Token Optimization
Intelligent context management for 40-60% token reduction.
The Token Efficiency Problem
Traditional AI applications send full conversation histories with each request, leading to linear token scaling with conversation length. This approach results in wasted compute on irrelevant context, increased latency, and unnecessary costs.
Cortyxia transforms this into a semantic retrieval problem, injecting only the most relevant memory nodes based on query intent. The system employs multiple optimization strategies including context compression, selective injection, and intelligent caching to achieve 40-60% token reduction while maintaining response quality.
Optimization Strategies
Semantic Retrieval
Instead of sending full conversation history, the MMU query engine analyzes query intent and retrieves only relevant memory nodes. BM25 indexing combined with semantic reranking ensures high-precision context selection.
CAS Deduplication
Content-addressable storage eliminates duplicate information across conversations. Identical content maps to the same SHA-256 hash, storing once and referencing multiple times. Typical storage reduction of 30-50%.
Context Compression
Intelligent compression algorithms reduce token count while preserving semantic meaning. Techniques include filler word removal, redundancy elimination, and abbreviation substitution while maintaining critical details like names, dates, and numbers.
Selective Injection
Not all queries require context. The system analyzes semantic need and only injects memory when relevant nodes exist above confidence thresholds. Simple queries bypass memory retrieval entirely for zero-overhead responses.
Token Budget Management
Respects context window limits while maximizing information density. Allocates tokens across system prompt, current query, and memory nodes with dynamic ranking by relevance score. Includes safety margin buffer for edge cases.
Real-World Performance
Customer Support Bot
69%8,000 → 2,500 tokens per conversation
Internal Knowledge Base
84%5,000 → 800 tokens per query
AI Agent
63%12,000 → 4,500 tokens per session
Cost Impact Analysis
Monthly Savings Calculation (100K requests)
Based on example provider pricing at $0.15/1M input tokens
Observability & Tracking
OSuite gives you four lenses into every inference: compare models, audit prompts, examine guardrails, and trace every step — all in one pane.
Model Comparison
Benchmark every model across cost, latency, and token usage. Track 6 quality metrics — hallucination, groundedness, drift, relevance, safety, and accuracy — in a unified leaderboard.
Prompt Metrics
See how each prompt fares between models on the 6 core metrics. Spot weak prompts, compare outputs side-by-side, and optimize what you send to the LLM.
Tracer
Full granular visibility into every message. Trace tool calls, memory searches, context retrieval, and agent reasoning across the entire pipeline — no black boxes, full accountability.
Guardrail Check
Auto-detect behavior and guardrails, ranging from positive to tone to styling, and more — from “you are a marketing bot” to “do not mention Topic X”. Every message pair is checked for compliance with full violation traces.