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description: A team called Catphish won 'Best Use of Valkey API' at HackNC State 2026 by building a voice identity verification system for telecom fraud prevention. The...
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og:description: A team called Catphish won 'Best Use of Valkey API' at HackNC State 2026 by building a voice identity verification system for telecom fraud prevention. The...
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# Valkey · Victory at HackNC State: Innovating for the Telecom Industry with Valkey

**[Valkey](https://daily.dev/sources/valkey-blog)** · 7 min read · 0 upvotes · 0 comments

## Summary

A team called Catphish won 'Best Use of Valkey API' at HackNC State 2026 by building a voice identity verification system for telecom fraud prevention. The system uses a two-layer approach: voice embedding matching plus dynamic cognitive prompts (like counting or spelling) that are easy for humans but hard for AI text-to-speech systems to answer naturally, distinguishing real speakers from voice clones. Valkey's in-memory key-value store was used to hold voice embeddings, session data, time-limited challenge prompts, API keys, and rate-limiting counters, enabling millisecond-latency lookups without schema design or migrations. The team settled on an ~85% similarity threshold for voice matching and envisioned the system as a B2B redirect-based API similar to Stripe's payment links.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://valkey.io/blog/victory-at-hackncstate>

## Questions this post answers

### How can I detect AI voice clones during phone-based authentication?

Combine voice embedding matching with dynamic cognitive prompts that ask users to perform a small task, such as counting from one to five or spelling a word, rather than reading a fixed phrase. Humans respond naturally to the instruction while many text-to-speech systems read the instruction word-for-word, exposing synthetic voices. A voice similarity threshold around 85 percent balances rejecting attackers against tolerating legitimate variation.

_daily.dev surfaces engineering writeups like this for developers building anti-fraud authentication flows._

### What data should I store in Valkey for a real-time voice authentication system?

Store voice embeddings for enrolled users, session data for authentication attempts, time-limited challenge prompts, API keys for tenant authentication, and rate-limiting counters. Valkey's in-memory key-value architecture retrieves this data in milliseconds without requiring schema design or migrations, which matters for pipelines involving audio capture, embedding generation, and verification scoring.

_Developers weighing in-memory stores for latency-sensitive auth pipelines can track patterns like this on daily.dev._

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---

Tags: [#authentication](https://daily.dev/tags/authentication), [#hackathon](https://daily.dev/tags/hackathon), [#valkey](https://daily.dev/tags/valkey)

[View this post on daily.dev](https://daily.dev/posts/valkey-victory-at-hacknc-state-innovating-for-the-telecom-industry-with-valkey-ppza39cku)

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