Proof, not promises.
We publish our benchmarks, architectural decisions, and performance data. Every claim is backed by reproducible results.
Published Reports
Deep-dives into architecture, benchmarks, and methodology.
Consensus-Based Replay Filtering for Zero False Positive LLM Decision Caching
We evaluate four consensus filter configurations across 4,000 multi-turn benchmark conversations and find that strict unanimous consensus (K=3) reduces false positive replay rates from 17.1% to 2.5%.
100-Conversation Benchmark: Decision Replay Performance
Head-to-head comparison of direct OpenAI calls vs Decyra proxy across 100 multi-turn agentic conversations with 8 financial tools.
Decision Replay Architectures for Agentic AI
Semantic caching, ML-based action routing, and scalable edge inference — a rigorous evaluation of approaches for reducing LLM cost in agentic workflows.
See the savings for yourself
Integrate Decyra in 3 lines of code. Watch your AI agent costs drop as the cache warms — backed by the same methodology in our published benchmarks.