Advancing Benchmarks.
We focus on technical benchmarks, runtime latencies, and token-saving metrics to prove the efficiency of persistent agent memory.
92% Context Reduction Methodology
How we compress large conversational histories into compact relevance summaries, reducing tokens sent to the LLM by up to 92% (projected) while preserving core entity state.
Sub-100ms End-to-End Retrieval
Technical analysis of the routing pipeline showing how semantic vector matching, graph node traversal, and caching layers resolve context under 85ms (projected).
Temporal Semantic Decay
Mathematical formulations governing memory strength, pruning algorithms for stale context, and entity relevance reinforcement based on access frequency.
Want to explore our core research directions?
Visit Haqikos Research to read about the cognitive science concepts, theoretical math, and multi-agent shared memory architectures powering our product.