LLM Cost Infrastructure for BFSI

The problem

You're paying for tokens your users never sent.

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

01 / Infrastructure overhead

Every API call to GPT-4o carries 1,600+ tokens before your user says a word.

02 / Where the bill comes from

RAG context + system prompt account for 97% of your token bill. The customer message is noise.

03 / At production scale

At 500,000 calls/month, that's ₹1,96,384 in input costs — before a single rupee of output.

How it works

Compress before you call. Pay for what matters.

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

Your bot sends the message to Indic Engine

Raw input — customer message in Hindi/Urdu/Marathi, full RAG context, complete system prompt — arrives at the edge.

RAG context, system prompt, and Indic message compressed 83.5% before reaching GPT-4o

Each component is compressed independently: policy documents to semantic JSON, prompt rules to a compact key-value set, Indic text to structured English intent data. All at the edge, sub-100ms.

1,635 tokens → 270 tokens

GPT-4o receives clean structured data. You pay for 270 tokens, not 1,635.

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

Real numbers

The maths on your current stack.

Based on a production BFSI chatbot: 1,200 RAG tokens + 400 system prompt tokens + 35-token Hindi message. 500,000 calls/month. GPT-4o at $2.50/M input tokens.

Metric	Raw (today)	After Indic Engine
Tokens per call	1,635	270
Monthly input cost @ 500K calls	₹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 · RAG Context

Policy & Product RAG

Policy documents, loan product details, and compliance rules injected per call — compressed to semantic JSON before the LLM sees them. Only relevant facts survive.

80%

reduction · System Prompt

Instruction Set

Your 400-token instruction set compressed to 80 tokens. Same behaviour, same guardrails, every call. Compress once, reuse across every session.

70%

reduction · Indic Messages

Customer Messages

Hindi, Marathi, Bangla, Arabic, Urdu — compressed to dense English JSON with intent, amount, account reference, and KYC stage extracted. 24 languages supported.

Integration

Two lines. No bot logic change.

Drop-in middleware. No model change. No bot logic change. 15-minute integration.
You add one API call before GPT-4o. 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":    "customer message in Hindi/Urdu/Marathi",
  "vertical": "bfsi"
}

← { "data": "{\"i\":\"emi_inquiry\",\"prod\":\"home_loan\",\"amt\":25000,\"acc\":\"4521\"}" }
   { "savings": "83%" , "tokens": { "in": 1635, "out": 270 } }

# ── Step 2: Pass compressed data to GPT-4o ───────────────
POST https://api.openai.com/v1/chat/completions
{
  "messages": [{ "role": "user", "content": compressed }]
}
# GPT-4o billed for 270 tokens. Not 1,635.

Compliance & Data Handling

No content stored. No data at rest.

Each request is processed and discarded. A per-client semantic cache accelerates repeat query patterns, isolated to your account only, with 30-day automatic expiry.

Edge compression only. Processing happens at the Cloudflare edge node closest to your infrastructure.

Cloudflare infrastructure. SOC 2 Type II certified. Data never leaves the request lifecycle.

No PII retention. Only aggregated token counts and savings ratios are logged — never message content.

Free savings audit

See the exact saving on your traffic.

Send us 50 anonymised BFSI messages. We return token counts, cost comparison, and monthly saving in rupees — within 24 hours. No commitment.

Request Free Audit →

Or write directly to [email protected]