natural language processing
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Meet the startups in the latest Alchemist class
Alchemist is the Valley’s premiere enterprise accelerator and every season they feature a group of promising startups. They are also trying something new this year: they’re putting a reserve button next to each company, allowing angels to express their interest in investing immediately. It’s a clever addition to the demo day model.
You can watch the live stream at 3pm PST here.
Videoflow – Videoflow allows broadcasters to personalize live TV. The founding team is a duo of brothers — one from the creative side of TV as a designer, the other a computer scientist. Their SaaS product delivers personalized and targeted content on top of live video streams to viewers. Completely bootstrapped to date, they’ve landed NBC, ABC, and CBS Sports as paying customers and appear to be growing fast, having booked over $300k in revenue this year.
Redbird Health Tech – Redbird is a lab-in-a-box for convenient health monitoring in emerging market pharmacies, starting with Africa. Africa has the fastest growing middle class in the world — but also the fastest growing rate of diabetes (double North America’s). Redbird supplies local pharmacies with software and rapid tests to transform them into health monitoring points – for anything from blood sugar to malaria to cholesterol. The founding team includes a Princeton Chemical Engineer, 2 Peace Corps alums, and a Pharmacist from Ghana’s top engineering school. They have 20 customers, and are growing 36% week over week.
Shuttle – Shuttle is getting a head start on the future of space travel by building a commercial spaceflight booking platform. Space tourism may be coming sooner than you think. Shuttle wants to democratize access to the heavens above. Founded by a Stanford Computer Science alum active in Stanford’s Student Space Society, Shuttle has partnerships with the leading spaceflight operators, including Virgin Galactic, Space Adventures, and Zero-G. Tickets to space today will set you back a cool $250K, but Shuttle believes that prices will drop exponentially as reusable rockets and landing pads become pervasive. They have $1.6m in reservations and growing.
Birdnest – Threading the needle between communal and private, Birdnest is the Goldilocks of office space for startups. Communal coworking spaces are accessible but have too many distractions. Traditional office spaces are private but inflexible on their terms. Birdnest brings the best of each without the drawbacks: finding, leasing, and operating a network of underutilized spaces inside of private offices. The cofounders, a duo of Duke and Kellogg MBA grads, are at $300K ARR with a fast-growing 50+ client waitlist.
Tag.bio – Tag.bio wants to make data science actionable in healthtech. The founding team is comprised of a former Ayasdi bioinformatician and a former Honda Racing engineer with a Stanford MBA. They’ve developed a next-generation data science platform that makes it easy and fast to build data apps for end users, or as they say, “WordPress for data science.” The result they claim is lightning-fast analysis apps that can be run by end users, dramatically accelerating insight discovery. They count the UCSF Medical Center and a “large Swiss pharma company” as early customers.
nCorium – They’ve built a new server architecture to handle the onslaught of AI to come with what they claim is the world’s first AI accelerator on memory to deliver 30x greater performance than the status quo. The quad founding team is intimidatingly technical — including a UCSD Professor, and former engineers from Qualcomm and Intel with 40 patents among them. They have $300K in pilots.
Spiio – Software eats landscaping with Spiio, which combines cloud-driven AI with physical sensors to monitor watering and landscaping for big companies. Their smart system knows when to water and when not to. This reduces water consumption by 50%, which means their system pays for itself in less than 30 days for big companies. They want to connect every plant to the internet, and look like they are off to a good start — $100K in orders from brand name Valley tech firms, and they are doubling monthly.
Element42 – Fraud is a major problem — For example, if you buy a Rolex on eBay, you run the risk of winding up with a counterfeit. Started by ex-VPs from Citibank, the founders are using risk models and technologies that banks use to help brands combat fraud and counterfeiting. Designed with token economics, they also incentivize customers to buy genuine products by serving exclusive content and promotions only to genuine product holders. Built on blockchain at the core, they claim to be the world’s first peer-to-peer authentication platform for physical assets. They have 45 customers across two industry verticals, 800K in ARR and are a member of World Economic Forum’s global initiatives against corruption.
My90 – Distrust between the public and the police has rarely been more strained than it is today. My90 wants to solve that by collecting data about interactions between the police and the public—think traffic stops, service calls, etc.—and turn these into actionable intelligence via an online analytics dashboard. Users text My90 anonymously about their interactions, and My90’s dashboard analyzes the results using natural language processing. Customers include major city police departments like the San Jose Police Department and the world’s largest community policing program. They have booked $150K in pilots and are expanding aggressively across the US.
