WhatsApp ingestion
Turn conversational updates into structured local records without forcing a new workflow.
The platform is designed for inputs that do not arrive in tidy hospital-system formats. It extracts, structures, and stores data locally, then makes it immediately useful for care.
Extracted from WhatsApp:
Added to timeline and flagged for follow-up.
Turn conversational updates into structured local records without forcing a new workflow.
Convert scans and photos into usable data for charts, summaries, and follow-up.
Speech-to-note workflows that reduce typing overhead in busy settings.
The app is meant to reduce friction, not create another system people must remember to update.
Parse clinically relevant updates from messaging threads and keep them attached to the right patient.
Turn referral notes, prescriptions, and handwritten summaries into searchable records.
Extract useful metadata from imaging reports and scanned lab results without leaving the device.
Capture spoken notes in the moment, then structure them for review later.
New Loka is more than capture. It organizes incoming data into a consistent local structure so it can be retrieved quickly during care and later reused for research or reporting.
Bring in the message, image, scan, or spoken note with minimal manual work.
Extract the useful fields, preserve context, and keep the original source attached.
Keep records separated by patient, practitioner, clinic, or department where needed.
Show timelines, summaries, and alerts at the moment of care.
Fast filtering by date, source, symptom, lab, or note type.
Mark urgent items, pending responses, and next steps.
One-screen view for quick review before a consult.
Move a record into another workflow when a user needs to share it.
Use structured clinical templates without losing flexibility.
Support multilingual environments and mixed-language workflows.
Minimal steps for entering and reviewing information means less training overhead for teams with limited time.
Structured output is preserved in a way that supports later anonymization and aggregation.
The platform should behave consistently across phones, tablets, and larger workstation screens.