LogMeIn scales meeting data processing 1400% with super.AI humans-and-AI pipeline
LogMeIn was processing meeting recordings internally to power their Scopus.io meeting bot, but the internal team could not scale fast enough, forcing technical shortcuts and compromises to user experience.
A pure AI approach would sacrifice output quality to achieve desired turnaround times, while a 100% human solution would not meaningfully accelerate throughput beyond their existing internal team.
super.AI scaled LogMeIn's production traffic by 1400% and enabled a full 100% transition to the super.AI platform, freeing the LogMeIn team to focus on developing new product features and preparing for customer rollout.
Frequently asked questions
What did this team achieve with this AI workflow?
super.AI scaled LogMeIn's production traffic by 1400% and enabled a full 100% transition to the super.AI platform, freeing the LogMeIn team to focus on developing new product features and preparing for customer rollout.
What tools did this team use?
super.AI, Scopus.io.
What results were reported?
Production traffic scaling: 1400%; transition to super.AI platform: 100% (source-reported, not independently verified).
What failed first in this deployment?
A pure AI approach would sacrifice output quality to achieve desired turnaround times, while a 100% human solution would not meaningfully accelerate throughput beyond their existing internal team.
How is this back office ops AI workflow structured?
Meeting data submitted via API → Proprietary AI program processes recordings → Human intelligence in the loop → Real-time results delivered via API.