Nunetz – A Stanford Computer Science grad and UCSF Neurosurgeon have come together to try to build a single unifying interface to replace the deluge of monitors and data sources in today’s clinical health environment. The goal is to prepare a daily “battle map” for physicians, nurses, and other providers, with an initial focus on the Intensive Care Unit (ICU). They have closed 3 paid pilots with hospitals through grants.
When Labs – If you hate managing people, When Labs wants to unburden you. Using an AI-powered assistant that texts with employees to negotiate assignments for hourly work, WhenLabs is trying to free customers like Hilton from spending money on managers who would normally do this manually. As the system gets smarter, they claim employees will prefer interfacing with their AI bot more than a human. AI and HR is a crowded space, but this might be the team to separate from the pack: the founding team’s previous company had a 9 figure exit to IBM.
FirstCut – FirstCut helps businesses put video content out at scale. Video dominates social media — it creates 10x more comments than text — and is emerging as a necessity for B2B media. But putting video out if you are a B2B marketer normally requires using agencies that charge hefty fees. FirstCut wants to disrupt the agencies with software and marketplaces. They use software automation and an on-demand talent marketplace to offer a fixed price product for video content. They are at $180k revenue, and most of it is moving to recurring subscriptions.
LynxCare – LynxCare claims that 90% of healthcare data goes untapped when doctors make critical decisions about your life. Further, they claim the average person’s life could be extended by 4 years if that data can be converted into insights. Their team of clinicians and data scientists aims to do just that — building a data platform that aggregates disparate data sets and drive insight for better clinical outcomes. And it looks like their platform has fans: they are active in 9 hospitals, count Pharma companies like Pfizer as Partners, and grew 4x over the past year and now are at $800K ARR.
ADIAN – Adian is a B2B SaaS product that digitizes the complex agrochemical supply chain in order to improve the sales process between manufacturers and distributors. The company claims manufacturers reduce costs by 20% and increase sales by 4% by using their online framework. $1.5 Billion and 70,000 orders have gone through the platform to date.
Hardin Scientific – Hardin is building IoT-enabled, Smart Lab Equipment. The hardware becomes a gateway to become the hub for monitoring, controlling, and sharing scientific data across teams. They’ve closed over $1.5m in revenue, and raised $15m in equity and debt financing. One of their smart devices is being used to 3D print bio-tissues and human organs in space.
ZaiNar – This team of 5 Stanford grads — 3 PhD’s and 2 MBAs — joined up with the Co-Founder of BlueKai to build the world’s best time synchronization technology. ZaiNar claims their ability to wirelessly synchronize and distribute time between networked devices is a thousand times better than existing technologies. This enables them to locate RF-emitting devices (i.e. phones, cars, drones, & RFID) at long distances with sub-meter accuracy. Beyond location, this technology has applications across data transmission, 5G communications, and energy grids. ZaiNar has raised a $1.7 million seed from AME Cloud and Softbank, and has built an extensive patent portfolio.
SMART Brain Aging – This startup claims to reduce the onset of dementia by 2.25 years with software. They are the only company approved by Medicare to get reimbursed on a preventative basis for the treatment of dementia. In conjunction with Harvard University, they have developed 20,000 exercises that are clinically proven to reduce the onset of dementia and, they claim, help build neurotransmitters. The company works with 300 patients per week ($2.2 million annual revenue) and is building to a goal of helping 22,000 people in 24 months.
Phoneic – Phoneic believes the data trapped in voice calls from cellphones is a gold mine waiting to be unleashed. Their app records and transcribes cell phones conversations, and the company has built an integration layer to enterprise AI and CRM systems that traditionally didn’t have access to voice data. The team is led by the co-founder of 3jam, one of the first group SMS and virtual number companies, which was acquired by Skype in 2011. He is keenly aware of the power of virality — and like Skype, the use of Phoneic spreads its adoption. The company has already raised $800,000 in seed funding.
