Seattle-based startup Ethosphere, a voice-enabled artificial intelligence platform for retail operations, said today it raised $2.5 million in pre-seed funding to bring the power of large language models to brick and mortar store floors to help sales associates deliver exceptional in-person service.

Point72 Ventures led the round, with participation from AI2 Incubator, Carya Ventures, Pack VC, Hike Ventures and J4 Ventures.

Founded in 2024, the company has built a platform that helps retailers that use data from front-line interactions with customers to generate coaching insights for associates. It comes in the form of guidance through the use of large language models and voice AI.

“AI is bringing change to every industry, and retail is no exception, but there is a significant gap in how the technology can be applied in a useful, human-focused manner,” said Evan Smith, cofounder and chief executive of Ethosphere.

Smith stated that the company takes a human-centric approach to improve the purchasing experience for customers, as this positively affects retailers’ bottom lines. When customers have a more enjoyable experience in-store due to effective salespeople, they are more likely to return or spend more at that establishment.

The same is true for employee morale. Service workers can often feel unseen by management for their accomplishments and hard work. Much of the modern retail landscape has become driven by outcomes that can be tracked and put in a ledger rather than the day-to-day experiences and context of work on the sales floor. This can become a black spiral for frontline workers who are guided to chase results instead of feeling empowered to engage with customers.

The company’s platform uses wearable microphones to record interactions between customers and associates. These recordings are processed using a set of large language models that transcribe the audio to gain insights into how salespeople are learning and developing their customer-facing skills on the job. The platform then offers specific, individualized feedback and coaching to help them improve their performance on the sales floor.

The platform’s guidance consists of praise, data insights and suggestions for improvement.

Ethosphere said the messaging provided can be tailored to the specific brand voice of the business, including adhering to jargon and company culture.

Management has access to a dashboard that allows them to see both the areas where their team excels and the challenges they need to address. The platform also provides recommendations on next steps to help managers determine the best way to support associates in their work. This includes assisting them by reducing bias in how they view their team, celebrating high-performers and addressing team building.

“In an increasingly busy landscape flooded with theoretical AI, Ethosphere stood out to us with a practical, powerful application that we believe has the potential to directly impact the sales and customer experience,” said Sri Chandrasekar, managing partner at Point72 Ventures.

The company said it would use the funds to scale up program pilots with major retailers to assist them with enhancing support for frontline employees.

Image: Pixabay

Many people worry that AI is going to take their job. But a recent survey conducted by the Federal Reserve Bank of New York found that rather than laying off workers, many AI-adopting firms are retraining their workforces to use the new technology. Yet there’s little research into whether existing job-training programs are helping workers successfully adapt to an evolving labor market.

A new working paper starts to fill that gap. A team of researchers, including doctoral candidate Karen Ni of the Harvard Kennedy School, analyzed worker outcomes after they participated in job-training programs through the U.S. government’s Workforce Innovation and Opportunity Act. Researchers looked at administrative earnings records spanning the quarters before and after workers completed training. Then they analyzed workers’ earning when transitioning from or into an occupation that was highly “AI-exposed” — a term that refers to the extent of tasks that have the potential to be automated, both in the traditional computerization sense and through generative AI technology.

Across the board, the training programs demonstrated a positive impact, with displaced workers seeing increased earnings after entering a new occupation. Still, those earnings were less for someone who targeted a high AI-exposed occupation than someone who targeted a low AI-exposed occupation.

In this edited conversation, Ni explains the role that job-training programs play as AI use is transforming the labor market.

With all the discussion around job displacement and AI, what led you to focus on retraining in particular?

When thinking about the disruptions that a new large-scale technology might have for the labor market, it’s important to understand whether it’s possible for us to help workers who might be displaced by these technologies to transition into other work. So we homed in on, OK, we know that some of these workers are being displaced. Now, what can job training services do for them? Can they improve their job prospects? Can they help them move up in terms of earnings? Is it possible to retrain some of these workers for highly AI-exposed roles?

We wanted to help document the transition and adaptability for these displaced workers, especially those who are lower income. Because then we can think about how we can support these workers, whether it be better investing in these kinds of workforce-development programs or training programs, or adapting those programs to the evolving labor market landscape.

What can we learn by looking at data from government workforce development programs?

One of the big advantages of using these trainees is that it’s nationwide, and so it’s nationally representative. That allows us to take a broad look at trainees across the entire country and capture a fair bit of heterogeneity in terms of their occupations and backgrounds. For the large part, our sample captures displaced workers who tend to be lower income, making an average of $40,000 a year. Some are making big transitions from one occupation to a completely different one. We also see a fair number of people who end up going into the same types of jobs that they had before. We think those workers are likely trying to develop new skills or credentials that might be helpful to enter back into a similar occupation. Some of these people might be displaced from their occupation because of AI. But the job displacement in this sample could be for any reason, like a regional office shutting down.

Can you provide some examples of highAI-exposed careers versus low AI-exposed careers?

AI exposure refers to the extent of tasks within an occupation that could potentially be completed by a machine or a large language model. Among our sample of job trainees, some of the most common high AI-exposed occupations were customer service representatives, cashiers, office clerks. On the other end of the spectrum, the lowest AI-exposed workers tended to be manual laborers, such as movers, industrial truck drivers, or packagers.

AI retrainability by occupation

What were your main findings?