LLM Cost Infrastructure for Healthcare

The problem

You're paying for tokens your patients never sent.

Every LLM API call in a healthcare chatbot carries far more than the patient's message.

01 / Infrastructure overhead

Every patient interaction carries 1,600+ tokens before your bot reads a single symptom.

02 / Where the bill comes from

Appointment history, insurance docs, RAG context — 97% of your token bill is overhead. The patient message is noise.

03 / At production scale

At 500,000 monthly interactions, that's ₹1,96,384 in input costs before compression.

How it works

Compress before you call. Pay for what matters.

Indic Engine sits between your bot and the LLM. Three steps. No clinical system change.

Patient sends WhatsApp message to your healthcare bot

Raw input — patient message in Hindi, Marathi, Tamil, or any of 24 languages, full appointment history, insurance context, clinical RAG — arrives at the edge.

Appointment history, insurance context, and patient message compressed 83.5% before reaching your LLM

Each component is compressed independently: medical records and insurance chunks to structured clinical JSON, prompt rules to a compact key-value set, Indic patient text to intent data. All at the edge, sub-100ms.

1,635 tokens → 270 tokens

LLM receives structured clinical intent. You pay for 270 tokens, not 1,635.

The model receives dense, structured context — no formatting noise, no redundant prose. Equivalent clinical signal. Fraction of the cost. Your bot logic and responses are unchanged.

Real numbers

The maths on your current stack.

Based on a production healthcare chatbot: 1,200 RAG tokens (patient history + insurance) + 400 system prompt tokens + 35-token Hindi message. 500,000 interactions/month. GPT-4o at $2.50/M input tokens.

	Raw (today)	After Indic Engine
Tokens per interaction	1,635	270
Monthly cost @ 500K interactions	₹1,96,384	₹32,490
Monthly saving		₹1,63,894
Annual saving		₹19,66,728
Compression rate	—	83.5%

What gets compressed

Three compression passes. One API call.

Each component of your LLM call is compressed with a method tuned for its structure.

85%

reduction · Patient History & RAG

Patient History & RAG

Medical history, prescription records, insurance policy chunks injected per call — compressed to structured clinical JSON before LLM sees them. Only relevant facts survive.

80%

reduction · System Prompt

System Prompt

Your clinical guidelines, triage rules, escalation protocols — compressed to 80 tokens. Same behaviour. Every interaction. Compress once, reuse across every session.

70%

reduction · Indic Patient Messages

Indic Patient Messages

Hindi, Marathi, Bangla, Tamil, Telugu — patients describe symptoms in their language. Compressed to structured intent JSON. 24 languages supported.

Use cases

What patients say. What your LLM receives.

Real patient messages across three common healthcare workflows, compressed to structured clinical intent.

Appointment Booking

Patient says

"Doctor sahib se kal subah milna hai, pet dard hai"

Bot receives

{"i":"book_appointment",
"dept":"gastro",
"date":"tomorrow",
"sym":"abdominal_pain",
"time_pref":"morning"}

Insurance & Claims

Patient says

"insurance claim ka kya hua, policy number HC4521"

Bot receives

{"i":"claim_status",
"ins":"policy_hc4521",
"type":"health",
"status":"unknown"}

Pharmacy & Prescription

Patient says

"paracetamol 500mg refill chahiye, last prescription tha Dr Sharma se"

Bot receives

{"i":"prescription_refill",
"med":"paracetamol",
"dose":"500mg",
"doc":"sharma",
"qty":"unknown"}

Integration

Two lines. No clinical system change.

One API call before your LLM. No clinical system change. No bot logic change. Your API keys stay with you. 15-minute integration.
You add one API call before your LLM. Everything downstream is identical.

# ── Step 1: POST raw input to Indic Engine ───────────────
POST https://indic-engine.com/v1/chat/completions
Authorization: Bearer ie_live_xxxxxxxxxxxx
{
  "input":    "patient message in Hindi/Marathi/Tamil",
  "vertical": "healthcare"
}

← { "data": "{\"i\":\"book_appointment\",\"dept\":\"gastro\",\"date\":\"tomorrow\",\"sym\":\"abdominal_pain\"}" }
   { "savings": "83%" , "tokens": { "in": 1635, "out": 270 } }

# ── Step 2: Pass compressed data to your LLM ─────────────
POST https://api.openai.com/v1/chat/completions
{
  "messages": [{ "role": "user", "content": compressed }]
}
# LLM billed for 270 tokens. Not 1,635.

Compliance & Privacy

DPDP Act 2023 Compliant — By Architecture

Indic Engine is a data processor under DPDP Act 2023. Clients are data fiduciaries responsible for patient consent and their own compliance obligations.

No conversation history retained. Compressed results cached per-client for 30 days to improve speed — never shared across clients, never used for any purpose beyond serving your own future requests.

Data minimisation. Only token counts and savings metrics logged for billing. Never message content.

Purpose limitation. Messages processed solely for semantic compression. No secondary use. No training on client data. Ever.

Security. TLS 1.3 in transit via Cloudflare. All metadata encrypted at rest via Supabase.

Breach notification. In the event of a security incident affecting API keys or billing metadata, clients notified within 72 hours. Patient data breach via Indic Engine is not possible — nothing is stored.

Right to erasure. Automatically satisfied. Nothing stored means nothing to erase.

Cloudflare infrastructure — Mumbai region available for Indian data residency.

Free savings audit

See the exact saving on your patient interaction traffic.

Send us 50 anonymised patient messages. We return token counts, cost comparison, and monthly saving in 24 hours. No PHI required — anonymise before sending.

Request Free Audit →

Or write directly to [email protected]