Token Optimization
Intelligent context management with up to 80.8% fewer prompt tokens on published evals.
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. On our published 50-question enterprise governance eval, that meant 80.8% fewer prompt tokens versus full-context replay with answer quality held, compounding to 10.2× by question 50.
Optimization Strategies
Semantic Retrieval
Instead of sending full conversation history, the Cortyxia engine 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.
Published Eval Results
Enterprise governance
80.8%Fewer prompt tokens vs full-context on a 50-question session. Quality held. Compounds to 10.2× by question 50.
IDE coding
91.5%Token reduction on a 20-turn emulated IDE session with comparable code quality.
SWE-style fixes
70%Fewer tokens, with 100% task resolution vs 73.3% for full-context.
Full methodology and charts on the research page.
Cost Impact Analysis
Illustrative monthly savings (100K requests)
Based on example provider pricing at $0.15/1M input tokens. Actual savings depend on workload; see published evals for measured results.
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.