solution, an AI and IoT platform for farmers.

As part of this partnership, DJI is launching a software development kit (SDK) for Windows that will allow Windows developers to build native apps to control DJI drones. Using the SDK, developers can also integrate third-party tools for managing payloads or accessing sensors and robotics components on their drones. DJI already offers a Windows-based ground station.

“DJI is excited to form this unique partnership with Microsoft to bring the power of DJI aerial platforms to the Microsoft developer ecosystem,” said Roger Luo, DJI president, in today’s announcement. “Using our new SDK, Windows developers will soon be able to employ drones, AI and machine learning technologies to create intelligent flying robots that will save businesses time and money and help make drone technology a mainstay in the workplace.”

Interestingly, Microsoft also stresses that this partnership gives DJI access to its Azure IP Advantage program. “For Microsoft, the partnership is an example of the important role IP plays in ensuring a healthy and vibrant technology ecosystem and builds upon existing partnerships in emerging sectors such as connected cars and personal wearables,” the company notes in today’s announcement.


By Frederic Lardinois

Microsoft brings more AI smarts to the edge

At its Build developer conference this week, Microsoft is putting a lot of emphasis on artificial intelligence and edge computing. To a large degree, that means bringing many of the existing Azure services to machines that sit at the edge, no matter whether that’s a large industrial machine in a warehouse or a remote oil-drilling platform. The service that brings all of this together is Azure IoT Edge, which is getting quite a few updates today. IoT Edge is a collection of tools that brings AI, Azure services and custom apps to IoT devices.

As Microsoft announced today, Azure IoT Edge, which sits on top of Microsoft’s IoT Hub service, is now getting support for Microsoft’s Cognitive Services APIs, for example, as well as support for Event Grid and Kubernetes containers. In addition, Microsoft is also open sourcing the Azure IoT Edge runtime, which will allow developers to customize their edge deployments as needed.

The highlight here is support for Cognitive Services for edge deployments. Right now, this is a bit of a limited service as it actually only supports the Custom Vision service, but over time, the company plans to bring other Cognitive Services to the edge as well. The appeal of this service is pretty obvious, too, as it will allow industrial equipment or even drones to use these machine learning models without internet connectivity so they can take action even when they are offline.

As far as AI goes, Microsoft also today announced that it will bring its new Brainwave deep neural network acceleration platform for real-time AI to the edge.

The company has also teamed up with Qualcomm to launch an AI developer kit for on-device inferencing on the edge. The focus of the first version of this kit will be on camera-based solutions, which doesn’t come as a major surprise given that Qualcomm recently launched its own vision intelligence platform.

IoT Edge is also getting a number of other updates that don’t directly involve machine learning. Kubernetes support is an obvious one and a smart addition, given that it will allow developers to build Kubernetes clusters that can span both the edge and a more centralized cloud.

The appeal of running Event Grid, Microsoft’s event routing service, at the edge is also pretty obvious, given that it’ll allow developers to connect services with far lower latency than if all the data had to run through a remote data center.

Other IoT Edge updates include the planned launch of a marketplace that will allow Microsoft partners and developers to share and monetize their edge modules, as well as a new certification program for hardware manufacturers to ensure that their devices are compatible with Microsoft’s platform. IoT Edge, as well as Windows 10 IoT and Azure Machine Learning, will also soon support hardware-accelerated model evaluation with DirextX 12 GPU, which is available in virtually every modern Windows PC.


By Frederic Lardinois

Apple, in a very Apple move, is reportedly working on its own Mac chips

Apple is planning to use its own chips for its Mac devices, which could replace the Intel chips currently running on its desktop and laptop hardware, according to a report from Bloomberg.

Apple already designs a lot of custom silicon, including its chipsets like the W-series for its Bluetooth headphones, the S-series in its watches, its A-series iPhone chips, as well as customized GPU for the new iPhones. In that sense, Apple has in a lot of ways built its own internal fabless chip firm, which makes sense as it looks for its devices to tackle more and more specific use cases and remove some of its reliance on third parties for their equipment. Apple is already in the middle of in a very public spat with Qualcomm over royalties, and while the Mac is sort of a tertiary product in its lineup, it still contributes a significant portion of revenue to the company.

Creating an entire suite of custom silicon could do a lot of things for Apple, the least of which bringing in the Mac into a system where the devices can talk to each other more efficiently. Apple already has a lot of tools to shift user activities between all its devices, but making that more seamless means it’s easier to lock users into the Apple ecosystem. If you’ve ever compared connecting headphones with a W1 chip to the iPhone and just typical Bluetooth headphones, you’ve probably seen the difference, and that could be even more robust with its own chipset. Bloomberg reports that Apple may implement the chips as soon as 2020.

Intel may be the clear loser here, and the market is reflecting that. Intel’s stock is down nearly 8% after the report came out, as it would be a clear shift away from the company’s typical architecture where it has long held its ground as Apple moves on from traditional silicon to its own custom designs. Apple, too, is not the only company looking to design its own silicon, with Amazon looking into building its own AI chips for Alexa in another move to create a lock-in for the Amazon ecosystem. And while the biggest players are looking at their own architecture, there’s an entire suite of startups getting a lot of funding building custom silicon geared toward AI.

Apple declined to comment.

The Linux Foundation launches a deep learning foundation

Despite its name, the Linux Foundation has long been about more than just Linux. These days, it’s a foundation that provides support to other open source foundations and projects like Cloud Foundry, the Automotive Grade Linux initiative and the Cloud Native Computing Foundation. Today, the Linux Foundation is adding yet another foundation to its stable: the LF Deep Learning Foundation.

