Memory fragments by person and provider.
A useful explanation in one employee’s AI history does not automatically become available in another employee’s work.
Own your company’s AI memory.
Take it with you.
Capture the knowledge, decisions, and preferences your employees develop with AI. Keep them as a shared, governable asset your company owns. Switch AI providers tomorrow. Your memory stays with you.
Omnishi Memory. A durable, portable memory layer for large organizations.
Omnishi Memory
Your company’s memory
01Decisions & reasoning
02Knowledge & preferences
03Sources & access rules
Your organization.
Your permissions.
Your permissions.
Same memory. Now with ChatGPT.
Switch the AI tool. The memory stays.
Interactive illustration · Coverage defined at deployment.
“Companies need to turn their workflows, domain knowledge, and accumulated judgment into AI systems that improve with each use. . . . This loop becomes the new IP of the firm . . . That is how companies drive value for themselves and the broader economy.”
Your company’s thinking is happening inside individual AI conversations.
An employee works through a problem with AI, forms a judgment, makes a decision, and implements it. A colleague does the same in another conversation, sometimes in another AI system.
As employees do more of their thinking with AI, more company knowledge develops without ever passing through a conversation with another employee.
Each person’s AI accumulates context. The organization needs a way to own that context, connect it, and decide how it should influence everyone else’s work.
Company direction arrives unevenly.
Leadership makes a decision. Employees’ AI chats may continue reasoning from earlier assumptions.
Changing tools can mean rebuilding context.
The company needs its accumulated knowledge to survive whichever AI system it chooses next.
Your AI systems have context.
Your company needs continuity.
ChatGPT, Claude, Microsoft Copilot, Google Gemini: employees build histories, saved context, and working preferences inside the AI systems they use. The form and scope of that memory vary. The company still needs to retain and govern its knowledge across them.
What you haveWhat it gives youWhat still needs to happen
Documents & wikis
A place to publish and maintain what the company knows.
Someone has to recognize the useful finding in a conversation, write it up, and keep it current.
Enterprise search
A way to retrieve information from connected company sources.
The finding has to reach an indexed source. The next person has to know enough to look for it.
AI chat & project memory
Context that helps a person or project continue working within an AI system.
The organization needs a durable memory it owns, can share under its rules, and can take to another provider.
AI usage monitoring
Visibility into where AI is being used and what information passes through it.
The organization still needs to turn that activity into knowledge people can use in their work.
Change your AI provider.
Keep what your company learned.
The memory should be an asset you can bring with you.
Your employees supplied the context, made the judgments, and developed the knowledge. That investment needs to survive a change of model, application, or vendor.
It also needs to be available to the right people while they work—with company decisions and sharing permissions attached.
Build a durable memory
your company owns.
Omnishi stores the knowledge created in approved AI work as a shared, governable asset. Individual thinking can contribute to company memory. Company decisions can inform individual AI conversations.
The memory persists independently of the AI provider. You can keep using it, share it under your rules, and take it with you when your tools change.
Capture the context.
The on-device companion reads AI conversations from the applications included in your deployment.
Retain what you learn.
Store decisions, judgments, preferences, and reasoning in company-owned memory, with sources and ownership attached.
Govern its use.
Set sharing permissions. Publish leadership decisions. Resolve gaps and contradictions with the responsible people.
Take it forward.
Bring permitted memory into the next employee’s work or your next AI provider through supported integrations.
Start with a team, the AI tools it uses, and the knowledge that should reach other teams.
Request a Memory deploymentRetain the thinking.
Control where it goes.
Durability, portability, sharing, and governance belong in the same memory layer.
Capture AI conversations.
Collect context from the approved prompts and responses in your deployment. Define which applications, employees, and work are in scope.
Build durable company memory.
Retain the decisions, judgments, preferences, and explanations produced in AI work. Keep useful thinking available beyond its original conversation.
Carry memory between providers.
Keep your accumulated knowledge independently of the AI system that helped create it. Take the memory with you and use it through supported integrations.
Share thinking across AI chats.
Admins can enable employees to benefit from each other’s reasoning and findings across their AI conversations, within defined access boundaries.
Direct AI chats to leadership decisions.
Senior leadership can publish decisions and direct relevant employee AI chats to reference them. Keep the decision, its authority, and its current version attached.
Detect and help resolve knowledge gaps.
Surface missing context, isolated findings, outdated guidance, and conflicting assumptions. Help the responsible people supply, correct, approve, or share what is needed.
Trace a memory to its source.
Inspect the conversation behind a finding, who contributed it, and its approval status. Keep the reasoning available alongside the conclusion.
