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Cognigy raises $44M to scale its enterprise-focused conversational AI platform
Artificial intelligence is becoming an increasingly common part of how customer service works — a trend that was accelerated in this past year as so many other services went virtual and digital — and today a startup that has built a set of low-code tools to help enterprises integrate more AI into their customer service processes is announcing some funding to fuel its growth.
Cognigy, which provides a low-code conversational AI platform that notably can be used flexibly across a range of applications and geographies — it supports 120 languages; it can be used in external or internal service applications; it can support voice services but also chatbots; it provides real-time assistance for human agents and usage analytics or fully automated responses; it can integrate with standard call center software, and also with RPA packages; and it can be run in the cloud or on-premise — has closed a round of $44 million, funding that it will be using to continue scaling its business internationally.
Insight Partners is leading the Series B investment, with previous backers DN Capital, Global Brain, Nordic Makers, Inventures and Digital Innovation and Growth also participating. The Dusseldorf-based company had previously only raised $11 million and spent the first several years of business bootstrapped.
Cognigy is not disclosing its valuation but it has up to now built up a concentration of customers in areas like transportation, e-commerce and insurance and counts a number of big multinational companies among its customer list, including Lufthansa, Mobily, BioNTech, Vueling Airlines, Bosch and Daimler, with “thousands” of virtual assistants now powered by Cognigy live in the market.
With 25% of Cognigy’s business already coming from the U.S., the plan now is to use some funding to invest in building out its service deeper into the U.S., Asia and across more of Europe, CEO and founder Philipp Heltewig said in an interview.
“Conversational AI” these days appears in many guises: it can be a chatbot you come across on a website when you’re searching for something, or it can be prompts provided to agents or salespeople, information and real-time feedback to help them do their jobs better. Conversational AI can also be a personal assistant on your company’s HR application to help you book time off or deal with any number of other administrative jobs, or a personal assistant that helps you use your phone or set your house alarm.
There are a number of companies in the tech world that have built tools to address these various use cases. Specifically in the area of services aimed at enterprises, some of them, like Gong, are raising huge money right now. What is notable about Cognigy is that it has built a platform that is attempting to address a wide swathe of applications: one platform, many uses, in other words.
Cognigy’s other selling point is that it is playing into the new interest in low- and no-code tools, which in Cognigy’s case makes the integration of AI into a customer assistance process a relatively easy task, something that can be built not just by developers, but data scientists, those working directly on conversation design, and nontechnical business users using the tools themselves.
“The low-code platform helps enterprises adopt what is otherwise complex technology in an easy and flexible way, whether it is a customer or employee contact center,” said Heltewig. As you might expect, there are some direct competitors in the low- and no-code conversational AI space, too, including Ada, Talkie, Snaps and more.
Flexibility seems to be the order of the day for enterprises, and also the companies building tools for them: it means that a company can grow into a larger customer, and that in theory Cognigy will also evolve the platform based on what its customers need. As one example, Heltewig pointed out that a number of its customers are — contrary to the beating drum and march you see every day toward cloud services — running a fair number of applications on-premises, since this appears to be a key way to ensure the security of the customer data that they handle.
“Lufthansa could never run its customer services in the cloud because they handle a lot of sensitive data and they want full ownership of it,” he noted. “We can run cloud services and have a full offering for those who want it, but many large enterprises prefer to run their services on premises.”
Teddie Wardi, an MD at Insight, is joining the board with this round. “We are thrilled to be leading Cognigy’s Series B as the company continues on their ScaleUp journey,” he said in a statement. “Evident by their strong customer retention, Cognigy has created an essential product for global businesses to improve their customer experience in an efficient and effortless manner. With the new funding, Cognigy will be able to expand their leadership position to reach new markets and acquire more customers.”
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Fireflies.ai raises $14M for its meeting transcription and automation service
The Fireflies.ai project is a good reminder that not every startup project goes from idea to unicorn-status in 48 minutes. Instead, the startup’s CEO Krish Ramineni told TechCrunch about how a period of interest in natural language processing (NLP), tinkering with a friend, a stint at Microsoft, and even working on Slack bots led him to helping found Fireflies.ai (Fireflies), a company that today announced a $14 million raise led by Khosla.
Fireflies is a two-part service. Its first point of business is recording and transcribing voice conversations. Things like video meetings, for example. Next, Fireflies wants to plug your voice data into other applications, helping its customers automate data entry, task creation and more.
