EdgeTal Documentation & User Guides
Welcome to the official EdgeTal documentation. Learn how to parse candidate resumes, perform 512d dense vector candidate searches, run explainable AI fit screening using Gemma-2B, and manage candidate pipelines 100% on-device.
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Explore Documentation Topics
How to Use Guide
Step-by-step onboarding: resume import, creating job roles, sub-100ms 512d vector search, Gemma-2B IT fit analysis, and peer-to-peer sharing.
iOS Public Beta (TestFlight)
iOS beta is ready for testing! Learn how to connect your iCloud email address to get added to the Apple TestFlight public beta build 1.0.1+6.
Privacy & Architecture
Deep-dive into zero-cloud AI architecture: MediaPipe 512d embeddings, quantized Gemma-2B LLM, Apple Metal & Android GPU delegates, and Vault Lock.
Bulk Resume Ingestion
Import thousands of candidate profiles from PDF/DOCX folders, cloud drives (iCloud/Google Drive/OneDrive), or structured CSV files in minutes.
Job Roles & Pipeline Management
Manage job descriptions, required competencies, 3-tab pipeline stages (Shortlisted → Placed), and export password-encrypted .edgetal packages.
EdgeTal Platform Overview at a Glance
| Dimension | Traditional Cloud ATS | EdgeTal On-Device Platform |
|---|---|---|
| Data Privacy | CVs stored on third-party cloud servers | 100% On-Device Vault (0 Cloud Bytes) |
| LLM API Charges | Pay-per-token ($0.03–$0.10 / analysis) | $0 Token Fees (Unlimited local Gemma-2B) |
| Search Technology | Basic exact string / boolean matching | 512d MediaPipe Dense Vector Search |
| Offline Capability | Fails without active high-speed internet | 100% Offline Capable (Search & LLM) |
| Team Collaboration | Unencrypted cloud database sync | Password-encrypted .edgetal archives |