Slingshot AI operates as a mental health research lab building a foundation model for psychology and accompanying therapy chatbot. The technical stack spans model development (PyTorch, TensorFlow, JAX) and production infrastructure (GCP, Kubernetes, Cloud Run, gRPC) with client applications in Flutter and Next.js/React. The team combines machine learning engineering, product development, and clinical research expertise, working with therapists and clinicians to align model behavior with therapeutic practices.
The core technical challenge is training a domain-specific foundation model that supports user agency in mental health contexts - framing the product as a tool that helps users recognize their own capacity for change rather than an answer-dispensing assistant. This architectural constraint requires careful training objective design and evaluation frameworks that measure therapeutic alignment, not just task completion. The system operates at global scale through partnerships with mental health organizations, though specific throughput or latency metrics are not disclosed.
Development follows rapid iteration cycles with emphasis on shipping velocity. The engineering stack reflects production priorities: Rust for performance-critical paths, typed languages (TypeScript, Kotlin) for application logic, and container orchestration for deployment. The team works within the constraint of adapting general-purpose ML infrastructure to specialized clinical requirements while maintaining operational reliability for users seeking mental health support.