Development Stage · TRL 3 → TRL 4
From engineering proof-of-concept to integrated clinical edge prototype. Provenant AI Labs is developing privacy-preserving, human-governed AI infrastructure designed to operate locally within healthcare environments. The current Medi AI+ programme is progressing toward an integrated laboratory-validated edge prototype.
Flagship Development Programme
Medi AI+ is Provenant AI Labs' flagship R&D programme for locally operated, human-governed clinical AI infrastructure. It is currently in pre-clinical engineering and controlled laboratory development, progressing from TRL 3 toward TRL 4.
Development stage: TRL 3 → TRL 4
Local speech and governed knowledge workflows
Multimodal clinical context under development
Deterministic, human-supervised safety model
Development status
From engineering proof-of-concept to integrated laboratory validation
Medi AI+ programme · 2026
Medi AI+ is progressing from an engineering proof-of-concept toward an integrated laboratory-validated edge prototype. The foundation includes simulation-based multimodal workflows, local speech processing, governed knowledge retrieval, human review controls, deterministic safety logic, auditability, and edge-oriented software packaging.
The next proof point
Integrate the software foundation with representative edge hardware and medical peripherals.
The software and governance baseline is in place.
The current round converts the baseline into an integrated edge prototype.
Subsequent work is subject to evidence, governance and partner readiness.
Engineering development status does not constitute clinical validation, regulatory approval or production hospital deployment readiness.
Patent & Publications
Provenant's clinical appliance roadmap is informed by intellectual-property work, public technical research, and a safety-first architecture designed for supervised local validation.
Patent
Publicly listed patent application covering the clinical edge appliance direction and localized AI deployment concepts.
Application No. 202541127477
Public Research
Public technical material describes a deterministic, edge-first, human-supervised architecture for safety-constrained environments.
Visit research pageWhitepaper
Download a concise technical brief covering deployment model, evaluation metrics, governance, and pilot-readiness boundaries.
Download whitepaperEngineered as deployable operational infrastructure for organizations that cannot rely on constant cloud connectivity or external data transit.
Processes sensitive workloads inside the client's physical perimeter, reducing external data movement and cloud exposure.
Keeps AI operations available during network disruption, cloud downtime, or constrained connectivity windows.
Versioned interfaces are being developed for healthcare interoperability, device inputs, and controlled institutional integration.
Shifts workloads from variable cloud billing toward efficient edge nodes, licensing, deployment services, and support.
Market Gap
Privacy-sensitive environments need reliable AI close to the point of operation. Provenant focuses on local execution, offline continuity, and deployment models that stay aligned with enterprise governance.
Privacy and sovereignty risks from moving sensitive data to public cloud systems.
Mission-critical dependency on internet connectivity, latency, and cloud uptime.
Variable remote compute, data pipeline, and cloud scaling fees.
Limited fit for hardware-constrained and distributed operational settings.
Localized execution within the client's site perimeter.
Offline-capable continuity for uninterrupted local operations.
Predictable edge infrastructure economics and support contracts.
Integration-oriented architecture for future local edge nodes and institutional systems.
Technical Foundation
The product strategy is grounded in local inference, controlled data movement, deployment governance, and pilot validation instead of generic cloud-first AI delivery.
Protecting core localized AI infrastructure and deployment concepts.
Designed for on-site execution where sensitive data should remain inside the facility perimeter.
Built around data minimization, auditability, and human-supervised workflows.
Informed by applied research, constrained-device engineering, and clinical workflow realities.
Structured for stakeholder review, site readiness, validation, and support planning.
Performance Metrics
Each pilot is evaluated against operational, safety, privacy, and support metrics. Final benchmarks are confirmed only within approved deployment scopes and site-specific test plans.
Local latency
Tracked per workflow
Measures response time for on-device inference and structured workflow actions.
Connectivity resilience
Offline-capable
Evaluates continuity during constrained or interrupted network conditions.
Data locality
Site-controlled
Assesses whether sensitive operational data remains within approved local boundaries.
Auditability
Human-reviewed
Tracks traceability, review logs, and supervised use in controlled pilot settings.
Clinical Deployment
Medi AI appliances are positioned for evaluation in healthcare settings where privacy, reliability, human oversight, and institutional approval are essential. They are not presented as autonomous clinical decision-makers or substitutes for licensed professional judgement.
Pilot scopes define supervision, escalation, user roles, and acceptance criteria before deployment.
Device setup, peripherals, physical placement, power, and workflow fit are reviewed with site teams.
Data flows, retention, access controls, and local processing boundaries are evaluated with stakeholders.
Performance, safety, human-review, and operational metrics are agreed before field evaluation.
Pilot Deployment Program
The pilot program is designed for hospitals, research institutions, and operational sites evaluating local AI infrastructure under defined governance, validation, and deployment requirements.
Review facility readiness, stakeholder requirements, local data handling, and network constraints.
Coordinate device setup, clinical or operational workflows, peripherals, and support expectations.
Evaluate suitability, workflow impact, human oversight, and readiness for next-stage adoption.
Engineering Maturity Roadmap
The milestones below are target development stages, not guaranteed outcomes. Progress remains subject to engineering evidence, institutional collaboration, validation, and applicable regulatory requirements.
2026
Edge hardware integration, model adaptation, representative peripherals, and laboratory verification.
Target stage
TRL 3 → TRL 4
2027
Clinical/research partner collaboration, workflow testing, interoperability validation, and regulatory pathway assessment.
Target stage
TRL 4 → TRL 5/6
Following Validation
Qualified institutional pilot, regulatory and certification work as applicable, and manufacturing preparation.
Condition
Subject to validation and requirements
Commercial Stage
Commercial scale only after validation and regulatory requirements are appropriately addressed.
Condition
Not guaranteed; subject to readiness
Document Library
Public-facing documents become available immediately after a successful enquiry submission through a short-lived download link.
Public request
Singapore and India compliance statements, regulatory boundaries, ISO/BIS roadmap, and public disclaimers.
Public request
PDPA and DPDP-facing privacy summary for enquiries, retention, rights handling, and contact routing.
Public request
ISO/IEC, ISO medical-device, telehealth, laboratory, and India BIS/ISI readiness roadmap.
Next Phase
Provenant is seeking prototype-development partners, institutional collaborators, technical partners, clinical/research validation collaborators, investors, and grant or innovation programme partnerships.
Founding Leadership
Provenant AI Labs is led by a founding team combining research credibility, edge infrastructure engineering, and enterprise deployment execution.
CEO & Co-Founder
Leads executive strategy, healthcare innovation alignment, research ecosystem partnerships, and long-term deep-tech commercialization from Singapore.
View founder profileDirector, CTO & Co-Founder
EntrePass granted by Singapore MOM
Leads edge-AI architecture, product engineering, technical IP and the Medi AI+ prototype programme from Singapore.
View founder profileCOO & Co-Founder
Leads enterprise deployment execution, infrastructure coordination, operational readiness, and site support planning.
View founder profile