Use Case
Edge AI with Endee
Run a full vector database on-device. Sub-10ms search on Raspberry Pi, Android, and NVIDIA Jetson with no cloud required.
<10ms
on-device query latency
99%+
recall accuracy
Zero
cloud dependencies
Supported hardware
Raspberry Pi 4 (ARM)
ARMv8, 1-8 GB RAM
Android (ARM64)
API 26+, any ARM64 device
NVIDIA Jetson
Nano, Xavier, Orin
x86 Linux
Edge servers, industrial PCs
Built for constrained hardware
Raspberry Pi and Jetson Ready
Native ARM and ARM64 binaries ship with no cross-compilation and no Docker overhead on constrained boards. Deploy with a single binary on Raspberry Pi 4, NVIDIA Jetson Nano, Jetson Orin, and any ARMv8 Linux device. CPU-only inference with no GPU required.
Fully Offline
Zero cloud dependency after deployment. Data never leaves the device. Works in air-gapped factories, offline mobile apps, and remote IoT installations where connectivity is intermittent or prohibited by compliance requirements.
Sub-10ms Query Latency
Real-time vector search on constrained hardware with no network round-trips. Search a million vectors in under 10ms on a Raspberry Pi 4 using INT8 quantization, which reduces memory footprint by 75% to fit large indexes in limited RAM.
Android Native Libraries
Embed Endee directly into Android apps via native ARM64 libraries. Run visual search, document RAG, or voice search fully on-device without any API calls. The library integrates with standard Android build tooling via a JNI wrapper.
How it works
Package the Endee binary for your target
Download the pre-built binary for your hardware platform: ARM for Raspberry Pi, ARM64 for Android or Jetson, or x86-64 for Linux edge servers. No compilation required. The binary includes the full Vector Graph Engine with all quantization levels.
Load your vector index at startup
Build your index offline using the Endee Python SDK, then export it to a file. At device startup, load the index file into memory. Use INT8 precision to reduce a 1M-vector index from 3 GB (FLOAT32) to under 400 MB so it fits in constrained RAM.
Serve queries offline
Embed user input on-device using a lightweight model such as all-MiniLM-L6-v2 (22 MB) and query the local Endee instance. Results return in under 10ms with no network call. The device operates identically whether connected to the internet or fully air-gapped.