Back to the discharge queue
Arjun Mehta, claim C-105
What this demo claim shows: Rs 8,000 a day against a Rs 5,000 cap (C3.1). The agent computes the proportionate deduction and tells the desk before discharge.
Not run yet
1. Claim file
Synthetic patient and documentsInsurer and policy
Sahyog General Insurance (fictional), Arogya Shield Plus, sum insured Rs 5,00,000
Policy since
2020-05-20
Room
Deluxe single room, Rs 8,000 a day for 3 days
Desk note
Patient upgraded to deluxe room on request
Documents in the file
2. Bill lines and evidence
Not checked yet| Line | Description and code | Amount | Evidence |
|---|---|---|---|
| L1 | Deluxe single room, 3 days ROOM-DLX | Rs 24,000 | Not checked |
| L2 | Physician visits x3 PRF-VISIT | Rs 2,400 | Not checked |
| L3 | Nursing charges NUR-CHG | Rs 1,500 | Not checked |
| L4 | Stool routine, CBC, electrolytes LAB-STOOL-CBC | Rs 2,800 | Not checked |
| L5 | Pharmacy (itemised) PHA-IP-305 | Rs 7,900 | Not checked |
3. Policy checks
Deterministic rules engine, not the language modelThe rules run when the agent calls the policy tool. Each result cites the clause it comes from.
4. Agent trace
0 tool callsLLM planner, gpt-4.1-miniThis deployment has a model configured. The language model will choose each tool call; you will see its reasoning notes.
Press Run the agent. Every card is a real tool call with its input and output; nothing is pre-recorded.
7. Messages with the insurer
Hospital and insurer go back and forth here until the claim is approved or rejectedLoading the conversation.