Compression research for large-scale information systems.
We design efficient indexing, compression, and distributed processing architectures for high-density data environments.
Full-spectrum
signal acquisition
Air-gapped
deployment capable
Zero-retention
configurable
Research
Core research areas
Our work spans foundational systems research and applied engineering, with a focus on efficiency, correctness, and scale.
Compression Systems
We develop entropy-aware compression pipelines for structured and semi-structured data at scale. Our architectures reduce storage costs without sacrificing retrieval fidelity or latency.
Distributed Indexing
Scalable inverted and forward index designs for distributed query environments. We optimize shard allocation, merge strategies, and consistency models for sustained high-throughput workloads.
Signal Reconstruction
Probabilistic reconstruction of high-dimensional signals from sparse observations. Applied to telemetry, sensor fusion, and data-stream recovery under constrained bandwidth.
Data Structure Optimization
Rigorous design and analysis of cache-aware, memory-efficient data structures. We target bloom filter variants, succinct structures, and adaptive B-tree derivatives for modern hardware.
Infrastructure
Operational scale
Our infrastructure operates continuously at production scale, supporting research workloads and applied deployments across geographically distributed clusters. Offshore resource allocation provides operational independence from any single jurisdiction or provider.
Petabyte-scale
Total indexed volume
across active partitions
Billions daily
Records processed
sustained throughput
Single-digit ms
Average retrieval latency
p50 across all clusters
>99.9%
Pipeline availability
rolling 90-day window
Processing topology
LiveDistributed
Shards
Redundant
Replicas
Offshore
Availability
Publications
Research findings
Selected research outputs from the greyconnaissance group. Full papers available upon request.
Adaptive Entropy Coding for Heterogeneous Data Streams
Compression Systems
We introduce a context-adaptive entropy coder that dynamically adjusts its model based on observed symbol distributions, achieving 12–18% reduction over static Huffman coding on mixed telemetry workloads.
Principles
Operating principles
Precision
Every system we build is grounded in formal analysis. We hold our work to the standard of verifiable correctness — not engineering intuition, not approximation, not convenient assumptions. Measurement is the discipline.
Efficiency
We treat computational resources as a constraint to be reasoned about, not a budget to be consumed. The best algorithm is the one that wastes nothing — in time, in space, in complexity of operation.
Restraint
Complexity is a liability. We resist the urge to add abstraction, to generalize prematurely, to accumulate features. The goal is the minimum system that is completely correct — not the most expressive one that might be.
Discretion
We operate neutrally across jurisdictions, with no preferential alignment to any single regulatory environment. Our infrastructure is designed for covert continuity — client work, research outputs, and operational details remain strictly confidential.
Contact
Correspondence
For research collaboration, infrastructure partnership, or general correspondence. We respond to substantive inquiries within three business days.