rempart (n.) | The Fortified Shield for Production AI

10x Cheaper AI Inference.
Uncompromised Quality.
Private AI.

Enterprise confidential inference engineered for high-volume repetitive tasks. Swap your OpenAI or Gemini endpoint to receive a free email report on your exact speed and cost optimization potential.

Simulate Savings

drop-in-inference.py

1 Line Change

# Keep your existing OpenAI / Gemini SDK — Just point base_url to rempa

from openai import OpenAI

client = OpenAI(
    api_key="REMPA_API_KEY",
    base_url="https://api.rempa.ai/v1" # ← 10x savings, full privacy & free audit
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Process customer order stream..."}]
)

THE 4-STEP AUDIT ENGINE

How It Works: Free Analysis Before Optimization.

Connect your existing endpoint without breaking production. Rempa observes your patterns, analyzes cost leaks, and delivers a full optimization blueprint directly to your inbox.

Endpoint Replacement

Update the base_url in your OpenAI or Gemini SDK to Rempa’s passive proxy.

✓ Non-breaking drop-in

Passive Observation

Rempa securely maps prompt repetition rates, token redundancy, and context window lengths in real-time.

✓ 0% latency impact

Deep AI Analysis

Our engine computes potential gains from KV-caching, prompt compression, pruning, and dynamic LoRA routing.

✓ Automated ROI audit

Free Analysis Delivery

Receive a full audit report in your email with exact cost comparisons and speedup metrics.

★ Sent to your email

Interactive ROI Calculator

See what 10x cheaper inference means for your bottom line.

Your Current Monthly OpenAI / Gemini Spend: $10,000 / mo

With rempa.ai (10x Optimization)

$1,000 / mo

Includes KV Cache prefix sharing, compression & confidential enclaves.

Projected Annual Savings

$108,000 / yr

Engine Architecture

How we achieve 10x cost reduction without losing 0.1% quality.

The "Rempart" Enclave

100% Confidential Hardware Compute

Your prompts never touch persistent storage. Executed inside zero-trust hardware enclaves (NVIDIA H100 Confidential Compute / AMD SEV), guaranteeing zero data retention.

Prefix Deduplication

Cross-Request KV Caching

Static prompts and RAG contexts are cached in memory across calls, eliminating up to 90% of redundant computation.

Zero Migration Friction

Compatible with all major AI SDKs. Change the URL and you are live.

import openai

client = openai.OpenAI(
    api_key="rempa_sk_live_...",
    base_url="https://api.rempa.ai/v1"
)

import google.generativeai as genai

genai.configure(
    api_key="REMPA_API_KEY",
    client_options={"api_endpoint": "https://gemini.rempa.ai"}
)

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.REMPA_API_KEY,
  baseURL: 'https://api.rempa.ai/v1',
});

10x Cheaper AI Inference. Uncompromised Quality. Private AI.