May
20
2020
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Directly, which taps experts to train chatbots, raises $11M, closes out Series B at $51M

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

May
14
2020
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Kustomer acquires Reply.ai to enhance chatbots on its CRM platform

Last December, when CRM startup Kustomer was announcing its latest round of funding — a $60 million round led by Coatue — its co-founder and CEO Brad Birnbaum said it would use some of the money to build more RPA-style automations into its platform to expand KustomerIQ, its AI-based product that helps understand and respond to customer enquiries to take some of the more repetitive load off of agents. Today, Kustomer is announcing some M&A that will help in that strategy: it is acquiring Reply.ai, a startup originally founded in Madrid that has built a code-free platform for companies to create customised chatbots to handle customer service enquires that use machine learning to, over time, become better at responding to those inbound contacts.

Kustomer, which has raised more than $170 million and is now valued at $710 million (per PitchBook), said it is not disclosing the financial terms of the deal.

Reply.ai — whose customers include Coca-Cola, Starbucks, Samsung, and a number of retailers and major ad and marketing agencies working on behalf of clients — had by comparison raised a modest $4 million in funding (with the last round back in 2018). Its list of investors included strategic backers like Aflac and Westfield (the shopping mall giant), as well as Seedcamp, Madrid’s JME Ventures, and Y Combinator, where Reply.ai was a part of its Startup School cohort in 2017.

Birnbaum said that the conversation for acquiring Reply.ai started before the global health pandemic — the two already worked together, as part of Reply.ai’s integrations with a number of CRM platforms. But active discussions, due diligence, and the closing of the deal were all done over Zoom. “We were fortunate that we got to meet before corona, but for the most part we did this remotely,” he said.

Reply.ai was founded back in 2016 — the year when chatbots suddenly became all the rage — and it managed to make it through that and then the subsequent trough of disillusionment, when a lot of the early novelty wore off after they were discovered to be not quite as effective as many had hoped or assumed they would be. One of the reasons for Reply.ai’s survival was that it had proven to be a builder of effective applications in one of the only segments of the market to become a willing customer and user of chatbots: customer service.

While a large part of the CRM industry — estimated to be worth some $40 billion in 2019 —  is still based around human interactions, there has been a growing push to leverage advances in AI, cloud services, and use of the internet as a point of interaction to bring more automation into the process, both to help those who are agents deal with more tricky issues, and to help bring overall costs down for those who rely on customer support as part of their service proposition.

That trend, if anything, is only getting a boost right now. In some cases, agents are unable to work because of social distancing rules in cases where customer queries cannot be handled by remote workers. In others, companies are seeing a lot of financial pressure and are looking to reduce expenses. But at the same time, with more people at home and unable to make physical queries at stores, the whole medium of customer support is seeing new levels of usage.

Kustomer has been taking on the bigger names in CRM, including Salesforce (where Birnbaum and his cofounder Jeremy Suriel previously worked), Zendesk and Oracle, by providing a platform that makes it easier for human agents to handle inbound “omnichannel” customer requests — another big trend, leveraging the rise of multiple messaging and communications platforms as potential routes to both speaking to customers and seeing them complain for all the world to see. So moving deeper into chatbots and other AI-powered tools is a natural progression.

Birnbaum said that one of its key interests with Reply.ai was its focus on “deflection” — the term for using non-human tools and services to help resolve inbound requests before needing to call in a human agent. Reply.ai’s tools have been shown to help deflect 40% of initial inbound queries, he noted.

“Some companies have been dealing with a significant increase in inbound volume, and it’s been hard to scale their teams of agents, especially when they are remote,” he said. “So those companies are looking for ways to respond more rapidly. So anything they can do to help with that deflection and let their agents be more productive to drive higher levels of satisfaction, anything that can enable self-service, is what this is about.”

Other tools in the Reply toolkit, in addition to its chatbot-building platform and deflection capabilities, include agent-assistant tools for suggesting relevant answers, as well as suggestions for tagging (for analytics) and re-routing.

