AI Token Optimization & Context Window Management
Stop paying upstream providers to process bloated HTML, redundant JSON indentation, and repetitive logs. ContextMiner is a drop-in LLM Proxy engineered for deterministic LLM Cost Reduction (15% to 80%+) with under 5ms local CPU latency.
Inspect How the LLM Proxy Slims Your Context Window
Select an ingestion pipeline below to compare raw application payloads against the optimized context forwarded to OpenAI, Anthropic, Gemini, or DeepSeek.
Cheerio HTML DOM Stripping
Cheerio parses raw scraped HTML in memory, removing <script>, <style>, <svg>, <nav>, <footer>, and tracking attributes while preserving semantic headings, tables, and anchor links.
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<style>.hdr{display:flex;padding:24px;background:#0f172a}.ad-slot{height:250px}</style>
<script src="https://cdn.tracker.example/gtm.js" async></script>
<script>window.__DATA__ = { session: "998123-ab-99", pixel: true };</script>
</head>
<body>
<nav class="hdr"><a href="/">Home</a><a href="/cart">Cart (0)</a></nav>
<main class="article-wrapper" data-tracking-id="v9-prod">
<h1>Q3 Cloud Infrastructure Report</h1>
<p>Total compute spend reached <strong>$142,800</strong> across 14 Kubernetes clusters.</p>
<a href="https://status.example.com/sla">View SLA Metrics</a>
</main>
<footer>© 2026 Corp Inc. All rights reserved. Cookie Preferences</footer>
</body>
</html>Q3 Cloud Infrastructure Report
Total compute spend reached $142,800 across 14 Kubernetes clusters.
View SLA Metrics (https://status.example.com/sla)Four Layers of Deterministic LLM Cost Reduction
Unlike lossy LLM summarization that adds latency and hallucinates facts, ContextMiner runs deterministic local CPU parsers in under 5ms.
Cheerio HTML & Web Noise Sanitization
Scraped web pages and HTML documents carry massive markup overhead. ContextMiner loads HTML payloads into Cheerio on local CPU, stripping <script>, <style>, <svg>, <nav>, and layout wrappers while preserving links and tables—delivering 60–80%+ AI Token Optimization.
BM25 Semantic Relevance Filtering for RAG
Vector databases frequently inject 10 to 20 noisy chunks into every RAG prompt. ContextMiner's Context Window Management scores chunks against the active user prompt using local BM25 + NLP density, pruning irrelevant paragraphs before they inflate your token bill.
Code-Aware AST & JSON Minification
Built for Cursor, Cline, Roo Code, and Aider. Minifies multi-line JSON tool responses, compacts historical <thinking> scratchpads, and prunes repeated system prompt examples while keeping source code and docstrings 100% untouched.
Deterministic Log & Stack-Trace Deduplication
Collapses thousands of identical container heartbeats, CI/CD progress bars, and repeating timestamp lines into compact summary markers—preserving only unique exception frames and status anomalies.
Calculate Your AI Token Optimization Savings
Simulate how Cheerio HTML DOM stripping, local PII masking, and Context Window Management slash your monthly LLM bill.
If you process 500,000,000 tokens (500M/mo) and ContextMiner masks & removes 70% of sensitive data and HTML noise via Cheerio, you save $875 per month ($10,500/year).
Full RAG + Cheerio + Semantic Filter: Combines Cheerio HTML sanitization, BM25 Semantic Relevance Filtering, log deduplication, and Zero-Egress PII Vault masking.
AI Token Optimization & LLM Proxy Questions
How does an LLM Proxy achieve 15% to 80% AI Token Optimization without losing context?
ContextMiner inspects incoming OpenAI-compatible payloads on local CPU before forwarding them upstream. It removes non-semantic bloat—such as HTML DOM scripts and styles via Cheerio, pretty-printed JSON whitespace, repetitive syslog heartbeats, and low-relevance RAG chunks—while preserving every entity, code signature, and instruction.
How does ContextMiner handle HTML noise with Cheerio?
When web scrapers or RAG agents ingest HTML pages, up to 75% of tokens are wasted on <script>, <style>, <svg>, navigation menus, and inline CSS. ContextMiner uses Cheerio in memory to strip non-content DOM nodes and extract a clean semantic text skeleton in under 2ms.
What is Context Window Management and why does it lower LLM costs?
Context Window Management ensures only high-signal tokens occupy your model context window. By combining BM25 Semantic Relevance Filtering with Cross-Context Restoration (CCR) caching, ContextMiner prevents context window overflow, eliminates "Lost in the Middle" hallucination, and slashes input token billing by 40% to 80%.
Will AI Token Optimization break code blocks or tool calls in Cursor and Cline?
No. Switching your API key or connection to IDE & Dev Mode activates code-aware Structural Logic Analysis. It guarantees 100% preservation of raw source code, Python/JSDoc docstrings, tool schemas, and local test endpoints (127.0.0.1 / localhost), while still achieving 40% to 55% LLM Cost Reduction on conversation history and JSON tool outputs.
Need AI LGPD/GDPR Compliance & Zero-Egress LLM Data Masking?
See how ContextMiner masks CPFs, SSNs, credit cards, and emails into reversible vault tokens before prompts leave your infrastructure.