Claude
Shared company context
ProductsUse case · Company-wide AI adoption
Give every team the best AI for the job - with one company memory.
Let people work in Claude, Codex, ChatGPT, Cursor, and the tools they prefer while company context, shared capabilities, and completed work keep compounding in HQ.
Start free. Connect two teammates through one shared workflow.
The company layerMemory, skills, access, projects
Shared through HQOne operating system
Codex
Shared company skills
Your AI tools
Shared company work
Model freedom for the team. Continuity and control for the company.
Any supported AI tool
One company context layer
Work leaders can see compounding
The adoption gap
Your people are using AI. Your company is not learning from it.
Individual adoption can move quickly while the organization stays fragmented: different tools, private methods, repeated setup, and no durable record of what actually works.
Every tool becomes an island.
Claude knows one thread. Codex knows another repo. A personal prompt library knows neither the company nor the team's latest decisions.
Leaders see licenses, not leverage.
Seat counts and anecdotes do not show which company workflows are improving, where adoption stalls, or what capability now exists.
Each employee rebuilds the setup.
People reconnect the same context, tools, and instructions instead of inheriting a working company system.
Governance arrives after the work.
When access, boundaries, and ownership are not built into the operating layer, policy becomes a document people work around.
The adoption model
Standardize the company layer. Let teams choose the interface.
HQ separates the durable organizational system from the models and tools that will keep changing.
- 01Executive sponsor
Create the company HQ
Establish the company boundary for shared knowledge, capabilities, projects, people, agents, and governed access.
- 02Functional leader
Start with a real workflow
Choose a high-value recurring task and define the output, owner, inputs, and review standard that prove useful adoption.
- 03Domain expert
Package the shared capability
Turn the context, method, examples, and checks behind that workflow into a company skill.
- 04Operations / IT
Connect approved systems
Give authorized people and agents the data and tools the workflow needs without distributing raw credentials.
- 05The team
Run it from different AI tools
Let teammates use supported models and interfaces while drawing from the same company capability and project context.
- 06Leaders
Scale what actually works
Use company-owned projects, skills, agent history, and completed workflows to see adoption through durable work - not private prompt surveillance.
Beyond model-by-model rollout
The model is a tool. Adoption belongs to the operating system around it.
HQ lets the company preserve the parts that should survive when a teammate, model, interface, or vendor changes.
The layerTool-by-tool adoptionWith HQ
ChoiceStandardize on one interface or accept fragmentationUse supported AI tools through one company layer
ContextRebuilt inside each tool and employee accountCompany knowledge is maintained as a shared asset
CapabilityPersonal prompts and local setupTeam-owned skills, agents, tools, and workflows
ContinuityWork ends with the chat or employeeProjects, decisions, outputs, and corrections persist
VisibilitySeats purchased, tokens used, anecdotes collectedCompany-owned AI work and capability can be reviewed
GovernancePolicies sit outside the workflowBoundaries, membership, and access are part of the system
What leaders can operationalize
Adoption becomes a portfolio of working capabilities.
The useful unit is not a prompt sent. It is a company workflow that multiple people can run, improve, and build on.
Shared skills
See which repeatable methods the company has made available to teams.
Shared projects
Keep the outputs, decisions, sources, corrections, and open loops behind important work.
Company agents
Give persistent AI teammates scoped jobs, context, access, and inspectable history.
Connected systems
Make approved tools and data useful across authorized workflows instead of local setups.
Recurring work
Schedule repeatable company tasks from the same maintained context and standards.
Training by doing
Onboard teammates into proven workflows with examples and real outputs, not generic AI theory.
What changes
AI use becomes company capability.
- 01
Let teams choose the supported model or tool that fits the work without leaving company context behind.
- 02
Give every new teammate a useful starting point built from what the organization already knows.
- 03
Make strong workflows discoverable and reusable beyond the person or function that created them.
- 04
Track adoption through company-owned projects, skills, agents, integrations, and completed work.
- 05
Keep credentials and access inside governed company boundaries as usage grows.
- 06
Improve the company layer continuously even as models, tools, and vendors change.
Your first win
Prove adoption across people and models.
The first milestone is not every employee logging in. It is two people completing the same useful company workflow from the shared system.
Start your company HQ- 1
Choose one workflow with a measurable business output.
- 2
Package its context, method, examples, and quality bar in HQ.
- 3
Invite two teammates who prefer different supported AI tools.
- 4
Have each complete a real version of the workflow.
- 5
Review the outputs and improve the shared capability once.
Useful visibility, clear boundaries
Measure company capability - not private thought.
Leaders need to know whether the organization is building leverage. Employees need a clear boundary around what the company owns and can review.
- Visibility applies to company-owned HQ projects, skills, agents, integrations, and supported work surfaces.
- HQ is not positioned as a monitor of every private prompt an employee sends to every AI tool.
- Company membership and boundaries define where shared knowledge and work belong.
- Access can be granted, scoped, and removed without rebuilding the company system.
The executive question becomes: what can our company now do repeatedly with AI that it could not do before?
Build the layer that compounds
Model freedom. Company memory. Work you can actually see compounding.
Start with one real workflow, two teammates, and a shared company capability. Expand adoption from proven work instead of another top-down tool rollout.