Before today’s round, the startup had raised around $5 million, including some micro-rounds, a stint in the Acceleprise accelerator, and a $4.9 million seed round raised in late 2019. That investment included participation from Canaan Partners and well-known angel April Underwood.
That Fireflies has raised more capital is not surprising, given how quickly it has accreted users. According to an interview with Ramineni, more than 10,000 teams use Fireflies today. In individual usage terms, some 35,000 organizations are represented amongst its user base.
As the company launched its product in early 2020, those results sound pretty good.
But TechCrunch was curious if revenue tracked with usage at Fireflies, as is sometimes the case. It does, Ramineni said, adding that his company grew its revenues 300% in the last six or seven months.
How did it manage such rapid growth while only having raised $5 million before, and with a team that is around 90% in its product and engineering teams? By pursuing everyone’s favorite: the bottoms-up sales model. In short, you can use Fireflies for free, but if you run out of meeting credits, other usage-based blockers or the need for different, paywalled functionality, you have to cough up for the product.
Folks are, it appears.
Fireflies is in fact an interesting hybrid of SaaS and usage-based pricing. The higher the paid tier that a user selects, the more minutes of transcription they are apportioned per month. But there are caps, limits that users can buy their way out of. TechCrunch asked Ramineni about it, with the CEO explaining that some customers want to ingest years of saved meetings. Our read is that despite work done by the startup to keep its infrastructure costs low, building pricing guardrails around product usage just makes sense for the startup.
The company will sport SaaS-like gross margins, Ramineni confirmed to TechCrunch.
Looking ahead, Fireflies wants to plug into more and more meeting platforms, and external software. You can currently link your Fireflies account to services like Zapier, Slack and your CRM. Over time, it’s not hard to see how the startup could take more direct commands from meetings, and help users better distribute, file and recall meeting information.
As someone with too many meetings, and too many notes documents spread out across the wasteland that is my Google Drive account, I get why people are using Fireflies today. But if the startup can build a no-code automation platform on top of my note taking? Then I will probably have to buy its service.
Speaking of which, as a final note, working for a Major American Corporation can have its downsides. For example, Ramineni provided TechCrunch with a recording of our interview inside of Fireflies. This was nice, as I prefer to write from both my notes and transcripts to ensure that I am not missing things, or making mistakes. Fireflies kept asking me to log in. I tried with my corporate Google account. Which blocks such log-ins. So I kept getting the same prompt again and again.
Annoying? Sure. Lethal? No.
More when we can squeeze more growth data out of the startup.
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Headroom, which uses AI to supercharge videoconferencing, raises $5M
Videoconferencing has become a cornerstone of how many of us work these days — so much so that one leading service, Zoom, has graduated into verb status because of how much it’s getting used.
But does that mean videoconferencing works as well as it should? Today, a new startup called Headroom is coming out of stealth, tapping into a battery of AI tools — computer vision, natural language processing and more — on the belief that the answer to that question is a clear — no bad Wi-Fi interruption here — “no.”
Headroom not only hosts videoconferences, but then provides transcripts, summaries with highlights, gesture recognition, optimised video quality and more, and today it’s announcing that it has raised a seed round of $5 million as it gears up to launch its freemium service into the world.
You can sign up to the waitlist to pilot it, and get other updates here.
The funding is coming from Anna Patterson of Gradient Ventures (Google’s AI venture fund); Evan Nisselson of LDV Capital (a specialist VC backing companies building visual technologies); Yahoo founder Jerry Yang, now of AME Cloud Ventures; Ash Patel of Morado Ventures; Anthony Goldbloom, the co-founder and CEO of Kaggle.com; and Serge Belongie, Cornell Tech associate dean and professor of Computer Vision and Machine Learning.
It’s an interesting group of backers, but that might be because the founders themselves have a pretty illustrious background with years of experience using some of the most cutting-edge visual technologies to build other consumer and enterprise services.
Julian Green — a British transplant — was most recently at Google, where he ran the company’s computer vision products, including the Cloud Vision API that was launched under his watch. He came to Google by way of its acquisition of his previous startup Jetpac, which used deep learning and other AI tools to analyze photos to make travel recommendations. In a previous life, he was one of the co-founders of Houzz, another kind of platform that hinges on visual interactivity.
Russian-born Andrew Rabinovich, meanwhile, spent the last five years at Magic Leap, where he was the head of AI, and before that, the director of deep learning and the head of engineering. Before that, he too was at Google, as a software engineer specializing in computer vision and machine learning.
