Press releases, research highlights and announcements from Scicom's AI team.
The company's AI Enterprise division releases pyremote, llm-benchmaq, and a GPU Calculator — purpose-built tools that simplify AI infrastructure management, standardize large language model benchmarking, and give any organization the ability to estimate GPU requirements and project real-world inference performance.
Scicom demonstrates breakthrough performance in multilingual TTS, optimized inference, and enterprise AI infrastructure — outperforming GPT-4o on speech benchmarks while contributing over 141,000 hours of open-source training data
False cutoff rate across ten end-of-turn systems at two latency budgets, measured over 300 real call turns on Malaysian telephony. The lowest of any system we tested, at both.
Our open source speech enhancement model cuts character error rate on a private call centre set from 71.48% to 19.01%. Here is what we are building on top of it.
A single codebook at 50 tokens per second, scored against four other 44 kHz-class codecs on a 9,291-clip multilingual wideband set.
Benchmarked across more than 400 languages, and ready to post-train a language model into a speech model without touching the architecture.
Cluster configuration, node bootstrapping, a browser terminal, job insights and proxy exposure, without the terminal windows and SSH configs.