Prune Smarter, Not Harder
Cut RAG tokens 60–70% with proof your accuracy stays safe.
Performance
Measurable impact on every call
Every pruning operation includes proof of quality preservation
How it works
Four steps to smarter context
Send Query + Docs
Your RAG retrieval results
MMR Selection
Query-aware relevance scoring
Quality Check
Faithfulness evaluation
Get Results
Pruned context + proof
Integration
10 lines to production
Drop-in SDK for Python. Works with LangChain, LlamaIndex, and any RAG pipeline.
from pruni import Pruni
client = Pruni(api_key="your-key")
result = client.prune(
query="What are the key findings?",
documents=retrieved_docs,
target_tokens=8000
)
# 60-70% smaller, with proof
print(result.pruned_context)
print(result.faithfulness_score)The problem
RAG systems over-retrieve
You fetch 20 documents "just in case" and pay for tokens you don't need.
LLMs suffer from "lost in the middle" — they ignore 70% of long context. You're hurting both cost AND accuracy.
80K tokens sent → 15K used → Thousands wasted monthly
The solution
Query-aware compression
Pruni uses extractive compression (MMR + embeddings) to keep only what matters.
Every response includes faithfulness evaluation and full provenance mapping — proof the quality didn't degrade.
60-70% compression + Quality proof + Full audit trail