Arkose Labs – Whether or not you think Russia interfered with the 2016 election, it’s no secret that bots are having significant impact on society. Arkose Labs wants to fight fraud, without adding friction to legit users. Most fraud prevention platforms today focus on gathering info from the user and providing a probability score that the traffic is good or bad. This leaves companies with a difficult decision where they may be blocking revenue generating users. Arkose has a different approach, and uses a bilateral approach that doesn’t force this tradeoff. They claim to be the only solution to offer a 100% SLA on fraud prevention. Big companies like Singapore Airlines and Electronic Arts are customers. USVP led a $6 million investment into the company.
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Einstein Voice gives Salesforce users gift of gab
Salespeople usually spend their days talking. They are on the phone and in meetings, but when it comes to updating Salesforce, they are back at the keyboard again typing notes and milestones, or searching for metrics about their performance. Today, Salesforce decided to change that by introducing Einstein Voice, a bit of AI magic that allows salespeople to talk to the program instead of typing.
In a world where Amazon Alexa and Siri make talking to our devices more commonplace in our non-work lives, it makes sense that companies are trying to bring that same kind of interaction to work.
In this case, you can conversationally enter information about a meeting, get daily briefings about key information on your day’s meetings (particularly nice for salespeople who spend their day in the car) and interact with Salesforce data dashboards by asking questions instead of typing queries.
All of these tools are designed to make life easier for busy salespeople. Most hate doing the administrative part of their jobs because if they are entering information, even if it will benefit them having a record in the long run, they are not doing their primary job, which is selling stuff.
For the meetings notes part, instead of typing on a smartphone, which can be a challenge anyway, you simply touch Meeting Debrief in the Einstein Voice mobile tool and start talking to enter your notes. The tool interprets what you’re saying. As with most transcription services, this is probably not perfect and will require some correcting, but should get you most of the way there.
It can also pick out key data like dates and deal amounts and let you set action items to follow up on.
Gif: Salesforce
Brent Leary, who is the founder and principal analyst at CRM Essentials says this is a natural progression for Salesforce as people get more comfortable using voice interfaces. “I think this will make voice-first devices and assistants as important pieces to the CRM puzzle from both a customer experience and an employee productivity perspective,” he told TechCrunch.
It’s worth pointing out that Tact.AI has been giving Salesforce users these kind of voice services for some time, and Tact CEO Chuck Ganapathi doesn’t seem too concerned about Salesforce jumping in.
“Conversational AI is the future of enterprise software and it’s not a question of if or when. It’s all about the how, and we strongly believe that a Switzerland strategy is the only way to deliver on its promise. It’s no wonder we are the only company to be backed by Microsoft, Amazon and Salesforce,” he said.
Leary things there’s plenty of room for everyone and Salesforce getting involved will accelerate adoption for all players. “The Salesforce tide will lift all boats, and companies like Tact will see their profile increased significantly because while Salesforce is the leader in the category, its share of the market is still less than 20% of the market.”
Einstein is Salesforce’s catch-all brand for its artificial intelligence layer. In this case it’s using natural language processing, voice recognition technology and other artificial intelligence pieces to interpret the person’s voice and transcribe what they are saying or understand their request better.
Typically, Salesforce starts with a small set of functionality and the builds on that over time. That’s very likely what they are doing here, coming out with a product announcement in time for Dreamforce, their massive customer conference next week,
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Fresh out of Y Combinator, Leena AI scores $2M seed round
Leena AI, a recent Y Combinator graduate focusing on HR chatbots to help employees answer questions like how much vacation time they have left, announced a $2 million seed round today from a variety of investors including Elad Gil and Snapdeal co-founders Kunal Bahl and Rohit Bansal.
Company co-founder and CEO Adit Jain says the seed money is about scaling the company and gaining customers. They hope to have 50 enterprise customers within the next 12-18 months. They currently have 16.
We wrote about the company in June when it was part of the Y Combinator Summer 2018 class. At the time Jain explained that they began in 2015 in India as a company called Chatteron. The original idea was to help others build chatbots, but like many startups, they realized there was a need not being addressed, in this case around HR, and they started Leena AI last year to focus specifically on that.
As they delved deeper into the HR problem, they found most employees had trouble getting answers to basic questions like how much vacation time they had or how to get a new baby on their health insurance. This forced a call to a help desk when the information was available online, but not always easy to find.
