McKinsey estimates today's generative AI and related technologies can automate activities that take 60-70% of employee time.
product / CompanyOS
CompanyOS turns scattered AI tools into operating leverage.
Your team already has models, chats, docs, tickets, CRMs, dashboards, and automations. CompanyOS connects them into one governed execution layer where humans and agents know the context, own the next step, and learn from the outcome.
Knowledge workers spend roughly one day a week searching and gathering information instead of executing.
Microsoft found people spend most Microsoft 365 work time communicating, not creating.
A 5,172-agent field study found embedded AI assistance increased support productivity by 15% on average.
AI can already touch most knowledge work. The hard part is turning that capability into a company that executes with less search, less coordination drag, and more accountable follow-through.
the shift
CompanyOS turns fragmented knowledge, hidden processes, and isolated assistants into shared operating memory for human and AI execution.
01 / operating layer
Most software stores information. CompanyOS makes it executable.
Knowledge workers lose roughly one day a week searching and gathering information. CompanyOS sits above Slack, Teams, email, CRM, ERP, project management, documentation, customer conversations, and operational systems so agents can act from the same operating truth as the team.
02 / multiplayer company
The value does not live in one person's chat history.
Microsoft found people spend more time communicating than creating. Personal AI chats help individuals, but they do not remove coordination load. CompanyOS is multiplayer by design: decisions become organizational memory, workflows become reusable, and agents inherit the same context as the humans who own the work.
03 / package
A practical path from company memory to AI-native enterprise.
The path starts with a Company Brain, adds assisted workflows, introduces governed agents, and expands only where measurable work is ready for production.
04 / AI workforce
Workflow-specific AI workers, coordinated by one operating layer.
Studies show embedded AI assistance can lift support productivity, and the strongest AI-native products are moving toward agents with context, permissions, audit trails, and production tooling. CompanyOS brings that pattern to operating work across sales, support, finance, operations, and domain-specific environments.
05 / deployment
Start fast, then deepen the operating model where ROI is clear.
Individual AI tools can reduce email and drafting time, but meetings, handoffs, and approvals only improve when the workflow changes. CompanyOS starts with a narrow production lane, proves the operating gain, then expands the model.
06 / why we win
The moat is not the model. The moat is how the business learns.
Models commoditize. The defensible system is the company's workflow history: decisions, approvals, exceptions, customer context, outcomes, and feedback loops that compound every week.
research signal
The pattern is already visible.
The market is moving from AI as a personal assistant toward AI as a governed operating layer: connected data, agent permissions, audit logs, observability, model routing, sandboxes, and workflows that reach production.
McKinsey
$2.6T-$4.4T annual genAI potential, 60-70% activity exposure, and one day per week lost to search.
Microsoft Work Trend Index57% of Microsoft 365 time spent communicating, 43% creating, and Teams meetings/calls up 192% since February 2020.
Generative AI at workA 5,172-agent field study found embedded AI assistance increased customer support productivity by 15% on average.
6,000-worker AI field studyAI reduced email time for active users, but did not significantly change meeting time without deeper workflow redesign.
GumloopBusiness teams are adopting connected workflow agents with data integrations, recurring tasks, RBAC, audit logs, and usage monitoring.
ReplitAI-native work is moving toward agents that plan, build, test, coordinate tasks, request approvals, and operate with enterprise controls.
CursorEngineering teams are standardizing on codebase-aware agents, model controls, MCP controls, PR review, analytics, and team governance.
Vercel AI CloudProduction AI now needs routing, model failover, sandboxes, observability, workflows, security, and cost controls.
become AI-native
Bring the workflow where complexity is growing faster than output.
We will map the operating truth, connect the systems, define the governance layer, and deploy the first AI workers where they can produce measurable leverage.