AML

Bridging Doctor-Patient Communication & Trust deficit to enable personalised healthcare for the average Indian

On a mission to On a mission to reduce doctor workload, increase patient time and trust, reduce healthcare cost, and give better treatment outcomes for clinics, nursing homes, small and medium hospitals, labs and individual doctors.

Recognized by the Government of India and Tamil Nadu
Startup IndiaDPIIT-recognized · Cert. DIPP280325
MSME UdyamReg. UDYAM-TN-02-0500939
Startup TNReg. STN99248
ABDM SandboxBridge ID SBXID_071320
Tech Partners
Microsoft for StartupsAzure startup credits
Amazon ActivateStartup programme
NVIDIA InceptionStartup programme
Recognitions
D-U-N-S Number581758835

What we provide

1 Integrated Agentic Health Ecosystem

Stage 4 · Outcomes

What changes for patients, doctors and the public

Care that continues between visits, with less paperwork and more evidence behind every decision.

  • Improved patient health outcomes
  • Tracking in between visits
  • Smoother workflows
  • More focus on clinical care
  • Less documentation and admin
  • More data for reasoning and prediction
  • Improved trust
  • Streamlined epidemiological research and public health policy
Stage 5 · A circular ecosystem

Every data point makes the care better

Each closed loop leaves a more complete record and a clearer view of what worked.

Data Decisions Outcomes Learning

How the ecosystem works

One unified data layer makes care a continuous process

Any data being ingested is cleaned, classified and stored. A team of people and Shishyan AI ensure it is complete, so each decision and answer is based on extensive data. Select a stage to see what happens there.

Photos & scans PDFs & reports Voice & calls WhatsApp & chat Devices & forms UNIVERSAL INPUT LAYER HUMAN COORDINATORS COREDATA SPINE ONEAGENT Doctors & teams Patients Public health

Illustrative diagram of the architecture.

Any source, as it already exists

Clinics and patients already hold the data: lab printouts, discharge PDFs, handwritten prescriptions, voice notes, WhatsApp messages, device readings, intake forms. Nothing has to be re-typed into a new system first.

Universal input layer: ingest, clean, store

Each file is classified by type, its fields are extracted, cleaned and sorted into investigations, medications, procedures, history and signs, then written to the patient's record with the original file kept alongside.

Core data spine: one longitudinal record

One continuously updated record per patient (vitals, diagnoses, medicines, allergies, labs and history) that every other part reads from. Continuous inflow keeps it current, so nobody works from last month's picture.

Human coordinators close the gaps

People talk to the patient, build trust, bridge the communication gap between clinic and home, and chase the missing data points the system flags. AI is the backbone behind them; it does not face the patient.

One agent orchestrates the work

A single agent runs workflows, tasks and schedules, watches for abnormalities and worsening, does the computation and runs simulations. It proposes and prepares; clinical decisions stay with the doctor.

Three kinds of beneficiary from the same data

Doctors and care teams get a complete, current picture. Patients get explained plans through their coordinator. Population-level, de-identified data supports policy, epidemiology and research.

Certified infrastructure — data & AI

Data & file storage Health records, documents & database
ISO 27001 ISO 27017 ISO 27018 ISO 27701 SOC 2 Type II SOC 3 HIPAA Eligible HITRUST CSF PCI DSS FedRAMP GDPR
AI analysis & processing AI model routing, inference & reasoning
ISO/IEC 42001 SOC 1 SOC 2 SOC 3 HIPAA Eligible HITRUST CSF FedRAMP GDPR Compatible Zero Data Retention

Storage infrastructure is HIPAA-eligible and ISO/IEC 27018 certified for cloud PII protection. AI inference runs on ISO/IEC 42001 certified infrastructure with zero-data-retention providers — your inputs are never stored or used for model training.

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