Jain pointed out that most HR policies are defined in policy documents, but employees don’t always know where they are. They felt a chatbot would be a good way to solve this problem and save a lot of time searching or calling for answers that should be easily found. What’s more, they learned that the vast majority of questions are fairly common and therefore easier for a system to learn.
Employees can access the Leena chatbot in Slack, Workplace by Facebook, Outlook, Skype for Business, Microsoft Teams and Cisco Spark. They also offer Web and mobile access to their service independent of these other tools.
Photo: Leena AI
What’s more, since most companies use a common set of backend HR systems like those from Oracle, SAP and NetSuite (also owned by Oracle), they have been able to build a set of standard integrators that are available out of the box with their solution.
The customer provides Leena with a handbook or a set of policy documents and they put their machine learning to work on that. Jain says, armed with this information, they can convert these documents into a structured set of questions and answers and feed that to the chatbot. They apply Natural Language Processing (NLP) to understand the question being asked and provide the correct answer.
They see room to move beyond HR and expand into other departments such as IT, finance and vendor procurement that could also take advantage of bots to answer a set of common questions. For now, as a recent YC graduate, they have their first bit of significant funding and they will concentrate on building HR chatbots and see where that takes them.
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Forethought looks to reshape enterprise search with AI
Forethought, a 2018 TechCrunch Disrupt Battlefield participant, has a modern vision for enterprise search that uses AI to surface the content that matters most in the context of work. Its first use case involves customer service, but it has a broader ambition to work across the enterprise.
The startup takes a bit of an unusual approach to search. Instead of a keyword-driven experience we are used to with Google, Forethought uses an information retrieval model driven by artificial intelligence underpinnings that they then embed directly into the workflow, company co-founder and CEO Deon Nicholas told TechCrunch. They have dubbed their answer engine ‘Agatha.’
Much like any search product, it begins by indexing relevant content. Nicholas says they built the search engine to be able to index millions of documents at scale very quickly. It then uses natural language processing (NLP) and natural language understanding (NLU) to read the documents as a human would.
“We don’t work on keywords. You can ask questions without keywords and using synonyms to help understand what you actually mean, we can actually pull out the correct answer [from the content] and deliver it to you,” he said.
One of first use cases where they are seeing traction in is customer support. “Our AI, Agatha for Support, integrates into a company’s help desk software, either Zendesk, Salesforce Service Cloud, and then we [read] tickets and suggest answers and relevant knowledge base articles to help close tickets more efficiently,” Nicholas explained. He claims their approach has increased agent efficiency by 20-30 percent.
Forethought at work in Salesforce Service Cloud. Screenshot: Forethought
The plan is to eventually expand beyond the initial customer service use case into other areas of the enterprise and follow a similar path of indexing documents and embedding the solution into the tools that people are using to do their jobs.
When they reach Beta or general release, they will operate as a cloud service where customers sign up, enter their Zendesk or Salesforce credentials (or whatever other products happen to be supported at that point) and the product begins indexing the content.
Forethought in Zendesk. Screenshot: Forethought
The founding team, all in their mid-20s, have had a passion for artificial intelligence since high school. In fact, Nicholas built an AI program to read his notes and quiz him on history while still in high school. Later at the University of Waterloo he published a paper on machine learning and had internships at Palantir, Facebook and Dropbox. His first job out of school was at Pure Storage. All these positions had a common thread of working with data and AI.
The company launched last year and they debuted Agatha in private Beta 4 months ago. They currently have six companies participating, the first of which has been converted to a paying customer.
They have closed a pre-seed round of funding too, and although they weren’t prepared to share the amount, the investment was led by K9 Ventures. While Village Global, Original Capital and other unnamed investors also participated.
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Klarity uses AI to strip drudgery from contract review
Klarity, a member of the Y Combinator 2018 Summer class, wants to automate much of the contract review process by applying artificial intelligence, specifically natural language processing.
Company co-founder and CEO Andrew Antos has experienced the pain of contract reviews first hand. After graduating from Harvard Law, he landed a job spending 16 hours a day reviewing contract language, a process he called mind-numbing. He figured there had to be a way to put technology to bear on the problem and Klarity was born.
“A lot of companies are employing internal or external lawyers because their customers, vendors or suppliers are sending them a contract to sign,” Antos explained They have to get somebody to read it, understand it and figure out whether it’s something that they can sign or if it requires specific changes.
