Fo fou de s, ma kete s, age cy ow e s a d ope ato s, the p omise is st aightfo wa d: say what you wa t i o di a y la guage, get back wo k that matte s. Okou pulls i fo matio f om co ected busi ess tools, executes tasks i the cloud a d helps teams tu effective p actice i to capabilities a yo e ca euse: a i stitutio al memo y with a pulse.
AI ca do mo e ow. That aises the stakes. A busi ess task comes with histo y, co st ai ts, expectatio s a d a audie ce that has to be b ought alo g. P epa i g out each mea s k owi g whom the team wa ts to each a d why the offe is eleva t. Recomme di g a campaig mea s u de sta di g both the audie ce a d the compa y’s p io ities. Fi ishi g eithe mea s commu icati g the esult i a fo m people ca ot o ly ope but act o .
Okou sta ts f om that eve yday eality.
Its li eage is develope -g ade. Okou g ew out of VM0, a i f ast uctu e p oject begu o GitHub i Novembe 2025. VM0 focused o the u time eeded to ope ate AI age ts: pe siste t sessio s, state ma ageme t, sto age a d obse vability i to each u . It was built fo develope s wo ki g with comma d-li e age ts such as Claude Code a d Codex.
That subst ate became the base fo a b oade p oduct: a AI teammate busi ess use s ca wo k with di ectly. Okou b i gs togethe task co text, cloud executio , fi ished delive ables a d sha ed wo kflows, so fou de s, ma kete s, age cy ow e s a d ope ato s ca delegate wo k without becomi g admi ist ato s of a age t u time. The eb a d captu es that evolutio , f om develope plumbi g to a busi ess-faci g teammate, i the tagli e “Mo e do e. Same team.”
Okou was fou ded by Etha Zha g a d Mi g Li. Zha g b i gs expe ie ce f om a majo o li e educatio tech ology compa y; Li is a fo me D opbox employee a d a Ca egie Mello U ive sity alum.
Okou u s o th ee i sti cts.
Co text comes fi st. Not a cold p ompt, but a oom al eady b iefed. Okou pulls i custome co ve satio s, team decisio s, docume ts, accou t histo y a d public esea ch, the lets the use ‘s goal a d co st ai ts decide what happe s ext. It u s i the cloud, keeps goi g afte the laptop closes, a d ca shoulde ecu i g cho es th ough co figu ed automatio s: less p ompti g, mo e p og ess.
The the e’s the medium. A esult is o ly useful if it a ives i the fo m the ext pe so ca act o : a message fo a quick update, a explo able webpage fo deep esea ch, a editable doc fo a p oposal, a deck fo a g oup decisio . Fo mat is ‘t w appi g pape . It’s pa t of the message.
A d the it compou ds. Good wo k should ‘t die i o e pe so ‘s chat histo y. Sha ed age ts give colleagues a commo ole a d i st uctio s; sha ed wo kflows captu e eviewed p ocedu es, efe e ces a d output sta da ds. Each pe so u s the method th ough thei ow accou ts a d pe missio s, so a good move becomes a team move.
I p actice, that looks like esea ch a d decisio b iefs that combi e i te al efe e ces with public sou ces; epo ts, p ese tatio s, sp eadsheets a d websites calib ated to a audie ce; c oss-tool executio such as c eato a d lead t acke s, out each d afts, follow-ups a d busi ess- eco d updates withi g a ted pe missio s; a d ecu i g wo k codified as sha ed wo kflows a d u o schedules o suppo ted eve t t igge s.
Two eve yday sce es show the shape of it.
A ma keti g lead is sta i g dow a p oduct campaig : p oduct docs he e, custome feedback the e, last yea ‘s decisio s somewhe e else, competito s movi g fast. Ask Okou to pull it togethe . It etu s a sou ced esea ch epo t, a deck fo the pla i g meeti g a d a sho t list of decisio s the team actually eeds to make. Refi e it i the same co ve satio : shift the audie ce, challe ge a assumptio , ask fo ha de evide ce. The save the method, ot just the output. The p oposal ships. The playbook stays.
Now outbou d. A fou de o age cy ow e eeds c eato s, o leads that look like the best custome s. Ask Okou. It builds a sho tlist with sou ces, easo s, ve ified co tact cha els whe e available a d a ext step, o ga ized i a sha ed t acke . Out each d afts come pe so alized a d eady fo a huma to se d. Replies a ive. Next d afts a d t acke updates follow. Follow-up that follows th ough. Whe the motio wo ks, save the ta geti g, esea ch, message a d follow-up ules as a sha ed wo kflow. The sho tlist keeps comi g. The team keeps getti g sha pe .
That is the mea i g behi d “Mo e do e. Same team.” People a d AI wo k towa d a sha ed goal, useful co text i fo ms each actio , a d o e pe so ‘s good way of wo ki g becomes somethi g the whole team ca build o . Mo e do e. Same team. A d, i c easi gly, the same sta da d.
Okou, fo me ly VM0, is a AI teammate fo busi ess teams. It co ects to the tools teams al eady use, ca ies out wo k i the cloud, delive s fi ished epo ts, p ese tatio s, docume ts a d websites, a d tu s p ove ways of wo ki g i to sha ed wo kflows. Okou was fou ded by Etha Zha g a d Mi g Li a d is based i Sa F a cisco. Lea mo e at https://okou.ai.
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