You might think that leaving their jobs to build an improved videoconferencing service was an opportunistic move, given the huge surge of use that the medium has had this year. Green, however, tells me that they came up with the idea and started building it at the end of 2019, when the term “COVID-19” didn’t even exist.
“But it certainly has made this a more interesting area,” he quipped, adding that it did make raising money significantly easier, too. (The round closed in July, he said.)
Given that Magic Leap had long been in limbo — AR and VR have proven to be incredibly tough to build businesses around, especially in the short to medium-term, even for a startup with hundreds of millions of dollars in VC backing — and could have probably used some more interesting ideas to pivot to; and that Google is Google, with everything tech having an endpoint in Mountain View, it’s also curious that the pair decided to strike out on their own to build Headroom rather than pitch building the tech at their respective previous employers.
Green said the reasons were two-fold. The first has to do with the efficiency of building something when you are small. “I enjoy moving at startup speed,” he said.
And the second has to do with the challenges of building things on legacy platforms versus fresh, from the ground up.
“Google can do anything it wants,” he replied when I asked why he didn’t think of bringing these ideas to the team working on Meet (or Hangouts if you’re a non-business user). “But to run real-time AI on video conferencing, you need to build for that from the start. We started with that assumption,” he said.
All the same, the reasons why Headroom are interesting are also likely going to be the ones that will pose big challenges for it. The new ubiquity (and our present lives working at home) might make us more open to using video calling, but for better or worse, we’re all also now pretty used to what we already use. And for many companies, they’ve now paid up as premium users to one service or another, so they may be reluctant to try out new and less-tested platforms.
But as we’ve seen in tech so many times, sometimes it pays to be a late mover, and the early movers are not always the winners.
The first iteration of Headroom will include features that will automatically take transcripts of the whole conversation, with the ability to use the video replay to edit the transcript if something has gone awry; offer a summary of the key points that are made during the call; and identify gestures to help shift the conversation.
And Green tells me that they are already also working on features that will be added into future iterations. When the videoconference uses supplementary presentation materials, those can also be processed by the engine for highlights and transcription too.
And another feature will optimize the pixels that you see for much better video quality, which should come in especially handy when you or the person/people you are talking to are on poor connections.
“You can understand where and what the pixels are in a video conference and send the right ones,” he explained. “Most of what you see of me and my background is not changing, so those don’t need to be sent all the time.”
All of this taps into some of the more interesting aspects of sophisticated computer vision and natural language algorithms. Creating a summary, for example, relies on technology that is able to suss out not just what you are saying, but what are the most important parts of what you or someone else is saying.
And if you’ve ever been on a videocall and found it hard to make it clear you’ve wanted to say something, without straight-out interrupting the speaker, you’ll understand why gestures might be very useful.
But they can also come in handy if a speaker wants to know if he or she is losing the attention of the audience: The same tech that Headroom is using to detect gestures for people keen to speak up can also be used to detect when they are getting bored or annoyed and pass that information on to the person doing the talking.
“It’s about helping with EQ,” he said, with what I’m sure was a little bit of his tongue in his cheek, but then again we were on a Google Meet, and I may have misread that.
And that brings us to why Headroom is tapping into an interesting opportunity. At their best, when they work, tools like these not only supercharge videoconferences, but they have the potential to solve some of the problems you may have come up against in face-to-face meetings, too. Building software that actually might be better than the “real thing” is one way of making sure that it can have staying power beyond the demands of our current circumstances (which hopefully won’t be permanent circumstances).
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Writer pens a $5M seed round for its AI style guide that flags bias and tone
Anyone who writes online or in a word processor has likely gotten used to the inevitable squiggly line denoting a misspelled word or clumsy phrase. But what if you use a word that’s loaded, a phrase that’s too formal or not formal enough, or refer to a group of people in an outdated way? Writer is a service that watches as you type, flagging language that doesn’t match up with your style guide and values, and it just raised $5 million to scale up.
Both people and the companies they work for want to improve the way they write, but not just in terms of grammar and spelling. If a company says it’s inclusive, but the language in its press releases or internal blogs are peppered with anachronisms and bias, it suggests their concern only goes so far.
“Companies are hungry to put actions behind their words,” said Writer founder and CEO May Habib. “They want to be able to tell a consistent story to their users everywhere that they’re interacting with them. What Writer does is let people know when they’re using insensitive language, or things that could be considered negative, and let companies set brand guidelines.”
