Privacy & On-Device Architecture
EdgeTal is built from the ground up as a zero-cloud, 100% on-device recruitment platform. Candidate resumes, search queries, candidate notes, and AI evaluations remain strictly inside your device hardware sandbox.
0 Bytes Cloud Data Transmission
No third-party LLM APIs, no analytics telemetry, no remote databases. All embedding generation, vector indexing, and generative AI reasoning execute locally.
Native Hardware Acceleration Engine
- ▪Embedding Engine: Swift
MediaPipeTasksTextCocoaPod creating 512d dense vector representations. - ▪LLM Hardware Acceleration: Metal Performance Shaders binding directly to GPU & Apple Neural Engine.
- ▪Local Security: Protected with iOS Keychain and
AppLockServiceFace ID / Touch ID vault locks.
- ▪Embedding Engine: Native Kotlin
EmbedderChannel.ktwith Google TFLite text embedder bindings. - ▪LLM Hardware Acceleration:
LlmChannel.ktovercom.google.mediapipe:tasks-genai(GPU & NNAPI delegates). - ▪Local Database: High-performance ObjectBox HNSW C++ vector engine storing local candidate embeddings.
Core Architectural Highlights
1. 512-Dimensional MediaPipe Vector Search
EdgeTal maps candidate resumes into dense 512-dimensional vector spaces. Natural language queries (such as “Python developer with high-throughput API experience”) match candidate resumes based on conceptual skill relationships, even if exact keywords vary (e.g. matching FastAPI or Django).
2. Quantized Gemma-2B IT On-Device GenAI
Google Gemma-2B IT is loaded into mobile memory using Int4 quantization. The model reads target job descriptions and candidate resumes locally, outputting structured thought reasoning, evidence quotes, and fit recommendations without paying per-token API charges.
3. Passcode Vault Lock & Encrypted Peer-to-Peer Archives
EdgeTal protects your local app with biometric Face ID / Touch ID or PIN passcode locks via AppLockService. When sharing candidate pools with recruiting teammates, EdgeTal exports password-encrypted .edgetal archives over AirDrop, email, or USB.