Hindi, Marathi, Bangla, and Arabic inflate your LLM bill by 3–4× compared to English. Indic Engine removes that bloat before it reaches your model. No code changes. No model swap.
Supported languages
LLMs were trained on English. A typical Hindi or Marathi conversation burns 3–4× more tokens for the same meaning. It hits your margins silently, every day, at scale.
per typical Marathi conversation · 10 turns
Your system prompt, conversation history, RAG context, and the user's Indic message — all forwarded raw. You pay for every redundant character, every turn, every session.
same conversation · same LLM · far fewer tokens
The same meaning, forwarded efficiently. Your LLM receives exactly what it needs — nothing more. Same model, same output quality, dramatically lower API spend.
We sit between your app and your LLM. What happens inside stays inside. What changes is your invoice.
"One configuration change in your existing SDK. Your model, your key, your provider — unchanged."
Every vertical has its own token patterns and RAG footprint. We tune for each one separately.
Loan queries, insurance bots, KYC workflows, and RAG on compliance documents in regional languages. Heaviest token footprints. Highest savings.
Symptom queries, appointment scheduling, prescription lookup in Hindi and regional languages. DPDP-compliant. Zero PII retained.
Flight and hotel queries, booking bots, itinerary assistants in Hinglish, Tamil, Telugu, and Arabic. High volume, tight margins.
Lead qualification bots in Marathi, Hindi, and Bangla. Budget, configuration, and location intent compressed without losing context.
Driver support, shipment tracking, delivery bots in regional languages. High volume, short sessions, thin unit economics.
Policy Q&A bots, interview scheduling, offer queries in multiple languages. RAG on internal docs is expensive — we cut it.
Ways to reduce Indic LLM costs do exist. Most trade reasoning quality, add steps, or require a full migration.
| Capability | Native Indic Model | Translate → LLM | Indic Engine |
| Keep existing LLM unchanged | ✗ | ~ | ✓ |
| No model migration or retraining | ✗ | ✓ | ✓ |
| Reduces actual LLM input token spend | ~ | ✗ | ✓ |
| Single-step integration, no refactoring | ✗ | ✗ | ✓ |
| Negligible latency overhead | ~ | ✗ | ✓ |
| Provider-portable (switch LLMs freely) | ✗ | ~ | ✓ |
Anyone can describe this in a weekend. Building it to work reliably across 24 languages, 6 verticals, and production throughput is a different challenge entirely.
We validate compression quality across all 24 languages continuously. False compression — stripping meaning instead of bloat — is a failure mode we actively guard against.
BFSI messages carry different semantic weight than real estate or healthcare queries. We tune separately for each vertical. Generic compression tools don't.
Works with any LLM backend. If your provider's pricing changes or export restrictions force a switch, your middleware stays in place. Already battle-tested.
No user message stored anywhere. DPDP Act 2023 compliant by architecture, not policy. Material for BFSI and healthcare clients who can't afford compliance gaps.
Repeated queries — common in vertical bots — served from cache in milliseconds with no LLM call at all. Per-client, isolated. Gets smarter every month.
If compression fails for any reason, your original message passes through untouched. Your bot never breaks. Tested across every edge case.
Rough estimate based on typical Indic message profiles. Exact numbers come from your 24-hour audit.
Start free. Upgrade when the savings make it obvious. Typical clients see ₹10,000–₹50,000 net savings per month after plan cost.
Enterprise plans with custom volume, dedicated infrastructure, and SLAs. Gulf pricing available.
No code changes. No commitment. We run your real traffic through the engine and reply with the exact ₹ figure for your volume, language, and vertical.
Your savings report will be in your inbox within 24 hours.