LIET turns doctor-patient conversations into structured clinical notes, reviewable clinical findings, and longitudinal patient memory. Less time on paperwork. More time with the patient. The doctor is always in control.
Multilingual speech capture · Clinical intelligence layer · Hospital-grade infrastructure · Data hosted in India
10 Indian languages supported
The doctor's workflow doesn't change. Speak naturally. The system structures everything.
Any Indian language
Consultation happens naturally. Real-time transcription captures every symptom, pertinent negative, examination finding, and medication — organized as it’s spoken.
Extraction, structuring, and flagging
Positive history separated from negative history. Exam findings extracted. Brand names of medications captured accurately. Red flags identified. Investigation options listed. All in seconds.
Edit, prescribe, send
Editable clinical document. Prescription editor. Physician-requested reference tools — clinical evidence, referral templates, investigation checklists. WhatsApp delivery to patient. Everything auditable. The physician confirms every output.
45-year-old woman. 8-minute consultation in a regional Indian language. Here is what the AI found.
See the full walkthrough →Connected frothy urine + new-onset hypertension (148/96) + bilateral edema into a pattern suggestive of renal involvement. This pattern may not be obvious in a routine pain-first consultation.
Red flags identified
Clinical intelligence
Counted SLICC 2012 diagnostic criteria unprompted. Investigations prioritized: renal first (UPCR, microscopy, creatinine), then immunological (ANA, dsDNA, complement). Specified ANA methodology (IIF, HEp-2 substrate).
4 speech-to-text accuracy errors identified (drug name variants, family history formatting). Clinical analysis was accurate. Reviewed against physician interpretation.
Indian primary care generates 2.5 billion consultations per year. Almost none of it is searchable or analyzable. The doctor writes a paper note. The patient leaves with a handwritten prescription. The clinical pattern — the connection across visits, the red flag buried in a multi-language conversation — disappears.
Doctors need their notes done faster — that's the immediate value. But every note generated and reviewed by a physician creates something that doesn't exist elsewhere in Indian healthcare: searchable, longitudinal clinical data in the doctor's own corrected words. The documentation tool is useful on day one. The intelligence layer it builds becomes valuable over time.
Each consultation adds to a growing knowledge substrate — physician-reviewed, multi-language clinical records with corrections, confirmations, and patterns across visits. The longer the system runs in a hospital, the harder it becomes to replicate what it has learned.
Every consultation generates searchable, analyzable clinical data — from any Indian language.
Chief complaint, HPI, examination, diagnosis, investigations, advice — all organized fields
Flags must-not-miss conditions — renal, cardiac, and neurological red flags identified
Separates pertinent positive symptoms from pertinent negatives — important for complete and defensible clinical documentation
Tracks vitals, medication response, and symptom patterns across visits and across departments. Follow-up visit context built in.
Native Indian-language recognition with real-time translation. Handles mixed-language doctor-patient conversations, regional drug brand pronunciations, and medical terminology under real outpatient conditions.
Structured note generation, clinical pattern identification, and investigation support — built for Indian clinical practice. Evidence-based protocols. Indian drug brand awareness. Endemic disease context.
Row-level security. Immutable audit logs. Patient consent and data governance. Role-based access. FHIR R4 export. Data hosted in India. Every interaction logged and auditable.
Every consultation reviewed by a physician adds to a connected clinical knowledge base that doesn't exist elsewhere.
Multi-language clinical documentation with clinical intelligence. Hospitals buy the tool. Doctors use it 20 times a day.
Over time, de-identified consultation data supports research workflows, synthetic cohort generation, and clinical trial design. The clinic generates the data. The data funds the research. The research funds more clinics.
A small team building the clinical intelligence layer for Indian healthcare.
CHENNAI, INDIA
Full-stack engineer leading the clinical platform build. Hospital-grade architecture, encounter flow, and infrastructure reliability.
MYSORE, INDIA
Trained internal medicine physician. Leads clinical validations, product accuracy, and hospital partnerships.
LUXEMBOURG
Vehicle dynamics engineer. Background in complex systems modeling applied to healthcare. Leads product strategy, regulatory affairs.