Control how knowledge is shared.
Separate personal context, team knowledge, and company guidance. Set access boundaries and route sharing requests to the responsible owner.
Find the knowledge gaps.
Help the company close them.
A gap can mean nobody recorded the answer. It can also mean the answer exists in another person’s AI chat, two teams disagree, or a decision changed without reaching the people using it.
Missing
A recurring question has no reliable answer.
Ask the responsible person. Turn the answer into reusable memory.
Isolated
One employee has worked it out. Others cannot benefit from that thinking.
Identify the connection and help the owner share it with the right people.
Conflicting
Two AI conversations contain incompatible assumptions about the same thing.
Bring the sources together and route the disagreement for resolution.
Outdated
Work is still using a definition, preference, or policy that has changed.
Surface the current version and the earlier assumption it replaces.
Undistributed
Leadership has made a decision that relevant AI chats have not referenced.
Connect the decision to the employees and work it should inform.
Disconnected
A team changes AI tools and loses access to the context it accumulated.
Carry the retained memory into the new environment through a supported integration.
Start with the knowledge gaps your teams already encounter.
Discuss a Memory deploymentYour memory.
Your rules for using it.
Decide which conversations contribute to memory, which employees can share thinking, and who has authority to publish company decisions.
Senior leadership directs the guidance. Admins configure access. Employees benefit from permitted knowledge across their AI chats. Personal thinking, team findings, and official decisions retain their different scopes and authority.
Knowledge boundariesExample
Personal working context
Kept within its approved scopeDrafts, assumptions, unfinished thinking
Team knowledge
Available to authorized colleaguesFindings approved for a defined group
Company guidance
Published by the responsible ownerOfficial definitions and decisions
Define capture boundaries.
Specify the tools, workspaces, and employee groups included. Make the scope clear to the people whose AI work contributes to memory.
Set sharing and review rules.
Define who can access a source, approve a memory, correct it, and publish company guidance. Retrieval must respect those boundaries.
Agree where data is processed.
Review on-device processing, storage, retention, model providers, and infrastructure requirements as part of the deployment scope.
Bring your security and IT teams into the deployment discussion.
Discuss your requirementsKeep working in your AI tools.
Keep the memory when you change them.
The on-device companion captures approved AI work in the background. Employees continue in the tools included in your deployment, while company memory accumulates independently.
Admins enable the appropriate sharing. Leadership publishes decisions for relevant chats to reference. Employees receive permitted context with its source attached.
IT sets the scope.
Choose an initial team, deploy the companion, and configure the supported applications and access rules.
Employees continue their work.
Have the conversation, investigate the problem, and make the decision. Capture does not depend on writing a separate wiki entry.
The next conversation gets the context.
Review relevant company memory with its source attached. Apply it to the work and flag anything that needs correcting.
Application coverage and how context enters each AI tool are defined during deployment. We start with the tools your team actually uses.
Use it where the same knowledge
needs to reach different people.
A few examples of the work a shared memory layer can support.
Switching AI providers
A department adopts a different AI system after months of developing context in its previous one.
Bring the retained company memory with it, including decisions, reasoning, and working preferences.
Leadership direction
A senior leader changes a company priority. Employees are independently making plans in their AI chats.
Direct the relevant chats to reference that decision, so the new priority informs the work.
Shared employee thinking
Two colleagues are exploring related problems in different AI systems without knowing what the other has learned.
With admin-enabled sharing, each can benefit from the other’s permitted findings and reasoning.
Customer handovers
Sales explains a customer’s requirements in an AI conversation. The implementation team later needs the same context.
Bring approved customer context into the handover, including the reasons behind the commitments.
Repeated investigations
One engineering team diagnoses a recurring issue. Another encounters it in a different project.
Connect the new investigation to the earlier finding and the conditions under which it applied.
Company decisions
Finance changes a revenue definition. Sales Operations is still using the previous definition in a forecast.
Surface the current guidance and the difference that needs resolving.
New employees
A new colleague has access to the documentation but lacks the accumulated explanations behind it.
Make permitted team knowledge available as they work through their first questions.
Policy changes
A process is updated. AI projects across several departments still contain instructions based on the old process.
Connect affected work to the new policy and its owner, so the change can be applied.
Company-owned memory.
$5 per user, per month.
Bring your AI tools, your deployment requirements, and your rules for sharing knowledge. We’ll define where your first deployment starts.
Request a Memory deploymentMake your company’s AI memory
an asset you own.
Start with the AI tools your people use, the memory you want to retain, and the rules for sharing it. Build a first deployment your company can keep building on—even when its AI providers change.