Search Systems
Designing scalable retrieval pipelines capable of delivering precise context at scale.
Independent Engineering Laboratory
Engineering better context.
Lerya Labs explores advanced information retrieval, context-aware AI systems, GraphRAG, and resilient software architecture.
Mission
A lab for systems that retrieve, reason, and remain understandable.
Lerya Labs studies the engineering domains behind reliable AI systems: search, retrieval, context graphs, knowledge graphs, observability, distributed systems, and performance engineering. The work is exploratory, technical, and grounded in architecture.
Focus Areas
Core engineering domains.
Context Graphs
Exploring relationships between users, knowledge, entities, and interactions.
Open cockpitGraphRAG
Combining graph reasoning with semantic retrieval to improve context assembly.
AI Infrastructure
Reliable systems for indexing, orchestration, evaluation, and modern AI workloads.
Engineering
Architecture, performance, distributed systems, observability, and software craftsmanship.
ContextGraph Module
A cockpit for understanding how knowledge flows.
Inspect entity extraction, normalization, permission overlays, propagation traces, and raw graph state across the retrieval pipeline.
Philosophy
Build systems that are understandable before making them intelligent.
Current Research
Active lines of inquiry.
Reasoning across graph structure and semantic retrieval layers.
Identifying durable entities from unstructured and conversational sources.
Maintaining coherent relationships across evolving knowledge surfaces.
Coordinating search, ranking, reranking, memory, and evaluation paths.
Balancing lexical precision, embedding recall, and operational latency.