Hi HN, I am a 20 year old founder from Louisiana working on MotionOS.
MotionOS is a shared memory layer that sits under AI contact centers and voice agents. Right now most stacks treat each call as a fresh start. History is scattered across tickets and transcripts, so bots and agents keep asking customers to repeat themselves.
MotionOS keeps a single timeline per customer, extracts key memories and decisions from transcripts, and exposes simple APIs so agents and bots can:
• recall the story of a caller at the start of a conversation
• update that memory at the end of each interaction
• track continuity metrics like repeat contact rate
I am very early. The site shows the concept and the first pieces of the system I am building. I would love feedback on:
• whether this memory layer seems useful in real contact center stacks
• what data models or integrations you would expect
• gotchas you see from your own experience with support tools or AI agents
Hi HN, I am a 20 year old founder from Louisiana working on MotionOS.
MotionOS is a shared memory layer that sits under AI contact centers and voice agents. Right now most stacks treat each call as a fresh start. History is scattered across tickets and transcripts, so bots and agents keep asking customers to repeat themselves.
MotionOS keeps a single timeline per customer, extracts key memories and decisions from transcripts, and exposes simple APIs so agents and bots can: • recall the story of a caller at the start of a conversation • update that memory at the end of each interaction • track continuity metrics like repeat contact rate
I am very early. The site shows the concept and the first pieces of the system I am building. I would love feedback on: • whether this memory layer seems useful in real contact center stacks • what data models or integrations you would expect • gotchas you see from your own experience with support tools or AI agents
Thank you for reading and for any blunt feedback.
The github link is broken.