For customer service, Ultimate.ai‘s thesis is it’s not humans or AI but humans and AI. The Helsinki- and Berlin-based startup has built an AI-powered suggestion engine that, once trained on clients’ data-sets, is able to provide real-time help to (human) staff dealing with customer queries via chat, email and social channels. So the AI layer is intended to make the humans behind the screens smarter and faster at responding to customer needs — as well as freeing them up from handling basic queries to focus on more complex issues.
AI-fuelled chatbots have fast become a very crowded market, with hundreds of so-called ‘conversational AI’ startups all vying to serve the customer service cause.
Ultimate.ai stands out by merit of having focused on non-English language markets, says co-founder and CEO Reetu Kainulainen. This is a consequence of the business being founded in Finland, whose language belongs to a cluster of Eastern and Northern Eurasian languages that are plenty removed from English in sound and grammatical character.
“[We] started with one of the toughest languages in the world,” he tells TechCrunch. “With no available NLP [natural language processing] able to tackle Finnish, we had to build everything in house. To solve the problem, we leveraged state-of-the-art deep neural network technologies.
“Today, our proprietary deep learning algorithms enable us to learn the structure of any language by training on our clients’ customer service data. Core within this is our use of transfer learning, which we use to transfer knowledge between languages and customers, to provide a high-accuracy NLU engine. We grow more accurate the more clients we have and the more agents use our platform.”
Ultimate.ai was founded in November 2016 and launched its first product in summer 2017. It now has more than 25 enterprise clients, including the likes of Zalando, Telia and Finnair. It also touts partnerships with tech giants including SAP, Microsoft, Salesforce and Genesys — integrating with their Contact Center solutions.
“We partner with these players both technically (on client deployments) and commercially (via co-selling). We also list our solution on their Marketplaces,” he notes.
Up to taking in its first seed round now it had raised an angel round of €230k in March 2017, as well as relying on revenue generated by the product as soon as it launched.
The $1.3M seed round is co-led by Holtzbrinck Ventures and Maki.vc.
Kainulainen says one of the “key strengths” of Ultimate.ai’s approach to AI for text-based customer service touch-points is rapid set-up when it comes to ingesting a client’s historical customer logs to train the suggestion system.
“Our proprietary clustering algorithms automatically cluster our customer’s historical data (chat, email, knowledge base) to train our neural network. We can go from millions of lines of unstructured data into a trained deep neural network within a day,” he says.
“Alongside this, our state-of-the-art transfer learning algorithms can seed the AI with very limited data — we have deployed Contact Center automation for enterprise clients with as little as 500 lines of historical conversation.”
Ultimate.ai’s proprietary NLP achieves “state-of-the-art accuracy at 98.6%”, he claims.
It can also make use of what he dubs “semi-supervised learning” to further boost accuracy over time as agents use the tool.
“Finally, we leverage transfer learning to apply a single algorithmic model across all clients, scaling our learnings from client-to-client and constantly improving our solution,” he adds.
On the competitive front, it’s going up against the likes of IBM’s Watson AI. However Kainulainen argues that IBM’s manual tools — which he argues “require large onboarding projects and are limited in languages with no self-learning capabilities” — make that sort of manual approach to chatbot building “unsustainable in the long-term”.
He also contends that many rivals are saddled with “lengthy set-up and heavy maintenance requirements” which makes them “extortionately expensive”.
A closer competitor (in terms of approach) which he namechecks is TC Disrupt battlefield alum Digital Genius. But again they’ve got English language origins — so he flags that as a differentiating factor vs the proprietary NLP at the core of Ultimate.ai’s product (which he claims can handle any language).
“It is very difficult to scale out of English to other languages,” he argues. “It also uneconomical to rebuild your architecture to serve multi-language scenarios. Out of necessity, we have been language-agnostic since day one.”
Albeit, for its part Digital Genius, tells us it currently has 15 languages live-deployed to customers — and says it has “the easy ability to add others as needed”. So disputes Ultimate.ai’s claim it’s at any linguistic disadvantage.
“Our technology and team is tailored to the customer service problem; generic conversational AI tools cannot compete,” Kainulainen adds. “Within this, we are a full package for enterprises. We provide a complete AI platform, from automation to augmentation, as well as omnichannel capabilities across Chat, Email and Social. Languages are also a key technical strength, enabling our clients to serve their customers wherever they may be.”
The multi-language architecture is not the only claimed differentiator, either. Kainulainen points to the team’s mission as another key factor on that front, saying: “We want to transform how people work in customer service. It’s not about building a simple FAQ bot, it’s about deeply understanding how the division and the people work and building tools to empower them. For us, it’s not Superagent vs. Botman, it’s Superagent + Botman.”
So it’s not trying to suggest that AI should replace your entire customers service team but rather enhance your in house humans.
Asked what the AI can’t do well, he says this boils down to interactions that are transactional vs relational — with the former category meshing well with automation, but the latter (aka interactions that require emotional engagement and/or complex thought) definitely not something to attempt to automate away.
“Transactional cases are mechanical and AI is good at mechanical. The customer knows what they want (a specific query or action) and so can frame their request clearly. It’s a simple, in-and-out case. Full automation can be powerful here,” he says. “Relational cases are more frequent, more human and more complex. They can require empathy, persuasion and complex thought. Sometimes a customer doesn’t know what the problem is — “it’s just not working”.
“Other times are sales opportunities, which businesses definitely don’t want to automate away (AI isn’t great at persuasion). And some specific industries, e.g. emergency services, see the human response as so vital that they refuse automation entirely. In all of these situations, AI which augments people, rather than replaces, is most effective.
“We see work in customer service being transformed over the next decade. As automation of simple requests becomes the status-quo, businesses will increasingly differentiate through the quality of their human-touch. Customer service will become less labour intensive, higher skilled work. We try and imagine what tools will power this workforce of tomorrow and build them, today.”
On the ethics front, he says customers are always told when they are transferred to a human agent — though that agent will still be receiving AI support (i.e. in the form of suggested replies to help “bolster their speed and quality”) behind the scenes.
Ultimate.ai’s customers define cases they’d prefer an agent to handle — for instance where there may be a sales opportunity.
“In these cases, the AI may gather some pre-qualifying customer information to speed up the agent handle time. Human agents are also brought in for complex cases where the AI has had difficulty understanding the customer query, based on a set confidence threshold,” he adds.
Kainulainen says the seed funding will be used to enhance the scalability of the product, with investments going into its AI clustering system.
The team will also be targeting underserved language markets to chase scale — “focusing heavily on the Nordics and DACH [Germany, Austria, Switzerland]”.
“We are building out our teams across Berlin and Helsinki. We will be working closely with our partners – SAP, Microsoft, Salesforce and Genesys — to further this vision,” he adds.
Commenting on the funding in a statement, Jasper Masemann, investment manager at Holtzbrinck Ventures, added: “The customer service industry is a huge market and one of the world’s largest employers. Ultimate.ai addresses the main industry challenges of inefficiency, quality control and high people turnover with latest advancements in deep learning and human machine hybrid models. The results and customer feedback are the best I have seen, which makes me very confident the team can become a forerunner in this space.”