“We are excited for Reply to join Kustomer and share its mission to make customer service more efficient, effective and personalized,” said Omar Pera, one of Reply.ai’s founders, in a statement. “As a long-time partner of Kustomer, we are able to seamlessly integrate our deflection and chatbots technologies into Kustomer’s platform and help brands more cost-effectively increase efficiency. We look forward to working with Brad and the entire team.”

Mar
25
2020
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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.

Apr
10
2019
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Salesforce and Google want to build a smarter customer service experience

Anyone who has dealt with bad customer service has felt frustration with the lack of basic understanding of who you are as a customer and what you need. Google and Salesforce feel your pain, and today the two companies expanded their partnership to try and create a smarter customer service experience.

The goal is to combine Salesforce’s customer knowledge with Google’s customer service-related AI products and build on the strengths of the combined solution to produce a better customer service experience, whether that’s with an agent or a chatbot..

Bill Patterson, executive vice president for Salesforce Service Cloud, gets that bad customer service is a source of vexation for many consumers, but his goal is to change that. Patterson points out that Google and Salesforce have been working together since 2017, but mostly on sales- and marketing-related projects. Today’s announcement marks the first time they are working on a customer service solution together.

For starters, the partnership is looking at the human customer service agent experience.”The combination of Google Contact Center AI, which highlights the language and the stream of intelligence that comes through that interaction, combined with the customer data and the business process information that that Salesforce has, really makes that an incredibly enriching experience for agents,” Patterson explained.

The Google software will understand voice and intent, and have access to a set of external information like weather or news events that might be having an impact on the customers, while Salesforce looks at the hard data it stores about the customer such as who they are, their buying history and previous interactions.

The companies believe that by bringing these two types of data together, they can surface relevant information in real time to help the agent give the best answer. It may be the best article or it could be just suggesting that a shipment might be late because of bad weather in the area.

Customer service agent screen showing information surfaced by intelligent layers in Google and Salesforce

The second part of the announcement involves improving the chatbot experience. We’ve all dealt with rigid chatbots, who can’t understand your request. Sure, it can sometimes channel your call to the right person, but if you have any question outside the most basic ones, it tends to get stuck, while you scream “Operator! I said OPERATOR!” (Or at least I do.)

Google and Salesforce are hoping to change that by bringing together Einstein, Salesforce’s artificial intelligence layer and Google Natural Language Understanding (NLU) in its Google Dialogflow product to better understand the request, monitor the sentiment and direct you to a human operator before you get frustrated.

Patterson’s department, which is on a $3.8 billion run rate, is poised to become the largest revenue producer in the Salesforce family by the end of the year. The company itself is on a run rate over $14 billion.

“So many organizations just struggle with primitives of great customer service and experience. We have a lot of passion for making everyday interaction better with agents,” he said. Maybe this partnership will bring some much needed improvement.

Mar
29
2019
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ServiceNow teams with Workplace by Facebook on service chatbot

One of the great things about enterprise chat applications, beyond giving employees a common channel to communicate, is the ability to integrate with other enterprise applications. Today, Workplace, Facebook’s enterprise collaboration and communication application, and ServiceNow announced a new chatbot to make it easier for employees to navigate a company’s help desks inside Workplace Chat.

The beauty of the chatbot is that employees can get answers to common questions whenever they want, wherever they happen to be. The Workplace-ServiceNow integration happens in Workplace Chat and can can involve IT or HR help desk scenarios. A chatbot can help companies save time and money, and employees can get answers to common problems much faster.

Previously, getting these kind of answers would have required navigating multiple systems, making a phone call or submitting a ticket to the appropriate help desk. This approach provides a level of convenience and immediacy.

Companies can brainstorm common questions and answers and build them in the ServiceNow Virtual Agent Designer. It comes with some standard templates, and doesn’t require any kind of advanced scripting or programming skills. Instead, non-technical end users can adapt pre-populated templates to meet the needs, language and workflows of an individual organization.

Screenshot: ServiceNow

This is all part of a strategy by Facebook to integrate more enterprise applications into the tool. In May at the F8 conference, Facebook announced 52 such integrations from companies like Atlassian, SurveyMonkey, HubSpot and Marketo (the company Adobe bought in September for $4.75 billion).