Right off the bat let us admit that there is a whiff of the sinister about the idea of a company dictating how its employees speak, though that’s nothing new when it comes to content and official communications. But this isn’t about controlling speech for power — it’s about recognizing that we are all flawed communicators and could use a hand keeping ourselves honest. Less thought police and more a well-informed angel sitting on your shoulder whispering things like, “Hey. Are you sure you want to describe that lawyer as ‘exotic’?”
Examples of things Writer checks for. Image Credits: Writer
There are tons of slip-ups we all make along those lines; less obvious, but no less potentially offensive. It’s important in public communications, among other things, to refer to a group by the term they prefer, not the first one that pops into your head; Writer has up-to-date libraries of this information sourced from the communities themselves. Some phrases may have become politically loaded in the last couple of years, but you’re not aware; no problem, it has alternatives. You want to avoid unnecessarily gendered language, great, but everyone slips up now and then; Writer can spot it — or make the connection with previous pronouns to make sure you don’t, for example, gender an anonymous source.
Accusations of “political correctness” will dog the service, but as Habib put it: “This is beyond politics; this is about respect for people who live a certain way, or are a certain way, and prefer to use certain terms. We’re trying to help companies create communities of belonging.” And as we’ve seen over and over again in tech, there is often a serious disconnect between the stated aspiration of a company and how people are treated within them. Just using the right words is a pretty low bar to start with, honestly.
Image Credits: Writer
Writer isn’t just a growing blacklist of words you should think twice about using, though. The natural language processing engine at the heart of it is also very concerned with things like sentence complexity, paragraph length and tone. It has to have this deeper understanding, Habib explained, because “it’s not enough to underline — you need to know what to replace it with, and when you replace it, you need to fit it into the sentence. These are actually hard NLP problems.”
That lets it fit into a variety of roles in addition to promoting inclusive language. It can watch for the usual spelling and grammar mistakes, as well as things like formality, active voice, “liveliness” (whatever that is, I don’t have it) and other metrics that help define a brand.
And of course you can bring in your own style guide so your editors don’t have to roll their eyes at serial commas in headlines, double dashes instead of em dashes, e-mail instead of email and all the rest of the little nips and tucks that keep a brand’s writing in a generally recognizable shape.
Image Credits: Writer
The service can also switch between style guides or adjust or disable itself in different apps and sites — so internal emails aren’t given the same guidelines as press releases, or a blog post’s style can be differentiated from a newsletter’s.
Obviously Grammarly is a big competitor here, but Habib feels that it and the growing number of in-browser or in-app checking services are very focused on the technical piece. Writer is less about preventing an individual writer’s errors, and more about creating consistency among groups of writers and making sure they are working from the same high-level linguistic standards.
Of course security is also a concern — no one wants a keylogger running on their machine, however helpful it may be. Habib was careful to emphasize that Writer runs locally in the browser as a plug-in, integrating with Word or Chrome for now but with other apps and services on the way. “None of that data ever hits a writer server, and no metadata — all the processing is done in the text area,” she said. The only data that’s sent back is the fact that a given suggestion was used, such as changing “should of” to “should have” or “illegal aliens” to “undocumented immigrants.” No user data is used to train the models and no content apart from the correction itself is sent or stored on Writer’s servers.
Writer is available now, for $11/person/month (with the obligatory free trial period, of course) for a basic version and some unspecified amount for enterprise deals with multiple style guides, plagiarism detection, and so on. It’s only available in English, and although there is of course demand for the service in other languages, the depth of the NLP model and the specificity of what it recognizes to the language mean it does not generalize well. To take on Spanish or Korean would be to develop an entirely new product. So English it is for now.
The company is new, and has been developing its NLP engine (on the back of a previous effort, which monitored user-facing language in GitHub repos) for 18 months in something like stealth. The $5 million seed round, led by Upfront Ventures, Aspect Ventures, Bonfire Ventures, and Broadway Angels should help the company scale, though it already has some top-tier, household-name customers, so with that and the money, its immediate future seems to be secure.
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Espressive lands $30M Series B to build better help chatbots
Espressive, a four-year-old startup from former ServiceNow employees, is working to build a better chatbot to reduce calls to company help desks. Today, the company announced a $30 million Series B investment.
Insight Partners led the round with help from Series A lead investor General Catalyst along with Wing Venture Capital. Under the terms of today’s agreement, Insight founder and managing director Jeff Horing will be joining the Espressive Board. Today’s investment brings the total raised to $53 million, according to the company.
