HighSNR
Less tokens, less noise — compress docs and RAG context
Details
- Categories
- AI
- Use Cases
- AI AgentsWorkflow Automation
- Target Audience
- DevelopersData ScientistsBackend Developers
- Platforms
- API
About HighSNR
HighSNR is a context compression/denoising API that removes low-signal passages from long documents and RAG retrieval results before they reach your LLM. Send a document and a token budget, get back only the high-signal chunks. Fully deterministic, zero data retention, no AI model in the processing pipeline — runs fast on commodity CPUs. Benchmarked on LongBench v1 with GPT-4o (200 samples): at 90% budget, beats full-context F1 on HotpotQA (71.57 vs 69.71) and retains 97.9% of full-context quality on Qasper. Budget is accurate end-to-end. Free tier: 2M tokens, 14 days, no card required. LangChain integration available via pip. Self-hosted option in progress for teams that need data to never leave their network.
Product Insights
HighSNR is an API-based AI context compression tool designed for Developers, Data Scientists, and Backend Developers building AI Agents, AI Chatbots, and workflow automation. It operates with a free tier to deliver fully deterministic CPU-based document denoising for LLM optimization.
- Fully deterministic, CPU-based compression pipeline with zero data retention.
- Accurate end-to-end token budgeting with available LangChain integrations.
- Proven benchmarks showing high-quality preservation on GPT-4o and Claude Sonnet 4.5.
- Offers a 14-day free tier with 2 million tokens and no credit card required.
Ideal for: Ideal for Developers, Data Scientists, and Backend Developers creating AI Agents and Chatbots who need to compress RAG context and manage LLM token budgets.
Product Updates (1)
HighSNR v2 is out!
v2 is out with better signal selection, chunks input, and stronger benchmarks. The v2 engine is significantly more accurate at identifying high-signal passages, especially when you pass a query hint. Updated benchmarks (Claude Sonnet 4.5, n=200): - HotpotQA: beats full-document quality at 40–60% budget - Qasper: 97% of full-document quality at 50% of the tokens - Beats random selection by 5–15 F1 points across all budgets Holds across both GPT-4o and Sonnet 4.5, i.e., it's not model-specific. Free tier unchanged: 2M tokens, 14 days, no card.
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