Humanet
Humanet builds infrastructure and runtimes for isolated AI execution: high-density isolation primitives, deterministic dataflow engines, and security tooling for autonomous AI workloads in production and air-gapped environments.
At a Glance
- Production AI application operators
- Organizations running AI in air-gapped or regulated environments, including hospitals and defense networks
- Developers and platform teams building autonomous AI workloads
- High-throughput data-pipeline and streaming-telemetry teams
- +1 more
AI Tools by Humanet
(1)
airgap-check
Airgap Readiness Scanner for AI Apps
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Latest News
airgap-check v0.2.2 released with Benchmark v2 parser/masking fixes and Docker --run observer hardening
airgap-check v0.2.1 released with robustness_hints separation and held-out Phase 3 benchmark results
airgap-check v0.2.0 released with static lockfile/behavioral analysis and loopback sinkholes
airgap-check v0.1.0 initial CLI release
Products & Services
A high-density Firecracker microVM runtime for AI applications with automatic build-time cache sealing and wire-level Phantom Secret injection.
A zero-dependency static and dynamic auditor for AI repositories that checks offline readiness, runtime downloads, telemetry, and .env secret routing; it can run applications with Docker network isolation, DNS sinkholing, and loopback observation.
A Rust, lock-free, SIMD-accelerated deduplication and anomaly-detection engine built on Hyperdimensional Computing and Vector Symbolic Architectures. It supports exact and near-duplicate detection, LLM prompt/context compression, and anomaly pre-filtering.
A research/experimental runtime that propagates causal dependency updates at write time and materializes derived state so reads become O(1) memory lookups; the public repository contains architectural and theoretical documentation while implementation source is private.
Market Position
Humanet positions itself at the intersection of isolated AI execution, offline-readiness/security auditing, and deterministic dataflow infrastructure. Frame is differentiated around high-density Firecracker microVMs and cache/secret handling; airgap-check focuses on static plus dynamic offline-readiness analysis; CHSE-X focuses on lock-free HDC-based deduplication and prompt compression; PIE focuses on write-time rather than read-time computation. In the adjacent isolation market, Firecracker, gVisor, and Kata Containers are relevant alternatives or underlying technologies: Firecracker is the microVM technology Frame uses, while gVisor and Kata Containers provide other container/VM isolation approaches.
Target Markets
- Production AI application operators
- Organizations running AI in air-gapped or regulated environments, including hospitals and defense networks
- Developers and platform teams building autonomous AI workloads
- High-throughput data-pipeline and streaming-telemetry teams
- Distributed-systems engineers
- Running autonomous AI workloads in production with strong isolation
- Deploying AI applications in air-gapped VPCs, hospitals, defense networks, and offline containers
- Auditing AI repositories for hidden network dependencies, telemetry, and secret routing before deployment
- High-throughput streaming telemetry deduplication and anomaly pre-filtering
- Reducing LLM input-token volume and API cost
- Distributed-state systems that need predictable low-latency reads