You may think that this kind of work would be difficult to automate, but Antos said that contracts have fairly standard language and most companies use ‘playbooks.’ “Think of the playbook as a checklist for NDAs, sales agreements and vendor agreements — what they are looking for and specific preferences on what they agree to or what needs to be changed,” Antos explained.
Klarity is a subscription cloud service that checks contracts in Microsoft Word documents using NLP. It makes suggestions when it sees something that doesn’t match up with the playbook checklist. The product then generates a document, and a human lawyer reviews and signs off on the suggested changes, reducing the review time from an hour or more to 10 or 15 minutes.
Screenshot: Klarity
They launched the first iteration of the product last year and have 14 companies using it with 4 paying customers so far including one of the world’s largest private equity funds. These companies signed on because they have to process huge numbers of contracts. Klarity is helping them save time and money, while applying their preferences in a consistent fashion, something that a human reviewer can have trouble doing.
He acknowledges the solution could be taking away work from human lawyers, something they think about quite a bit. Ultimately though, they believe that contract reviewing is so tedious, it is freeing up lawyers for work that requires a greater level of intellectual rigor and creativity.
Antos met his co-founder and CTO, Nischal Nadhamuni, at an MIT entrepreneurship class in 2016 and the two became fast friends. In fact, he says that they pretty much decided to start a company the first day. “We spent 3 hours walking around Cambridge and decided to work together to solve this real problem people are having.”
They applied to Y Combinator two other times before being accepted in this summer’s cohort. The third time was the charm. He says the primary value of being in YC is the community and friendships they have formed and the help they have had in refining their approach.
“It’s like having a constant mirror that helps you realize any mistakes or any suboptimal things in your business on a high speed basis,” he said.
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Dialpad dials up $50M Series D led by Iconiq
Dialpad announced a $50 million Series D investment today, giving the company plenty of capital to keep expanding its business communications platform.
The round was led by Iconiq Capital with help from existing investors Andreessen Horowitz, Amasia, Scale Ventures, Section 32 and Work-Bench. With today’s round, the company has now raised $120 million.
As technology like artificial intelligence and internet of things advances, it’s giving the company an opportunity to expand its platform. Dialpad products include UberConference conferencing software and VoiceAI for voice transcription applications.
The company is competing in a crowded market that includes giants like Google and Cisco and a host of smaller companies like GoToMeeting (owned by LogMeIn), Zoom and BlueJeans. All of these companies are working to provide cloud-based meeting and communications services.
Increasingly, that involves artificial intelligence like natural language processing (NLP) to provide on the fly transcription services. While none of these services is perfect yet, they are growing increasingly accurate.
VoiceAI was launched shortly after Dialpad acquired TalkIQ in May to take this idea a step further by applying sentiment analysis and analytics to voice transcripts. The company plans to use the cash infusion to continue investing in artificial intelligence on the Dialpad platform.
Post call transcript generated by VoiceAI. Screenshot: Dialpad
CEO Craig Walker certainly sees the potential of artificial intelligence for the company moving forward. “Smart CIOs know AI isn’t just another trendy tech tool, it’s the future of work. By arming sales and support teams, and frankly everybody in the organization, with VoiceAI’s real-time artificial intelligence and insights, businesses can dramatically improve customer satisfaction and ultimately their bottom line,” Walker said in a statement.
Dialpad is also working with voice-driven devices like the Amazon Alexa and it announced Alexa integration with Dialpad in April. This allows Alexa users to make calls by saying something like, “Alexa, call Liz Green with Dialpad” and the Echo will make the phone call on your behalf using Dialpad software.
According to the company website, it has over 50,000 customers including WeWork, Stitch Fix, Uber and Reddit. The company says it has added over 10,000 new customers since its last funding round in September, 2017.
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Google’s ‘Semantic Experiences’ let you play word games with its AI
Google does a great deal of research into natural language processing and synthesis, but not every project has to be a new Assistant feature or voice improvement. The company has a little fun now and then, when the master AI permits it, and today it has posted a few web experiments that let you engage with its word-association systems in a playful way.
First is an interesting way of searching through Google Books, that fabulous database so rarely mentioned these days. Instead of just searching for text or title verbatim, you can ask questions, like “Why was Napoleon exiled?” or “What is the nature of consciousness?”
