The core idea is MindMark, an API that analyzes voice recordings from telehealth sessions to catch early signs of schizophrenia before standard clinical interviews would. The pitch is that psychiatric diagnosis gets blocked by months or years of diagnostic lag—patients describe vague symptoms, clinicians wait for behavioral changes to become obvious, and by then intervention windows have closed. MindMark listens for micro-acoustic markers: pitch instability, tremors, pause patterns, and speech syntax drift that show up in brief audio clips. It runs in the background of EHR dashboards and telehealth platforms, flagging risk without replacing the clinician's judgment. The business model stacks three revenue streams: $0.15 per minute of processed audio, monthly SaaS subscriptions from $499 to $2,499 for telehealth and EHR platforms, and custom calibration fees for clinical researchers. The go-to-market relies on open-sourcing a Python privacy SDK to build developer trust, launching an interactive web sandbox where clinicians can test audio samples and see spectrograms, and targeting developer SEO around HIPAA-compliant audio integration. The claimed moat is a longitudinal baseline lock-in—instead of comparing each patient against population averages, MindMark builds an individual acoustic profile over time. False positives drop as the system learns each person's baseline, which makes it sticky for platforms that integrate it into routine workflows. The timing argument centers on convergence: recent research shows AI can detect early psychosis indicators from voice samples with solid accuracy, and the infrastructure exists now through telehealth platforms already recording audio daily. The builder's corner sketches a practical stack—FastAPI for the API layer, Librosa and PyTorch for acoustic feature extraction and spectrograms, a lightweight fine-tuned language model for syntax analysis, and PostgreSQL with pgvector for storing anonymized embeddings. Audio processing happens in ephemeral memory with zero retention to stay HIPAA-compliant. The existing competitors (Sonde Health, Canary Speech, Winterlight Labs, Ellipsis Health, Kintsugi Voice) already work in voice analytics, so this isn't entirely new territory.