Company founder and CEO Pat Calhoun says that when he was at ServiceNow he observed that, in many companies, employees often got frustrated looking for answers to basic questions. That resulted in a call to a Help Desk requiring human intervention to answer the question.
He believed that there was a way to automate this with AI-driven chatbots, and he founded Espressive to develop a solution. “Our job is to help employees get immediate answers to their questions or solutions or resolutions to their issues, so that they can get back to work,” he said.
They do that by providing a very narrowly focused natural language processing (NLP) engine to understand the question and find answers quickly, while using machine learning to improve on those answers over time.
“We’re not trying to solve every problem that NLP can address. We’re going after a very specific set of use cases which is really around employee language, and as a result, we’ve really tuned our engine to have the highest accuracy possible in the industry,” Calhoun told TechCrunch.
He says what they’ve done to increase accuracy is combine the NLP with image recognition technology. “What we’ve done is we’ve built our NLP engine on top of some image recognition architecture that’s really designed for a high degree of accuracy and essentially breaks down the phrase to understand the true meaning behind the phrase,” he said.
The solution is designed to provide a single immediate answer. If, for some reason, it can’t understand a request, it will open a help ticket automatically and route it to a human to resolve, but they try to keep that to a minimum. He says that when they deploy their solution, they tune it to the individual customers’ buzzwords and terminology.
So far they have been able to reduce help desk calls by 40% to 60% across customers with around 85% employee participation, which shows that they are using the tool and it’s providing the answers they need. In fact, the product understands 750 million employee phrases out of the box.
The company was founded in 2016. It currently has 65 employees and 35 customers, but with the new funding, both of those numbers should increase.
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Google’s new voice recognition system works instantly and offline (if you have a Pixel)
Voice recognition is a standard part of the smartphone package these days, and a corresponding part is the delay while you wait for Siri, Alexa or Google to return your query, either correctly interpreted or horribly mangled. Google’s latest speech recognition works entirely offline, eliminating that delay altogether — though of course mangling is still an option.
The delay occurs because your voice, or some data derived from it anyway, has to travel from your phone to the servers of whoever operates the service, where it is analyzed and sent back a short time later. This can take anywhere from a handful of milliseconds to multiple entire seconds (what a nightmare!), or longer if your packets get lost in the ether.
Why not just do the voice recognition on the device? There’s nothing these companies would like more, but turning voice into text on the order of milliseconds takes quite a bit of computing power. It’s not just about hearing a sound and writing a word — understanding what someone is saying word by word involves a whole lot of context about language and intention.
Your phone could do it, for sure, but it wouldn’t be much faster than sending it off to the cloud, and it would eat up your battery. But steady advancements in the field have made it plausible to do so, and Google’s latest product makes it available to anyone with a Pixel.
Google’s work on the topic, documented in a paper here, built on previous advances to create a model small and efficient enough to fit on a phone (it’s 80 megabytes, if you’re curious), but capable of hearing and transcribing speech as you say it. No need to wait until you’ve finished a sentence to think whether you meant “their” or “there” — it figures it out on the fly.
So what’s the catch? Well, it only works in Gboard, Google’s keyboard app, and it only works on Pixels, and it only works in American English. So in a way this is just kind of a stress test for the real thing.
“Given the trends in the industry, with the convergence of specialized hardware and algorithmic improvements, we are hopeful that the techniques presented here can soon be adopted in more languages and across broader domains of application,” writes Google, as if it is the trends that need to do the hard work of localization.
Making speech recognition more responsive, and to have it work offline, is a nice development. But it’s sort of funny considering hardly any of Google’s other products work offline. Are you going to dictate into a shared document while you’re offline? Write an email? Ask for a conversion between liters and cups? You’re going to need a connection for that! Of course this will also be better on slow and spotty connections, but you have to admit it’s a little ironic.
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Chorus.ai rings up $33M for its platform that analyses sales calls to close more deals
Chorus.ai, a service that listens to sales calls in real time, and then transcribes and analyses them to give helpful tips to the salesperson, has raised $33 million to double down on the current demand for more AI-based tools in the enterprise.
The Series B is being led by Georgian Partners, with participation also from Redpoint Ventures and Emergence Capital, previous investors that backed Israeli-founded, SF-based Chorus.ai in its $16 million Series A two years ago.
In the gap between then and now, the startup has seen strong growth, listening in to some 5 million calls, and performing hundreds of thousands of hours of transcriptions for around 200 customers, including Adobe, Zoom, and Outreach (among others that it will not name).
