AI for speech pathologists, designed with speech pathologists.
Multi-domain screening, voice and fluency analysis, and evidence-linked clinical observations — built in collaboration with leading Australian SLP researchers.
What it does for speech pathologists.
Author treatment plans, parent and carer handouts, and home practice programmes from a brief case description. The platform suggests evidence-based starting plans aligned with SPA clinical guidelines; you approve, edit, and personalise.
Document sessions in the format you already use — DAP, SOAP, BIRP, GIRP, or your own template. Every observation is linked to the moment in the recording, with intervention tracking and themes tagged automatically.
Multi-domain screening across articulation, language, fluency, voice, and pragmatics. Each domain is scored with explicit confidence bounds. When the AI cannot assess something from the recording, it says so and explains why.
AI-led pronunciation and articulation practice for clients between sessions. Progress is tracked back to the clinician and feeds future session planning.
Built with the speech pathology research community.
Author of the leading Australian framework for simulation-based learning in speech-language pathology. Co-founder and clinical research lead.
Active research partnership supporting the Vietnamese postgraduate SLP programme established by Sally Hewat.
Active conversations with academic and clinical SLP partners across Australia. Get in touch if you'd like to be involved.
Aligned with the standards SLPs already use.
Speech Pathology Australia clinical guidelines, including the SPA Clinical Guideline for Stuttering Management.
GRBAS and CAPE-V auditory-perceptual evaluation frameworks for voice assessment.
Standard fluency measures including %SS (percent syllables stuttered) and disfluency typology.
Evidence-linked observations: every score, label, or flag traces back to a timestamped moment in the source audio.
What it looks like in practice.
All five clinical domains in one view, with confidence bands and gated outputs where the AI is uncertain.
Click any observation to hear the source moment. Verify before you trust.
DAP / SOAP / BIRP / GIRP — generated, edited, exported.
For students and clinical educators.
The platform supports simulation-based learning in line with Hewat et al.'s published framework. Students see explicit gated outputs — what AI cannot assess from a sample — which is itself a learning objective: sample adequacy, observation reliability, clinical reasoning under uncertainty.
Ready to see it for SLP?
We work with a small number of clinical partners at a time. Get in touch to scope a demo or pilot for your service.