Find the right person,
not just the right keyword.
EdgeTal brings semantic candidate search and AI-assisted screening to recruiters and hiring teams — and runs it all on the device in your hand, so résumé data never touches a server.
Private by architecture.
Fast by design.
Most hiring tools send your candidates' most sensitive data to the cloud. EdgeTal doesn't. It indexes résumés locally, understands what you're actually looking for, and ranks candidates by genuine fit — all offline, after a one-time setup.
Search the way you think.
Type "a backend engineer who knows Kafka and has led a small team" and get ranked matches — no boolean gymnastics, no rigid filters.
Understands meaning, not just keywords.
On-device semantic search reads skills, experience and summaries the way a recruiter would, and shows you why each candidate surfaced.
AI fit analysis in seconds.
Paste a role and EdgeTal reasons through each candidate's strengths and gaps, then gives a clear shortlisting recommendation.
Private by architecture.
Indexing, search and AI reasoning all happen locally. Nothing is uploaded, cached or indexed elsewhere.
GDPR by design.
Because candidate data never leaves the device, you sidestep the cross-border transfer and third-party processing headaches that come with cloud screening tools.
Works offline.
Download the on-device model once, then screen anywhere — on a plane, in a secure facility, or off the grid.
Three steps. Fully offline.
Import
Bring in résumés from a CSV — URL or local file. EdgeTal parses and indexes them on-device in seconds, building a private semantic index that never leaves your device.
Search
Describe your ideal candidate in plain language. EdgeTal converts it to meaning and ranks your pool by fit, with matching evidence highlighted so you see exactly why each person surfaced.
Analyse
Open a candidate, paste the role, and let the on-device model produce a strengths and gaps breakdown with a clear shortlist verdict — all without a network request.
Built for privacy-conscious hiring. EdgeTal is made for in-house recruiters, talent partners and hiring managers who need speed andcompliance — agencies handling sensitive candidate pools, employers under strict data-residency rules, and anyone who'd rather keep talent data in-house than hand it to a third party.
Research-Backed Architecture
EdgeTal's architecture is published in a peer-reviewed research paper presented at EICON 2026 (ESOFT International Conference). The system was empirically evaluated across two hardware tiers with benchmarks on retrieval accuracy, latency, scalability, and generative analysis quality.
EICON 2026 · ESOFT Uni, Sri Lanka · August 2026