Frequently asked questions

Micromeet builds healthcare AI infrastructure for continuous care. MCU CoPilot, AI Scribe, AI Front Desk and Care Loop support focused workflows; Micromeet Care Workspace brings their approved context, owners and next steps into one view.

Micromeet's AI work operates inside an institution's clinical and operational governance. The institution defines which outputs require review, who may approve them and what audit evidence is recorded for each enabled workflow. Micromeet prepares supported drafts and tasks within that configured scope; authorized people and source systems remain in control.

Claim readiness starts with complete, clinician-reviewed source documentation. Authorized institutional clinicians, coders and casemix teams own coding and submission; payer teams and systems own review and adjudication. Requirements remain institution- and jurisdiction-specific.

It means AI prepares drafts such as reports, clinical notes, follow-up messages and coding suggestions. A qualified clinician reviews, edits and approves clinical content before release, while assigned staff own operational messages and requests. The clinician remains responsible for medical judgment and approval; legal responsibilities follow applicable law, professional rules, contracts and institution policy.

Micromeet is headquartered in Singapore, with teams in Hong Kong, Jakarta and Shanghai.

Indonesia is Micromeet's primary market, with Hong Kong and Singapore also within its regional focus. Language, coding, privacy and data-location requirements are assessed for each institution and jurisdiction before a workflow is implemented.

MCU CoPilot prepares medical check-up (MCU) report drafts from supported structured inputs such as laboratory results, vitals, physical-examination findings and specialist conclusions. A doctor reviews, edits and signs the clinical report. A file-based browser workflow can be configured after its data mapping, access and review controls are verified; an HIS, LIS or EMR API is used only where the institution has verified and approved that supported integration path.

AI Scribe, also called Voice-to-EMR or V2N, turns supported consultation audio into structured note, referral-letter and visit-summary drafts. It supports 50+ languages, including Indonesian, English, Cantonese, Mandarin, Malay, Thai and Vietnamese. A clinician reviews every draft, and the institution verifies any export or EMR writeback path before use.

AI Front Desk prepares first-response drafts, intake details and booking requests for staff-owned queues across supported channels and languages. Staff send the response, confirm availability and the booking, and handle exceptions. The exact channels, service hours, knowledge sources and approval steps are configured with each institution.

Care Loop prepares report-explanation drafts, follow-up tasks, recheck reminders and patient requests in human-managed institution queues. Authorized clinicians and staff decide what is sent and what action follows. The context, channels, review steps and audit evidence are configured for each enabled workflow.

Micromeet Care Workspace is a modular workspace for healthcare teams. It connects enabled appointment, MCU, clinical reporting and follow-up work, carrying reviewed reports, consultation notes and follow-up history to authorized teams across visits. It shows context, owners, status and next steps without replacing the institution's HIS/EMR.

Micromeet publishes the Indonesia MCU Healthcare AI Agent Readiness Benchmark, including the evaluation method, machine-side results and stated limits. Clinical, quality and procurement teams can inspect the evidence at micromeet.ai/benchmark/index.html; the benchmark does not establish clinical correctness or production readiness.

It is a method-first benchmark that tests 12 foundation models under one fixed MCU workflow on 30 synthetic-regenerated Indonesian medical check-up cases. It evaluates machine-side structure, completeness, stability and traceability. A machine-side pass is not a clinical-correctness or production-readiness verdict.

Yes — that is the point. The benchmark includes an eight-point readiness checklist (structured-output proof, repeat stability, evidence traceability, safety boundaries, localization fit, review burden, independent review, change control) that an institution can apply to any AI documentation vendor, including Micromeet. If a vendor can only show a polished demo, the checklist tells you what evidence to ask for before committing to a pilot.

Patient-data controls are deployment-specific. Micromeet's ISO/IEC 27001:2022 certificate covers AI application platform development, not every product or customer system. Before production patient data is connected, Micromeet's operating gate requires institution-approved contractual processing terms, commonly a DPA or equivalent, covering purpose, access, storage, subprocessors, retention, deletion and permitted reuse. Encryption, data location and any use for improvement or training are verified and documented for that implementation rather than inferred from a general website promise.

Applicable privacy obligations depend on the jurisdiction, institutional roles, data and deployment. During scoping, the institution and its advisers map the relevant requirements, which may include Indonesia's UU PDP, Singapore's PDPA, Hong Kong's PDPO or applicable HIPAA requirements. The controls, contractual terms and evidence are verified for the implementation; naming a law or standard does not establish universal compliance.

No. Micromeet's agents are documentation and workflow systems, not Clinical Decision Support Systems (CDSS) or diagnostic engines. They do not interpret raw clinical media such as ECG waveforms or radiological images; specialist findings are used as reported by the responsible clinician. Clinical outputs enter the institution's configured clinician-review and authorization step before release, while operational items remain owned by assigned staff.

Storage location, access, backup, retention and deletion arrangements are confirmed for each implementation and documented in the applicable institution-approved terms. No single storage location or retention schedule applies to every deployment.

Implementation time depends on the workflow and institution. A verified file-based MCU workflow may start without an HIS/LIS integration after data mapping, access, storage, roles and review controls are agreed. API or connector paths follow the institution's IT change process, and any later migration is scoped and verified rather than assumed.

Micromeet evaluates supported API, structured-connector and export paths with each institution. Access, data mapping, audit coverage and any writeback are verified and configured for the target system before use; no integration or writeback is implied until that path is approved. Where writeback is not verified, a structured export can support a staff-owned handoff while the institution's system remains the source of record.

Micromeet supports 50+ languages across its voice and patient-facing products, including Indonesian, English, Cantonese, Mandarin, Malay, Thai, Vietnamese, Filipino, Tamil, Hindi, Japanese and Korean. Clinical output follows the institution's working language — for example, Bahasa Indonesia MCU reports with local clinical terminology preserved.

Start with a conversation: email enquiry@micromeet.ai or use the contact form at micromeet.ai, and tell us your institution type and the workflow you want to improve first. Pricing depends on deployment mode, volume, integration and governance scope, so those requirements are confirmed before an implementation or evaluation plan is proposed.