Micha Breakstone, the co-founder (who has a pretty long history in conversational AI, heading up R&D at Ginger Software and then Intel after it acquired the startup; and before that building the tech that eventually became Summly and got acquired by Yahoo, among other roles), says that while the platform gives information and updates to salespeople in real time, much of the focus today is on providing information to users post-conversation, based on both audio and video calls.
One of its big areas is “smart themes” — patterns and rules Chorus has learned through all those calls. For example, it has identified what kind of language the most successful sales people are using and in turn prompts those who are less successful to use it more. Two general tips Breakstone told me about: using more collaborative terms like we and us; and giving more backstory to clients, although there will be more specific themes and approaches based on Chorus’s specific customers and products.
“I’d say we are super attuned to our customers and what they need and want,” Breakstone said. Which makes sense given the whole premise of Chorus.
It also creates smart “playlists” for managers who will almost certainly never have the time to review hundreds of hours of calls but might want to hear instructive highlights or ‘red alert’ moments where a more senior person might need to step in to save or close a deal.
There are currently what seems like dozens of startups and larger businesses that are currently tackling the opportunity to provide “conversational intelligence” to sales teams, using advances in natural language processing, voice recognition, machine learning and big data to help turn every sales person into a Jerry Maguire (yes, I know he’s an agent, but still, he needs to close deals, and he’s a salesman). They include TalkIQ (which has now been acquired by Dialpad), People.AI, Gong, Voicera, VoiceOps, and I’m pulling from a long list.
“We were among the very first to start this, no one knew what conversational intelligence was before us,” Breakstone says. He describes most of what was out in the market at the time as “Nineties technology” and adds that “our tech is superior because we built it in the correct way from the ground up, with nothing sent to a third party.”
He says that this is one reason why the company has negative churn — it essentially wins customers and hasn’t lost any. And having the tech all in-house not only means the platform is smarter and more accurate, but that helps with compliance around regulations like GDPR, which also has been a boost to its business. It’s also scored well on metrics around reps hitting targets better with its tools (the company claims its products lead to 50 percent greater quota attainment and ‘ramp time’ up by 30 percent for new sales people who use it).
“Chorus.ai has helped us become a smarter sales organization as we’ve scaled. We have visibility into our sales conversations and what is working across all of our offices”, said Greg Holmes, Head of Sales for Zoom Video Communications, in a statement. “We’ve seen a drastic reduction in new hire ramp times and higher sales productivity with even more reps hitting quota. Chorus.ai is a game changer.”
Chorus has raised $55 million to date and Breakstone said he would not disclose its valuation — despite my best attempts to use some of those sales tips to winkle the information out of him. But I understand it to be “significantly higher” than in its last round, and definitely in the hundreds of millions.
As a point of reference, after its Series A two years ago, it was only valued at around $33 million post-money according to PitchBook.
“Maintaining high-quality sales conversations as you scale a sales organization is hard for many companies, but key to delivering predictable revenue growth. Chorus.ai’s Conversation Intelligence platform solves that challenge with a market-leading solution that is easy-to-use and delivers best-in-class results.” said Simon Chong, Managing Partner at Georgian Partners, in a statement. (Chong is joining the board with this round.) “Chorus.ai works with some of the best sales teams in the world and they love the product. We are very excited to partner with Chorus.ai on their next phase of growth as they help world class sales teams reach higher quota attainment and efficiency.”
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LinkedIn boosts its messaging with smart replies, pre-written, AI based interactions
LinkedIn — the Microsoft-owned platform for those who want to network with professional contacts and advance their own careers — has been in the middle of a long-term makeover of its social tools, as it looks to drive more usage. Today comes the latest chapter in that story: the site is unveiling a new smart reply feature in its messaging app, which gives users prompts with… Read More
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Tableau acquires ClearGraph, a startup that lets you analyze your data using natural language
Business intelligence and analytics firm Tableau today announced that it has acquired ClearGraph, a service that lets you query and visualize large amounts of business date through natural language queries. Tableau expects to integrate this technology with its own products as it looks to make it easier for its users to use similar queries to visualize their data. Read More
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Freshdesk owner Freshworks acquires Joe Hukum as it plans a move into chatbots
After raising $55 million last year to build its business beyond its existing help desk services, today Freshworks (the parent company of Freshdesk) has made an acquisition to help it fill out that strategy. The company has acquired Joe Hukum, a startup out of India that offers a platform for businesses to build their own chatbots. I’ve asked, but the companies are not revealing any terms… Read More
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