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Input

Tara Krishnamurthy
Founder & CEO at Mindbridge Health

I came to this problem from the wrong side of it. Before starting Mindbridge, I spent eight years in employee benefits consulting at Mercer, where I watched companies pour budget into EAP contracts that almost nobody used. The utilization rates were embarrassing — typically three to five percent — and the employers had no visibility into why, and employees had no real reason to trust a service their company had procured for them. That frustration is the founding thesis of everything we have built.

I am not a technical founder. My background is in organizational psychology and benefits strategy, and I want to be clear about that because I think it shapes how we approach the product. I hire for depth in the engineering and clinical science functions, and I try to stay out of their way on implementation decisions. What I do bring is an unusually detailed understanding of how HR buyers think, what procurement cycles look like inside a mid-market company, and what employees actually need to feel safe using a mental health tool their employer is paying for.

Mindbridge operates on two surfaces simultaneously. On the consumer side, we have a self-guided app that individuals can download, access anonymously, and use for mood tracking, guided CBT exercises, and on-demand text support from licensed therapists. On the employer side, we sell a SaaS dashboard that gives HR and benefits teams aggregate, anonymized population-level insights — utilization trends, engagement cohorts, early signals of workforce stress — without ever exposing individual data. The privacy architecture was the hardest design problem we faced in the first twelve months, and we went through three iterations before we had something that the clinical team, the legal team, and the product team could all live with.

We closed our Series A last year and used most of it to build out the AI layer on both surfaces. On the consumer side, we now have an intake and session-routing model that triages incoming users based on their self-reported history and presenting concerns, and routes them either to self-guided content, asynchronous text therapy, or a recommendation to seek higher-acuity care outside the platform. The model was trained in close collaboration with our clinical advisory board, and we have a human review loop that audits a sample of routing decisions every week. On the employer side, we added an AI-generated commentary layer to the analytics dashboard — natural language summaries that translate the population data into plain language recommendations for HR teams who do not have a data analyst on staff.

I spend a lot of my time now on the enterprise sales motion, which we are still figuring out. Mid-market employers with two hundred to two thousand employees are the sweet spot, and the sales cycle is longer than I expected when we started. I also sit on the advisory board of two other digital health companies and try to get to at least two benefits-and-wellbeing industry conferences a year.

The thing I keep coming back to is trust. Both the employee using the app and the employer buying the platform have to believe the incentives are aligned. Getting that architecture right — technically, legally, and in terms of how we talk about it — is the work I find most interesting.

Mindbridge Health is a hybrid B2C/B2B mental health platform serving individual users and their employers through two integrated product surfaces. The consumer app provides anonymous access to mood tracking, CBT-based self-guided programs, and on-demand licensed therapist support. The employer-facing SaaS layer delivers anonymized, population-level workforce wellbeing analytics and AI-generated insight summaries to HR and benefits teams. Founded by a benefits strategy and organizational psychology professional, Mindbridge has built its clinical and privacy architecture around a strict separation of individual and aggregate data. The company raised a Series A and has since deployed AI-driven triage and routing on the consumer side, alongside a natural language analytics layer for enterprise clients. Its primary market is mid-market employers in North America across professional services, technology, and healthcare verticals.

Gold

Expected action

propose

Fit score

0.62

Industry

Consumer / B2B SaaS hybrid

Segment

Mental health / workforce wellbeing

Seniority

Founder

Company size

—

Language

en

Notes

Very long bio. Hybrid B2C/B2B with explicit non-tech founder — same shape as Exemplar 2 and #16. Routes to propose.

Predictions