Psychology and therapist support
Psychology patient management system
Challenge: Therapists face fragmented notes, weak continuity between sessions, and a large admin burden that gets in the way of higher-quality care.
Built: An AI therapist support platform for psychology practices, combining session transcription, structured clinical notes, therapy timelines, journalling, mood capture, therapist dashboards, pre-session summaries, and between-session support workflows.
Why it matters: Shows how Metamorph-iT can work inside sensitive, higher-trust environments without falling into irresponsible replacement narratives. The strongest commercial buyer is an innovative clinic owner or psychology practice owner rather than an individual practitioner looking for a note tool.
Psychology AI and between-session care
ACT / CBT therapy companion
Challenge: Clients often struggle to apply therapy skills between sessions, and therapists rarely have enough visibility into what happens in the moments that matter most.
Built: A voice-guided between-session therapy support concept for ACT and CBT skill practice, emotional labelling, values-based action, AI journalling, and therapist-configured care continuity.
Why it matters: Useful proof of how AI can support adherence, reflection, and structured care continuity without crossing into unsafe clinical replacement. Voice was the standout interface, supported by app-based tracking, journalling, and therapist controls.
Fitness, coaching, and digital guidance
Personal training scaling system
Challenge: High-quality coaching is difficult to scale, and most digital fitness tools flatten instruction into static content or low-context reminders.
Built: A voice-led coaching system designed to deliver guided training, accountability, spoken instruction, in-ear workout support, and a more embedded digital coaching experience.
Why it matters: Demonstrates how AI can extend expert delivery models without simply turning a premium service into a chatbot. The strongest business case sits with gyms and personal trainers looking to scale their coaching method into an AI-supported membership tier or white-label layer.
Real estate
Real estate service and lead concepts
Challenge: Agencies need faster lead response, more scalable listing promotion, and better support workflows without making the experience feel low trust or generic.
Built: Voice-led enquiry handling, CRM capture, appointment booking, lead qualification, old-lead reactivation, workflow automation, and the RocketReel product for cinematic property video generation.
Why it matters: Shows both sides of the practice: advisory and concept work, plus a live product in market through RocketReel. This was an AI sales-operations layer for real estate rather than a simple FAQ chatbot.
Travel and place-based experiences
Walkabouts TravelMate
Challenge: Walking tours and location-based storytelling often rely on static formats that do not adapt to context, pace, or curiosity in real time.
Built: A location-aware spoken guide experience that turns movement, place, and narrative prompts into a more personal touring format.
Why it matters: Useful evidence of early thinking around embodied AI, voice interaction, and ambient service design.
Sales automation and business development
Outreach CRM and campaign engine
Challenge: Small teams know who they should sell to, but struggle to research prospects, personalise outreach, maintain CRM hygiene, and run disciplined follow-up at scale.
Built: An AI outbound CRM and prospecting engine for prospect identification, data enrichment, personalised messaging, lead scoring, sequencing, and repeatable campaign operations.
Why it matters: Shows how market intelligence, CRM structure, and AI-assisted outreach can combine into a scalable growth engine rather than a loose list of contacts. It started around real-estate outreach but was designed to generalise to broader targeted B2B sales.
Service operations
Voice ordering and high-throughput service
Challenge: High-volume service environments need faster handling, lower friction, and clearer structured input without overloading staff.
Built: A conversational ordering concept testing where voice interaction can improve throughput, reduce handling time, and simplify repetitive flows.
Why it matters: Shows how the lab explores practical, workflow-heavy opportunities rather than only consumer novelty.
Media training and crisis simulation
On The Record
Challenge: Leaders often do not get enough realistic practice before hostile interviews, crisis events, or high-pressure public scrutiny because media training is expensive and hard to scale.
Built: An AI-generated scenario and questioning simulation with voice-led interview pressure, transcript review, and coaching-style feedback for executives, founders, spokespeople, and communications teams.
Why it matters: Demonstrates a stronger simulation and training use of AI than a standard content or support workflow. Best positioned as executive communications AI, with media training and crisis simulation as the application.
Regulated and analysis-heavy environments
Regulatory review and knowledge-heavy workflows
Challenge: Knowledge-heavy review environments rely on fragmented documents, manual interpretation, and inconsistent handoffs between people and systems.
Built: A concept interface for evidence review, regulatory analysis, structured document intelligence, and AI-assisted knowledge work around documents and decision support.
Why it matters: Connects directly back to enterprise and regulated-sector credibility rather than separating innovation from advisory experience. The important framing is human-in-the-loop evidence review and mapping, not automated regulatory judgment.
Retail, supply visibility, and local economic signals
CheckShelves and distributed market monitoring
Challenge: Supply stress, empty shelves, and pricing disruption are often noticed too late because the signals are distributed, visual, and difficult to track consistently.
Built: A concept using shelf imagery, pricing patterns, location signals, and AI-assisted monitoring to detect supply stress and local disruption patterns.
Why it matters: Shows that the lab also explores observational AI products and signal-detection concepts beyond voice and language-heavy workflows.
