Research

What we are building

Our research spans the full AI infrastructure stack — from data pipelines and memory architecture to satellite intelligence and autonomous systems.

Active

Ignara Fabric

Data Pipeline Acceleration

ML training is bottlenecked at data loading more often than compute. Ignara Fabric is a pipeline acceleration layer that eliminates I/O wait time during training, keeping GPUs saturated and reducing time-to-model.

PyTorchFastAPIPrometheusGrafanaDocker
Active

Ignara Space Intelligence

Space Situational Awareness

Real-time satellite tracking and orbital analytics powered by TLE ingestion, sgp4 propagation, and AI-driven anomaly detection. Built for operational use in defense, commercial, and research contexts.

sgp4TLEFastAPIReal-timeEdge AI
Exploratory

CXL Memory Research

Disaggregated Memory

Exploring Compute Express Link (CXL) as a vehicle for disaggregating memory from compute — enabling AI systems to scale memory capacity independently and eliminate stranded DRAM in large GPU clusters.

CXLMemory FabricNUMAHBM
Exploratory

KV-Cache Optimization

Inference Memory Orchestration

Research into software-defined memory orchestration for LLM inference — KV-cache subsystem optimization, paged attention mechanisms, and token memory efficiency at deployment scale.

LLMKV-CachePaged AttentionInference
Research Philosophy

We build to learn.
We learn to build.

At Ignara AI, research and engineering are not separate disciplines. Every exploratory project produces runnable artifacts with reproducible benchmarks. Every production system produces insights that feed back into research.

The most important advances in AI infrastructure come from teams willing to go deep on hard problems — memory hierarchy, data throughput, distributed coordination — and bring both systems-level rigor and scientific curiosity to the work.

Collaborate With Us

We ship runnable code, not whitepapers

Every benchmark is reproducible

Research informs engineering, engineering informs research

We go deep on hard systems problems

Long-term thinking over short-term optimization