This is part of a broader enterprise chat application trend around making these applications the center of every employee’s work life, while reducing task switching, the act of moving from application to application. This kind of integration is something that Slack has done very well and has up until now provided it with a differentiator, but the other enterprise players are catching on and today’s announcement with ServiceNow is part of that.

Mar
19
2019
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Oracle adds more AI features to its suite of sales tools

As the biggest sales and marketing technology firms mature, they are all turning to AI and machine learning to advance the field. This morning it was Oracle’s turn, announcing several AI-fueled features for its suite of sales tools.

Rob Tarkoff, who had previous stints at EMC, Adobe and Lithium, and is now EVP of Oracle CX Cloud says that the company has found ways to increase efficiency in the sales and marketing process by using artificial intelligence to speed up previously manual workflows, while taking advantage of all the data that is part of modern sales and marketing.

For starters, the company wants to help managers and salespeople understand the market better to identify the best prospects in the pipeline. To that end, Oracle is announcing integration with DataFox, the company it purchased last fall. The acquisition gave Oracle the ability to integrate highly detailed company profiles into their Customer Experience Cloud, including information such as SEC filings, job postings, news stories and other data about the company.

DataFox company profile. Screenshot: Oracle

“One of the things that DataFox helps you you do better is machine learning-driven sales planning, so you can take sales and account data and optimize territory assignments,” he explained.

The company also announced an AI sales planning tool. Tarkoff says that Oracle created this tool in conjunction with its ERP team. The goal is to use machine learning to help finance make more accurate performance predictions based on internal data.

“It’s really a competitor to companies like Anaplan, where we are now in the business of helping sales leaders optimize planning and forecasting, using predictive models to identify better future trends,” Tarkoff said.

Sales forecasting tool. Screenshot: Oracle

The final tool is really about increasing sales productivity by giving salespeople a virtual assistant. In this case, it’s a chatbot that can help handle tasks like scheduling meetings and offering task reminders to busy sales people, while allowing them to use their voices to enter information about calls and tasks. “We’ve invested a lot in chatbot technology, and a lot in algorithms to help our bots with specific dialogues that have sales- and marketing-industry specific schema and a lot of things that help optimize the automation in a rep’s experience working with sales planning tools,” Tarkoff said.

Brent Leary, principal at CRM Essentials, says that this kind of voice-driven assistant could make it easier to use CRM tools. “The Smarter Sales Assistant has the potential to not only improve the usability of the application, but by letting users interact with the system with their voice it should increase system usage,” he said.

All of these enhancements are designed to increase the level of automation and help sales teams run more efficiently with the ultimate goal of using data to more sales and making better use of sales personnel. They are hardly alone in this goal as competitors like Salesforce, Adobe and Microsoft are bringing a similar level of automation to their sales and marketing tools

The sales forecasting tool and the sales assistant are generally available starting today. The DataFox integration will GA in June.

Sep
19
2018
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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.

Jun
29
2018
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Leena AI builds HR chatbots to answer policy questions automatically

Say you have a job with a large company and you want to know how much vacation time you have left, or how to add your new baby to your healthcare. This usually involves emailing or calling HR and waiting for an answer, or it could even involve crossing multiple systems to get what you need.

Leena AI, a member of the Y Combinator Summer 2018 class, wants to change that by building HR bots to answer questions for employees instantly.

The bots can be integrated into Slack or Workplace by Facebook and they are built and trained using information in policy documents and by pulling data from various back-end systems like Oracle and SAP.

Adit Jain, co-founder at Leena AI, says the company has its roots in another startup called Chatteron, which the founders started after they got out of college in India in 2015. That product helped people build their own chatbots. Jain says along the way, they discovered while doing their market research a particularly strong need in HR. They started Leena AI last year to address that specific requirement.

Jain says when building bots, the team learned through its experience with Chatteron that it’s better to concentrate on a single subject because the underlying machine learning model gets better the more it’s used. “Once you create a bot, for it to really add value and be [extremely] accurate, and for it to really go deep, it takes a lot of time and effort and that can only happen through verticalization,” Jain explained.

Photo: Leena AI

What’s more, as the founders have become more knowledgeable about the needs of HR, they have learned that 80 percent of the questions cover similar topics, like vacation, sick time and expense reporting. They have also seen companies using similar back-end systems, so they can now build standard integrators for common applications like SAP, Oracle and NetSuite.

