Industry leaders have gone on record saying that, after the smartphone, AI will be the next “big thing”. The Barcelona-based Artificial Solutions has been in the sector since 2001, and it has developed several technologies to push the industry forward, particularly in an effort to make other brands competitive against the usual tech leaders.

Andy Peart, Chief Marketing & Strategy Officer of the company, told us a bit more about the challenges of AI and the strengths of Teneo, Artifical Solutions’ main product.

First the basics, what can you tell me about Teneo? What’s your product about?

Teneo enables enterprises to rapidly build a range of artificially intelligent natural language applications from digital employees and mobile personal assistants, to wearables, bots and IoT interfaces, in 35 languages – all from a single platform. Deep analytics provides the backbone to deliver true machine learning and implicit personalization that enables greater understanding about an individual, their likes and preferences, through interaction only.

Historically, creating these conversational interfaces has required specialist skills, significant resources and a great deal of time. However, Teneo’s advanced machine learning capabilities automatically writes the complex underlying language code and algorithms that simulate the way a human thinks.

Can you elaborate about what Natural Language Interaction (NLI) is? What makes it different from other voice interfaces?

Natural Language Interaction (NLI) enables people to interact with any connected device using normal, everyday language. It understands the meaning of conversational input, and reacts accordingly, creating value and enhancing the user experience.

With NLI, users can converse with technology using complex sentences, containing multiple pieces of information and more than one request. There is no need for the user to repeat details during a conversation because memory, personal preferences and contextual understanding comes into play, just as it would in a conversation with a real person.

Into cars specifically, what are the main challenges of developing a truly conversational in-car interface?

While the automotive industry is no stranger to in-car voice recognition, until recently it had the reputation for being clunky and command-driven.  In-car apps such as Siri and Alexa are changing that perception, but they too, still have limitations.

Developing natural language applications that are truly conversational can be resource intensive. Both in terms of the number of highly skilled personnel required to build it, and the amount of data it takes to just to train the system. In addition, most natural language interfaces don’t understand complex sentences, remember pertinent facts, or bring the user back on track to the goal of the interaction when the customer veers off to ask another question.

While it might be tempting to OEM a pre-built app by one of the tech giants, this restricts the functionality available, and therefore the opportunity to differentiate the brand and the vehicle. More importantly, it also means losing control of one of the most important assets to come out of conversational systems—customer data.

With autonomous vehicles on the horizon and commercial autopilot technologies like Tesla’s, do you conceive conversational in-car interfaces for different ways of driving (manual and autonomous)? Is that a distinction you make?

Our technology allows developers to build the application they need, from digital co-driver to infotainment manager. It’s entirely up to the developer to decide how they wish the car to respond in different situations.

In addition, we don’t see that relationship between car and driver ending at the end of a journey. For example, because Teneo is able to persist conversations from device to device, users would be able to talk to their in-car systems, even when they’re not in the car. Using other channels such as a mobile app or messenger chatbot, they can find the answer those pesky questions such as, “I’m going to Birmingham today, do I have enough fuel?”, before they even leave for work in the morning.  This type of interaction allows for proactive responses such as offering to schedule a fuel stop at their favorite gas station or alert a customer if their daily commute has a delay.

“The diversity of how conversational applications are being used is quite staggering”



Aside from cars, you also work with other sectors. Which one of these do you see the most potential in? Are there “unexploited” sectors that could benefit from Teneo/NLI?

There isn’t an industry that doesn’t have some of its major players investing in AI technology. Some are just starting out at the beginning of their digital transformation, others have moved to the next stage and are rolling out applications across multiple platforms in multiple languages.  The diversity of how conversational applications are being used is quite staggering. Our main markets beyond the automation industry are currently finance, energy, retail, travel, telecoms and smart homes,  but they are by no means exclusive.

On the technical side of things, what do you think are the necessary ingredients to build a successful system? What technical strengths does Artificial Solutions have?

Teneo addresses all three aspects of the development process to deliver truly humanlike understanding. Firstly it provides the data needed to train the system, with its inclusive large, curated data resources, equivalent to millions of conversations and the tools for quickly expanding them if required.  Competitor products rely solely on the enterprise having already curated the data, which is often not practical, particularly if it is addressing a new opportunity within the business.

Secondly, it takes a hybrid approach to algorithms to manipulate the data. The backbone of the Teneo platform is a rule-based algorithm, but some components make use of statistical algorithms. This is a key advantage over purely statistical systems, which cannot work without curated training data.

Finally it provides a unique graphical user interface so that humans can easily construct the intelligence behind human-machine conversations to ensure that natural language applications properly understand the context of the conversation – every time.

What are some challenges you think the AI industry will face both in the short (5 years) and long term (15-20 years)?

To date, the Tech Giants have enjoyed a great deal of mindshare in the AI industry and, with consumer facing products like Alexa and Siri, it makes sense. In fact, a survey by Creative Strategies polled the habits of mobile users all over the world, and a full two-thirds of respondents said they enjoyed speaking conversationally to their mobile devices and were eager to see that same functionality expanded to places like their cars and living rooms. In other words, thanks to the Tech Giants individuals are becoming more comfortable and more trusting with AI-level engagement.

But the comfort-level with the technology is only one hurdle. For enterprises, it’s clear that conversational AI applications will become as important to their customer engagement strategy by 2020 as the website was in 2000 or the app in 2010. However, the enterprise can’t build a business strategy around an experience owned by Amazon, Apple or Google.

The conversational AI experience will be critical to a company’s brand, and the data acquired from customer engagement will be the most vital key differentiator for brand equity. Companies who maintain ownership of their brand and their unique customer data through conversational UI experiences will be the ones who differentiate from the Tech Giants and position themselves as leaders in today’s on demand economy.