posting velocity //
Opens vs closes per day
Based on 0 events over 90 days. Green days had more opens than closes, red vice-versa. The dark line is the 7-day rolling average.
Posting timing (day/hour) is available only when there are at least 5 jobs with a real publish stamp spread across 3 distinct days. This company's source doesn't expose post times, or there isn't enough data yet — showing what we know for sure: how many jobs are open, in which domains, and at which seniority levels.
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Open now
0
Total active openings across all sites
Δ 28-day
0
Opens minus closes in the last 28 days
Δ 90-day
0
Opens minus closes in the last 90 days
posting velocity //
Based on 0 events over 90 days. Green days had more opens than closes, red vice-versa. The dark line is the 7-day rolling average.
role mix //
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The green layer is the current share of active openings by role. The grey dashed layer is the 90-day baseline — gaps between them show where the company is shifting its hiring mix.
seniority pyramid //
Seniority is not exposed by the source for this company.
Distribution of active openings by seniority. The 'unknown' row groups jobs from sources that don't expose seniority.
geography //
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Active openings by region. Click a row to see jobs in that area.
time on market //
Median
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25th pct
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75th pct
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Based on 0 closed jobs and 0 still open (right-censored). Curve is Kaplan-Meier; band is the 95% CI.
Window: 180 days back. Don't read the mean — the long tail biases it. Median and percentiles are the honest summary.
Republish rate
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Fewer than 10 closures in the window — not enough to compute.
company intel · ai-generated
Updated 1d ago
Edgify is an Israeli AI company focused on on-device (edge) machine learning, enabling retailers to run computer-vision models directly on point-of-sale hardware without sending data to the cloud. Detailed public information on founding year, founders, headcount, funding rounds, and valuation is limited.
Edgify's primary product applies computer vision at the retail checkout: its models run on the GPU or CPU embedded in self-checkout terminals to recognize items — including fresh produce — reducing shrinkage and checkout errors without requiring a continuous cloud connection. The buyers are large grocery and general-merchandise retailers. The technical moat is federated learning across a fleet of edge devices, allowing models to improve collectively while keeping raw transaction data on-premises.
key people
Key people for this company are being prepared — they will appear here as soon as available.
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