Ambit: Build your own chatbots

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I am a chatbot sceptic. I think this is because they over promise and under deliver. They try to appear intelligent, but they’re not capable of true understanding. They remind me of bad search: where I can’t quite work out the right combination of words to get the information I want. I also find their attempts to be personable to be as annoying as clippy. I’m not alone with this point of view. Just today I read Justin Lee’s article Chatbots were the next big thing: what happened?

With that context I ran into Tim Warren, COO and co-founder of Ambit, while at the Hitech Awards in Christchurch. We connected after the event and I found out more about Ambit’s approach to chatbots. Tim has a diverse background. He started in software and then moved into finance, running Goldman Sachs/JB Were as COO. He and his co-founders spent quite a bit of time researching what sort of start-up they wanted to do, before settling on chatbots aimed at the enterprise (for now).

They came up with their first proof of concept in 2016 and by mid last year they had a product they could sell. They now have a reasonable amount of recurring revenue and their 14-person company is close to break even and growing quickly. There’s nothing like a little revenue to counter a dose of scepticism.

They like to describe their platform as WordPress for conversational AI. Customers can create their own chatbots, but at the moment this is done by conversational designers at Ambit. Their aim is to have a completely self-service product.  They initially started using the MS Luis platform, but found that it had limitations, so they wrote their own.

Their core technology is trying to distil intent from utterances: that is matching what is typed into the chat window by the user to one of their core tasks. The system learns from examples.  Typically, they need about 5 utterance examples to reasonably interpret a new utterances and connect it to an intent. Their system returns a confidence score for the various intents they have.  For example, the following questions might all be around starting an application for a mortgage.

  • I’d like to borrow some money for a house
  • Can I apply for a mortgage?
  • How do I get a loan for a property?
  • What are your mortgage rates?
  • What are your interest rates for a house loan?

A conversation is not represented linearly, but is a web of nodes. They have a demo where they can show how a web of nodes representing a conversation can be created quickly using their platform.

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The Ambit chatbot builder

The other key differentiator is that they have a hierarchical language model that means while an individual chat design belongs to a particular customer, the language and synonyms belong to the industry and all customers in the industry benefit as Ambit learns more.

Another important part of the platform is the analytics, where they can see how many of the different types of conversations are happening. They can also see where drop offs are in conversation funnels, to inform them where they might need to change the design to improve performance. As part of this they can inspect conversations to help guide them and learn new utterances.

Their client applications fall into three categories:

  • Navigate:  helping people find information on the website
  • Acquire: Using the bot to help generate sales leads
  • Support: Using the bot to automate the repetitive tasks and leaving more difficult support tasks to humans.

They charge their customers a fixed monthly fee. For that they get a certain number of conversational nodes and a generous number of conversations that can happen on the platform. Integration with the website is simple, with the customer being able to tailor the appearance via CSS.

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Chatbot at Squirrel Mortgages

They have the ability to integrate voice to text – but have not seen the demand for it yet. At this stage they support English only, however they could build functionality using partner tools such as Watson from IBM as a stop gap before they build their own multi-lingual functionality.

They are too new to have really solid customer case studies and hard ROI metrics. They are working on that now. However, the customer value is self-evident: lower cost to serve and happier customer facing people because they don’t have to deal with repetitive, easy questions.

While Ambit haven’t converted this sceptic to a chatbot fan, I can see there is a business here. As they get more customers and build out the product the chatbots will only improve. I’ll be interested to see how the business develops.

Jade: developing AI capability for chatbots and predictive modelling

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A couple of weeks ago I sat down with Eduard Liebenberger who is the head of digital at Jade to find out a little about their AI capabilities and plans. Eduard is passionate about AI and the possibilities it brings to transform the way we communicate with businesses.

In Eduard’s words, Jade’s core focus is around freeing people from mundane/repetitive tasks and instead allow them to apply their creativity/expertise to more challenging tasks – and the JADE development, database and integration technologies. Eduard and the team at Jade have been watching recent developments in AI and identifying which of these they can use to help their customers. Their first foray has been into conversation interfaces (chatbots). They’ve developed a number of showcases, including an insurance chatbot called TOBi which shows how the technology can be used to make a claim, change contact details etc. From their they have started rolling out this technology into existing customers.

The chatbot uses natural language processing and sentiment analysis. It aims to make businesses interactions with their customers more efficient by allowing them to communicate via conversations that don’t have to be in real time, like a phone call and are more intuitive than a web form. Jade’s main advantage with their existing customers is that they have already done the tricky integration work with the back-end systems and so can fairly quickly add a chatbot as an alternative to an existing interface. Jade’s focus on the digital experience means they invest heavily into making this a natural and human-like interaction. For non-Jade customers their attraction is their ability to deliver a whole solution and not just the chatbot.

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Another advantage Jade has is that through their existing customers they have access to a lot of data that can be used to power machine learning applications. One example Eduard talked about was a summer intern project with a NZ university to try and identify students at risk of dropping out.  This was done using the data in student record database which is powered by Jade and contains several years’ of records. In just a few weeks the interns built a predictive model that was able to predict which students were likely to drop out with 90%+ accuracy. Ed is a big fan of rapid development for these types of proof of concept projects and doesn’t believe it should cost a fortune to get value from AI.

Overall, I think it’s fair to say that Jade’s AI capability is nascent. However, it’s positive to see that they are looking to build capability, understandably with a focus on the business benefits to their customers. I’m keen to see how it develops.

For those that want to find out more, Eduard is delivering the keynote at Digital Disruption X 2018 in Sydney, and presenting at DX 2018 and the AI Day in Auckland, all later this month. He’s a busy man.