Our solution's stage of development:Pilot
We use artificial intelligence to develop emotional intelligence. With a platform that integrates voice interface with a machine learning layer, we have re-imagined talk therapy to be more elegant, efficient, and effective.
Mental health services are helpful, but the mental health system is broken. For an industry that’s meant to reduce stress, there are so many stressful steps just to get started! Moreover in urban areas, more than half of mental health counselors do not take insurance; and in rural areas, there is a major lack of available counselors in general. So although the demand for services is high, access to them remains low. Our online solution lowers the barrier to entry, while providing precision care that is data-based and outcome-oriented.
Why our solution will solve the problem:
From 2011-2016, our CEO owned and operated one of the first online therapy companies where sessions were facilitated remotely, through video and journaling. Of the thousands of clients served, 42% reported never having tried therapy before; for reasons including access, cost, and shame.
For in-person appointments, the modal number of sessions is one, meaning clients usually do not continue with therapy either due to lack of personality fit, cost sustainability, discomfort with the process, or scheduling availability. Our method cuts through all of that for a more self-directed approach that provides actionable data and guidance quickly and conveniently.
Our target outcomes:
1 We want our technology to make therapy available to as many people as possible within as many cultural frameworks as possible. Mental health treatment has always been biased by being based on the emotional experience of white, cishet men. A major goal is to build the largest database of emotion detection in voice to understand the wide range of feelings across different genders, cultures, and socioeconomic statuses.
2 We want to change the conversation about what therapy is and whom it’s for.
3 We will answer the question: “What makes you feel better when you feel bad?”
The populations we will benefit initially:
The regions we will benefit initially:
The technologies we employ:
Why our solution is unique:
Psychotherapy is an ancient practice in need of an update. The current marketplace model has done that to an extent by making therapy accessible online, but I don't think it went far enough in evolving the method so it's simpler and streamlined. With our tech, we've eliminated many of the clunky first steps to get straight to the guidance you seek. Our vision is to become the 23 & Me of mental health with a path towards building an intuitive, conversational therapy bot that learns more about you and your personality the more you engage with it.
Why our solution is human-centered:
In many countries, psychology isn't even a field you can study, let alone a service you can access. With our method, we can deploy tech-enabled mental health services that are data-based, judgment-free, and culturally competent.
How people will access our solution:
Our site will be available in different languages by Q2 2018. Our services will be below the standard industry rate of $200/hour, and we hope to have corporate sponsorships to subsidize consultations for populations in need, such as active duty military, new parents, and youth living below the poverty line.
Technology-Readiness Level:1-3 (Formulation)
Where we are located:
How we will sustain our team financially:
We've raised a friends and family round of $25K to build the platform and beta test this summer. Once we've nailed down the unit economics and user experience, we'll open a seed round in Q4 2017. Additionally, we have been in talks with X Factor (a pre-seed, female-focused fund for Flybridge Capital) for a $100K investment. We'll also plan to apply to Y Combinator for the Winter 2018 batch, as Bea is an alumnus of the program.
The factors limiting our success:
JB and I are intentionally keeping the team and budget quite lean, and are both taking on other consulting projects to keep burn low. However, by Q1 2018, we will need to staff data scientists and therapists, so fundraising will be a priority soon. Also, we are building an exclusive data set of emotion detection which will require hundreds of users, but we want to make sure we keep the quality of the service high while onboarding as many new clients as possible, so we'll have to be strategic about timing user acquisition.
How long we have been working on our solution:Less than 1 year
Our expected annual budget:
How much of our budget we've secured to date:
We're looking for partners in these fields:
Why we're applying to Solve:
Mental health is a massive and complicated issue that affects everyone, and we believe the right talk at the right time can make all The Difference. So solving this problem is ambitious, but necessary. JB and I both have extensive domain expertise in our respective fields, but given the complexity of the problem and our interdisciplinary solution, we think we could greatly benefit from engaging the Solve community's expertise, talent, and resources as we grow.
Our current partners:
Chris Wiggins, Chief Data Scientist at the New York Times
Rachael Norman, Better
Jesse Paquet and Tom Paquette, Tag Bio