The idea behind the LF Deep Learning Foundation is to “support and sustain open source innovation in artificial intelligence, machine learning, and deep learning while striving to make these critical new technologies available to developers and data scientists everywhere.”

The founding members of the new foundation include Amdocs, AT&T, B.Yond, Baidu, Huawei, Nokia, Tech Mahindra, Tencent, Univa and ZTE. Others will likely join in the future.

“We are excited to offer a deep learning foundation that can drive long-term strategy and support for a host of projects in the AI, machine learning, and deep learning ecosystems,” said Jim Zemlin, executive director of The Linux Foundation.

The foundation’s first official project is the Acumos AI Project, a collaboration between AT&T and Tech Mahindra that was already hosted by the Linux Foundation. Acumos AI is a platform for developing, discovering and sharing AI models and workflows.

Like similar Linux Foundation-based organizations, the LF Deep Learning Foundation will offer different membership levels for companies that want to support the project, as well as a membership level for non-profits. All LF Deep Learning members have to be Linux Foundation members, too.

Clari raises $35M for its AI-based sales platform, expands into marketing and supply chain management

Clari — a startup that has built a predictive sales tool that provides just-in-time assistance for sales people close deals and for those who work in the bigger chain of command to monitor the progress of the sales operation — is capitalising on the big boom in interest for all things AI in the business world. The company is today announcing that it has closed a Series B round of $35 million, funding that it will be using to build out its own sales and marketing team and expand its platform capabilities.

The round was led by Tenaya Capital, the VC fund that started its life as a part of Lehman Brothers, along with participation from other new investors Thomvest Ventures and Blue Cloud Ventures, and previous investors Sequoia Capital, Bain Capital Ventures and Northgate Capital. It brings the total raised by Clari to $61 million.

Andy Byrne, the founder and CEO who is a repeat entrepreneur and has been involved in several exits, said the funding closed “definitely at an upround, and much bigger than we thought it was going to be,” but declined to give a number. For some context, Clari, according to Pitchbook, had a relatively modest post-money valuation of $83.5 million in its last round in 2014, so my guess is that it’s now comfortably into hundred-million territory, once you add in this latest $35 million.

The funding comes at an interesting time for AI startups, particularly those aimed at enterprise IT.

When Clari first emerged from stealth in April 2014, the idea of applying AI to solve pain points for non-technical people in organizations was a fairly nascent and still-novel concept.

Fast forward to today, things have moved very fast, as is often the case in the tech world. Now, you can’t seem to move for all the enterprise IT startups that are either using or claiming to use AI in their solutions. There are so many startup hopefuls, and so many organizations looking for the best way to use AI to improve their business and operations, that there are even startups being founded to manage that opportunity of connecting the two pieces together, such as Element AI.

“I’m not saying we were clairvoyant for targeting the idea of using AI for sales in 2013,” Byrne said. “There has been a large macro trend and if you happen to be a small company that is along for the ride. When we first launched, we had this thesis about AI for sales. Now it’s not the number three or two priority for sales teams, it’s number one. It’s everywhere. Businesses want to invest and spend more money on AI and making things more efficient.”

Clari says that its customer base has tripled in the last year, with customers including Adobe, Audi, Check Point Software, Equinix, Epicor Software Corporation, GE, and PerkinElmer.

Clari’s approach for using AI for the sales team comes in two main areas. First, the company’s system is aimed to reduce some of the busywork that salespeople have in maintaining and updating files on people, by bringing in a number of different data sources and using them to provide composite pictures of target companies that salespeople might have had to otherwise compile with more manual means. Second, Clari puts a lot of focus on its “Opportunity-to-Close (OTC) solutions” — a type of risk-analysis for salespeople and their managers to help them figure out which leads and strategic directly would be the most likely to produce sales.

“Working with Clari since inception, we have been impressed with its growth and strong execution,” said Aaref Hilaly, Partner at Sequoia Capital, in a statement. “Clari has fast become indispensable to many of the most successful sales teams, giving them visibility into their most important metrics: rep productivity, pipeline health, and forecast accuracy.”

Indeed, risk and outcome is a smart area to be in: using AI to help model this is a key area of focus in enterprise IT at the moment, according to feedback I’ve had from a number of others in the enterprise world.

“If you have 150 opportunities presented to you as a salesperson, how do you choose 10 where you should spend your time?” Byrne asked. “A more traditional CRM platform has never showcased your risk and outcomes.”

While up to now Clari has focused on providing intelligence on what is already in a company’s account database, the next step, Byrne noted, is to draw on data from around the web, providing completely new business leads to the sales team.

When we last covered a funding round for Clari, we noted that the company’s laser focus on sales was something that made the company stand out for investors: nailing one aspect of a business’s operations without distractions from other parts of the organization and what it could be spending time solving elsewhere (in fact, when you think about it, the very goal that Clari has been aiming to achieve for salespeople through its product).

But four years on, the company is now widening that ambition. It’s applying its AI engine now to help marketeers weigh up the best opportunities for reaching out to prospective customers; and interestingly it sounds like it will also be applying its engine to product development and specifically supply chain management.

Byrne described one customer, a medical device maker, that was encountering “inefficiencies” around what they should build and when to meet market demand. “Now that they can predict and forecast order bookings and revenue targets, and what’s happened is that their supply chain has become more efficient,” he said. “It is great example of how our AI is now being expanded.”

“The Clari team has leveraged its deep AI expertise to build a unique platform that surfaces predictive insights for sales reps, managers, and execs during the opportunity-to-close process,” said Brian Paul, MD at Tenaya Capital, in a statement. “We see a massive opportunity for AI to transform how sales teams operate which is clearly validated by Clari’s customers and the impressive growth the team has achieved.”