It returns passages from books that, based on their language only, are closely associated with your question. And while the results are hit and miss, they are nice and flexible. Sentences answering my questions appeared even though they were not directly adjacent to key words or particularly specific about doing so.
I found, however, it’s not a very intuitive way to interact with a body of knowledge, at least for me. When I ask a question, I generally want to receive an answer, not a competing variety of quotes that may or may not bear on your inquiry. So while I can’t really picture using this regularly, it’s an interesting way to demonstrate the flexibility of the semantic engine at work here. And it may very well expose you to some new authors, though the 100,000 books included in the database are something of a mixed bag.
The second project Google highlights is a game it calls Semantris, though I must say it’s rather too simple to deserve the “-tris” moniker. You’re given a list of words and one in particular is highlighted. You type the word you most associate with that one, and the words will reorder with, as Google’s AI understands it, the closest matches to your word on the bottom. If you moved the target word to the bottom, it blows up a few words and adds some more.
It’s a nice little time waster, but I couldn’t help but feel I was basically just a guinea pig providing testing and training for Google’s word association agent. It was also pretty easy — I didn’t feel much of an achievement for associating “water” with “boat” — but maybe it gets harder as it goes on. I’ve asked Google if our responses are feeding into the AI’s training data.
For the coders and machine learning enthusiasts among you, Google has also provided some pre-trained TensorFlow modules, and of course documented their work in a couple of papers linked in the blog post.
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IBM can’t stop milking the Watson brand
More than seven years after IBM Watson beat a couple of human Jeopardy! champions, the company has continued to make hay with the brand. Watson, at its core, is simply an artificial intelligence engine and while that’s not trivial by any means, neither is it the personified intelligence that their TV commercials would have the less technically savvy believe.
These commercials contribute to this unrealistic idea that humans can talk to machines in this natural fashion. You’ve probably seen some. They show this symbol talking to humans in a robotic voice explaining its capabilities. Some of the humans include Bob Dylan, Serena Williams and Stephen King.
In spite of devices like Alexa and Google Home, we certainly don’t have machines giving us detailed explanations, at least not yet.
IBM would probably be better served aiming its commercials at the enterprises it sells to, rather than the general public, who may be impressed by a talking box having a conversation with a star. However, those of us who have at least some understanding of the capabilities of such tech, and those who buy it, don’t need such bells and whistles. We need much more practical applications. While chatting with Serena Williams about competitiveness may be entertaining, it isn’t really driving home the actual value proposition of this tech for business.
The trouble with using Watson as a catch-all phrase is that it reduces the authenticity of the core technology behind it. It’s not as though IBM is alone in trying to personify its AI though. We’ve seen the same thing from Salesforce with Einstein, Microsoft with Cortana and Adobe with Sensei. It seems that these large companies can’t deliver artificial intelligence without hiding it behind a brand.
The thing is this though, this is not a consumer device like the Amazon Echo or Google Home. It’s a set of technologies like deep learning, computer vision and natural language processing, but that’s hard to sell, so these companies try to put a brand on it like it’s a single entity.
Just this week, at the IBM Think Conference in Las Vegas, we saw a slew of announcements from IBM that took on the Watson brand. That included Watson Studio, Watson Knowledge Catalog, Watson Data Kits and Watson Assistant. While they were at it, they also announced they were beefing up their partnership Apple with — you guessed it — Watson and Apple Core ML. (Do you have anything without quite so much Watson in it?)
Marketers gonna market and there is little we can do, but when you overplay your brand, you may be doing your company more harm than good. IBM has saturated the Watson brand, and might not be reaching the intended audience as a result.
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Voicera lands $13.5 million with help from big-time enterprise investors
It seems that everyone agrees that meetings are a time suck. There have been many attempts to use technology to make it easier to organize and run them, but Voicera, a Bay area startup, is attacking the problem from a different angle. It wants to make it simpler to record meetings and pull out action items automatically using artificial intelligence. Today, it announced a $13.5 million Series… Read More
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Google Cloud’s Natural Language API gets content classification and more granular sentiment analysis
Google Cloud announced two updates this morning to its Natural Language API. Specifically users will now have access to content classification and entity sentiment analysis. These features are particularly valuable for brands and media companies For starters, GCP users will now be able to tag content as corresponding with common topics like health, entertainment and law (cc: Henry).… Read More
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