Keenon Werling would be the first to agree that conversational AI is regularly overhyped. So instead of taking the traditional approach and gloating about a glitzy new deeper learning algorithm to pitchhis new ventureEloquent Labs,Werling instead opted to differentiate by optimizingsomething far more low-tech, people.The startups special sauce is embracing a mix of AI, crowd workers on Amazons Mechanical Turk and traditional customer service representatives to improve experiences while cutting costs.
The companys basic plan is to sell a conversational assistant named Elleto small businesses on Shopify that integrates directly with online shops to help customers with common problems like product tracking, managing returns, executing cancellations and answering FAQs. But things start to get interesting when crowd sourced labor is mixed into that more traditional model.
Companies like Digital Genus have promised human + AI for customer support for quite some time. To make experiences more seamless for the average person just looking to return a sweater, moststartups in the space train their models to know when to give up. This stops conversations from spiraling out of control into computational purgatory. When an end user asks something not in the repertoire of the AI, a human customer service agent is seamlessly brought into the loop to finish the conversation.
This interplay between man andmachine has saved companies a lot of money.Werling explains that retailers spend an average of $5 for every human customer service interaction, so every issue that can be handled with a machine is cost-saving.
Butthe thesis behindEloquent Labsis that companies still spend too much money usingcustomer service agents for tasks that machines can almost do, but just lackenough confidenceto execute.
Werlings work in college centered around crowd-labor exchanges like Amazons Mechanical Turk. These platforms enable hundreds of thousands of people to contribute their time to complete relatively mundane tasks online in exchange for money.By integrating Turk with human customer service representatives and AI,Eloquent Labs is able to save even more money for enterprises.
In practice, most machine learning is a game of classification. You and I enter words into a chat thread, and a machine attempts to classify my jumbled mess into some semblance of meaning that can be translated to a pre-set list of skills with some degree of probability. If the machine is close to certain that Im asking about when my order will arrive, its easy.
But, if I didnt use the word order or shipping (oversimplifying), the model might be 62 percent sure DHL timeline means when will my package arrive, but its not enough confidence to act on. Instead of wasting time and money making a service agent determine what those words mean, workers on Turk can manually classify the request.
The other benefit of this approach is that these crowd-sourced workers are actually helping to trainEloquents machine learning models. All of this benefit comes from a workforce that doesnt really need training and can work on-demand in short time segments.
The company is still early in sales and business development, so there arent any customers paying forit yet. Werling wants his company to take an Apple approach to the market and really nail the execution first because his company is breathing down the back of giants like Zendesk.
Elle was also designed to solve a number of human computer interaction problems that currently exist in the space. For example,Eloquent offers businesses a ride-along mode so that they can approve and reject responses generated by Elle as they gain trust in the assistant to handle interactions with important customers.
The team seems to be doing a good job prioritizing items in its product roadmap. Theres still a lot of potential for a company likeEloquent Labs to optimize further by enabling conversations passed off to customer service reps (the completelybroken ones) to pass back to machines. That bi-directional interplay is proving to be a challenge for everyone given the current state of machine intelligence, so its probably a smart move forEloquent Labs to move forward without it. But eventually it will help further differentiate players in the competitive market.