What Is an AI Context Layer, and Why Should You Own It?

Your AI is only as good as what it knows about how your company works: your company standards and the past decisions and corrections your team has made along the way. If that knowledge is stored inside one model provider’s product, you lose it when you switch. Keep it in a layer you own, and any model can use it.

A company’s knowledge already exists across documents, messages, customer records, project histories, workflows, and people’s experience. Information becomes useful context when an AI system can identify what applies to the current task, determine who can use it, and understand how it should influence the result.

That context may include:

• Previous decisions and the reasoning behind them
• Examples of approved work
• Brand, quality, and communication standards
• Customer and project history
• Established workflows and responsibilities
• Corrections people have made to previous AI output
• Permissions governing access to information
• The organization’s definition of a successful result

“What’s in that layer is your memory, your understanding of what makes for a good or bad agent output,” said Superhuman Chief Product Officer Noam Lovinsky. “The knowledge that’s necessary to do a particular task, and the way you like that task done.”

Context also carries the human qualities that distinguish one organization’s work from another’s. “It’s my context, my judgment, my professional taste,” said Kevin Cannon, Product Design Director at Superhuman.

A foundation model can provide broad intelligence. Context steers that intelligence toward work that reflects the organization’s customers, decisions, standards, and point of view.

New technology creates value when the surrounding system changes

When electric motors first reached factories, some manufacturers replaced a central steam engine with a central electric motor while retaining the existing belts, line shafts, layouts, and production processes.

Larger productivity gains came as factories reorganized around the new power source. Smaller motors could power individual machines, freeing manufacturers to redesign the factory floor and reconsider how people and materials moved through production. Economic historian Paul David documented this delayed transformation in “The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox” (The American Economic Review, 1990).

“Just replacing the steam engine on the factory floors didn’t actually change the output of the factory,” Lovinsky said. “You had to rethink how the factory was configured, how work got done, what output you actually needed at each station.”

An organization that upgrades its model without improving the surrounding context faces a similar constraint. The reasoning engine becomes more capable, while the rest of the system continues to operate as before.

Redesigning the system around AI requires organizations to determine what knowledge AI can access, how it retrieves task-relevant information, and how existing permissions apply. They also need a way to capture people’s corrections, use that feedback in later interactions, and preserve the learning when a model changes.

Make context independent of the model

An organization attains movable context by keeping its knowledge, instructions, feedback, and controls independent of any single model provider. When an agent begins a task, the system retrieves the relevant context and gives the model permission to use it.

Portability comes from several connected capabilities:

1. Connect the sources of context. The context system needs controlled access to the documents, messages, customer records, project histories, and other systems where work is recorded.
2. Preserve existing permissions. Models and agents should receive only the information the person or workflow is authorized to use.
3. Organize context by scope. Company policies may apply across the organization, while customer history, team processes, personal preferences, and active project details apply to narrower sets of work.
4. Retrieve context for the task. The system identifies the information relevant to the current request instead of sending the organization’s entire knowledge base to a model.
5. Store learning outside the model. Approved examples, corrections, preferences, and evaluations remain in a system the organization controls, allowing other models to benefit from previous interactions.
6. Provide consistent access. Approved agents and models can use the same context through shared interfaces rather than requiring teams to re-create it for every provider.
7. Record where outputs came from. Teams can see which sources informed an answer, which model produced it, and how people corrected or approved the result.

“And then you give access to that context to different agents, and that’s how you make them operate well,” Lovinsky said.

Superhuman Go is an example of a context management system that works across applications and is not bound to one model provider. The knowledge built through one workflow can stay with the organization and become available to other approved agents.

“All of the skills and knowledge that you develop over time that help agents operate better, that improve the quality and the output of the agents, stays with you and builds with you over time regardless of what model provider you’re using,” he said.

Organizations can retain that control while using third-party technology. Ownership is defined by the ability to inspect, govern, update, export, and reuse the context as the organization’s AI strategy evolves.

Useful context reflects how the organization works

Work moves through documents, email, chat, calendars, customer systems, meeting transcripts, and specialized applications. Context confined to one product gives AI a partial view of the people, information, and decisions shaping the work.

“You have to be able to see the whole of what I’m working on in order to make good decisions about the things that are important to me,” said Luke Behnke, VP of Product at Superhuman.

Access should remain task-specific and governed by the organization’s existing controls. A scheduling agent may need calendar availability, active priorities, and the relationships involved in a meeting. It does not need unrestricted access to every company system.

The same principle applies across other workflows:

• A writing agent can apply approved voice and brand standards.
• An interview-feedback agent can use the role’s rubric and surface relevant notes.
• A customer agent can bring forward the history of an account or project.
• An agent created by one team can become available to other appropriate teams without requiring everyone to rebuild it.

Grammarly already shows how shared organizational context can reach people in the course of their work. “You can customize style, tone, voice, and brand, and everybody in the organization just gets it without having to click any buttons at all,” Behnke said.

One team establishes the standard, and the appropriate guidance becomes available to everyone who needs it. Employees can contribute improvements without each person having to find, explain, and configure the same context independently.

Scale depends on the system around the model

Grammarly processes over 100 billion LLM calls per week. That volume reflects thousands of small operations that occur as people write, revise, and communicate across their workday.

The usefulness of those calls depends on the system surrounding the model. The infrastructure must recognize the current task, retrieve the appropriate user or organizational context, enforce access controls, and provide the model with the right information. It then returns the result within the workflow and captures feedback to improve future assistance.

A model upgrade can improve the reasoning layer while the context system preserves the organization’s accumulated knowledge. This division allows the organization to adopt new intelligence without discarding its standards, permissions, and learning.

At Grammarly’s scale, the durable asset is the infrastructure that makes general intelligence useful across billions of model calls. The model contributes capability. The context system ensures that capability reaches the right person, at the right moment, with the information required to help.

How to audit your context layer

Before comparing another round of benchmarks, organizations can examine the context infrastructure supporting their AI:

1. Inventory the context and its sources. Identify the history, decisions, examples, standards, workflows, and corrections AI needs for important tasks. Document where that information lives, who maintains it, and how current it is.
2. Examine control. Determine whether the organization can inspect, export, update, and remove stored context.
3. Review permissions. Confirm that AI access follows existing security, privacy, and governance rules.
4. Evaluate retrieval. Test whether the system can find task-relevant information without introducing unrelated or outdated material.
5. Trace feedback. Decide what happens after someone corrects an output. Useful feedback should improve later work instead of disappearing with the session.
6. Test portability. Give another approved model or agent access to the same context and evaluate whether the team has to reconstruct its instructions and standards.

This audit can help teams determine whether disappointing output stems from the model, missing context, outdated information, weak retrieval, or a permission problem. It can also reveal whether a model change would improve the system or erase knowledge the organization has already accumulated.

“When it comes to context and your context management system, that’s something that customers can never rent from others,” Lovinsky said. “They must own that layer.”

Organizations that control their context can select the best model for each task and adopt new capabilities without starting over. Each approved example, correction, and decision strengthens a shared asset that keeps improving, no matter which model uses it next.

Learn more about how Superhuman is approaching context, agents, and connected work.