Roughly speaking, the bot tries to understand the meaning of a question and the context around it. Then it goes into its knowledge base to find an answer and, in some cases, perform a task—such as compiling or displaying information. It also learns as it goes, trying to make sense of all the different ways people may ask the same type of question.
“The hardest problem we face right now,” May says, is “trying to automatically map tasks to questions.”
Not surprisingly, Talla is working on other types of bots as well—for example, one for assisting a company’s social media and marketing efforts. May says Talla’s software is currently being tested by about 50 companies—the sweet spot so far is customers with 100 to 1,000 employees—and its public release is slated for July.
It’s early days indeed. Talla has just under 10 employees and is looking to grow judiciously. Given how big and open the market is for messaging apps and bots, May says, he would like to “focus on profitability as opposed to pure revenue growth.” Translation: raise a modest venture round, keep burn rates low, and start selling.
Tech investors not involved with the company see upside in A.I.-based assistants from smaller players. “I do see potential for [business-to-business] applications, and I think startups can compete,” says Matt Fates, a general partner with Ascent Venture Partners. “Perhaps not on the underlying science and infrastructure, but certainly in how it is applied to various industries and verticals. By narrowing the purpose and focus of the virtual assistant, it can also increase the accuracy.”
That bodes well for Talla—and other startups blazing narrow, focused trails in A.I. and machine learning. Around Boston, they include Gamalon, led by Lyric Semiconductor co-founder Ben Vigoda, in probabilistic programming; Semantic Machines, from Voice Signal Technologies’ Dan Roth, in conversational interfaces; DataRobot, led by Jeremy Achin, in data science; Indico, led by Slater Victoroff, in software development; Nara Logics, led by Jana Eggers, in recommendations and finance; and Sentenai, led by Rohit Gupta, in machine learning for the Internet of things. (Sentenai just announced a $1.8 million seed round from Boston-area investors today.)
Talla and its peers have their work cut out for them, of course. These are hard problems to solve. Virtual assistants, specifically, may seem like relatively low-hanging A.I. fruit—see X.ai for scheduling meetings, Spiro for improving sales, and Google Now for managing search and smartphone features. But there’s a reason they haven’t fully taken off yet.
“These systems will never be perfect, due to all the pieces that have to fit together to make it ‘correct,’” Fates says, “and then taking the intended and appropriate action.” He adds that “the cost of getting it wrong on the [consumer] side is fairly minor, whereas making a mistake on my business calendar with an appointment can be far more embarrassing or difficult.”