Of course, even though people may ask similar questions, the company may have unique terminology or people may ask the question in an unusual way. Jain says that’s where the natural language processing (NLP) comes in. The system can learn these variations over time as they build a larger database of possible queries.

The company just launched in 2017 and already has a dozen paying customers. They hope to double that number in just 60 days. Jain believes being part of Y Combinator should help in that regard. The partners are helping the team refine its pitch and making introductions to companies that could make use of this tool.

Their ultimate goal is nothing less than to be ubiquitous, to help bridge multiple legacy systems to provide answers seamlessly for employees to all their questions. If they can achieve that, they should be a successful company.

May
09
2018
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ServiceNow chatbot builder helps automate common service requests

When it comes to making requests inside a company for new equipment or to learn about HR policies, it can be a frustrating experience for both sides of the equation. HR and IT are probably tired of answering the same questions. Employees are tired of calling a help desk for routine inquiries and waiting for answers. ServiceNow’s new bot-building technology is designed to alleviate that problem by providing a way to create an automated bot-driven process for routine requests.

The company claims that you can build these bots to provide end-to-end service. Meaning if you tell the bot you need a new phone, it can pull your records, understand what you currently have and order a new one all in the same interaction — and all within a common messaging interface such as Slack or Microsoft Teams.

It also works for customer service transactions to process routine customer inquiries without having to route them to a CSR to answer typical questions.

The new chatbot building tool called Virtual Agent, has been built into the ServiceNow Now platform and provides a way for developers to build conversational interfaces easily, says CJ Desai, chief product officer at ServiceNow. “[The Virtual Agent] enables our customers to develop a wide range of intelligent service conversations from a quick question to an entire business action through the messaging platform of their choice,” Desai said in a statement.

The announcement is part of a broader AI initiative on the part of ServiceNow, which purchased Parlo, a chatbot startup, just last week for an undisclosed amount of cash. The acquisition should help give ServiceNow more AI engineering talent and help them beef up their natural language processing (NLP) to further refine and improve their chatbot products moving forward, as the Parlo team and technology get incorporated into the ServiceNow platform.

The company claims that using these chatbots, customers can reduce call volume to help desks and customer service by 15-20 percent, using the standard argument that it should free humans to handle more difficult inquiries.

The company joins a slew of other platform players including Salesforce, IBM, Oracle, AWS, and others who are incorporating chatbot building technology into their platforms.

Apr
17
2018
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Google Cloud releases Dialogflow Enterprise Edition for building chat apps

Building conversational interfaces is a hot new area for developers. Chatbots can be a way to reduce friction in websites and apps and to give customers quick answers to commonly asked questions in a conversational framework. Today, Google announced it was making Dialogflow Enterprise Edition generally available. It had previously been in beta.

This technology came to them via the API.AI acquisition in 2016. Google wisely decided to change the name of the tool along the way, giving it a moniker that more closely matched what it actually does. The company reports that hundreds of thousands of developers are using the tool already to build conversational interfaces.

This isn’t just an all-Google tool, though. It works across voice interface platforms, including Google Assistant, Amazon Alexa and Facebook Messenger, giving developers a tool to develop their chat apps once and use them across several devices without having to change the underlying code in a significant way.

What’s more, with today’s release the company is providing increased functionality and making it easier to transition to the enterprise edition at the same time.

“Starting today, you can combine batch operations that would have required multiple API calls into a single API call, reducing lines of code and shortening development time. Dialogflow API V2 is also now the default for all new agents, integrating with Google Cloud Speech-to-Text, enabling agent management via API, supporting gRPC, and providing an easy transition to Enterprise Edition with no code migration,” Dan Aharon, Google’s product manager for Cloud AI, wrote in a company blog post announcing the tool.

The company showed off a few new customers using Dialogflow to build chat interfaces for their customers, including KLM Royal Dutch Airlines, Domino’s and Ticketmaster.

The new tool, which is available today, supports more than 30 languages and as a generally available enterprise product comes with a support package and service level agreement (SLA).

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