# Avalanche Computing Taiwan Inc.

> Reevol Source machine-readable supplier card. Profile URL: https://source.reevol.com/en/suppliers/avalanche-computing-taiwan/. Locale: en.

## Profile status
- Unclaimed profile. The details below come from public business registries, trade-fair exhibitor lists and certification bodies, and have not yet been confirmed by the company itself. Fields that are absent here are unconfirmed, not negative findings.
- The company can claim this profile free at https://source.reevol.com/en/claim/avalanche-computing-taiwan/ to confirm and complete it.

## Identity
- Legal name (en): Avalanche Computing Taiwan Inc.
- Business type: unknown
- Location: Taipei, Taiwan
- Year founded: 2018
- Employee range: 8

## Industry
- Primary: Consumer Electronics
- Sub-industries: hAIsten AI, AIthena, GenAI, Agent, deep learning, confidential computing, low-code deployment, AI model training, AI framework optimization, compliance-ready AI
- HS codes: 8471, 8523

## Digital footprint
- Website: http://www.avalanc.com
- LinkedIn: http://www.linkedin.com/company/avalanc

## Summary
What we know: Based in Taipei, Taiwan; founded 2018 (verified by year founded and country) Team of 8 (verified by team size) Operates in consumer electronics with focus on AI frameworks, deep learning, and confidential computing (verified by industry and industry tags)

What the data suggests: Likely manufactures or integrates computing hardware or data storage media (suggested by HS codes 8471 and 8523) Targets enterprise AI deployment given emphasis on compliance-ready AI and low-code solutions (suggested by industry tags)

Not yet verified: Website, Certifications, Trade fairs, Export markets, Production capacity.

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## Provenance
- Last enriched: 2026-10-21T15:08:18.495Z
- AI content last generated: 2026-10-21T15:32:28.230Z
- Source: Reevol Source (https://source.reevol.com). Underlying data from Reevol, sourced from public business registries, trade-fair exhibitor lists, certification bodies, and digital footprint.
- License: see https://source.reevol.com/en/methodology/