Cleve

Anyone who uses AI seriously for knowledge work hits the same wall, and it is not model quality. It is that every conversation begins from nothing.

You explain the project, the audience, the constraints, what you tried last time and why it did not work. Then the conversation ends, and tomorrow you do it again. The model is capable; the relationship has no memory, so you carry all the context yourself, forever.

Cleve is built around fixing that rather than around better generation. Whether accumulated context is worth committing your work to a small company's product is the real question, and it deserves a direct answer.

What Problem Does Cleve Fix?

• It remembers your context between sessions, building working memory from your ideas, drafts and documents rather than starting fresh each time.
• Skills are reusable prompt shortcuts invoked with a slash, so a prompt you refined once becomes something you run rather than retype.
• @-mentions pull in specific documents, which is how you control what the model actually sees.
• Multi-model, not single-vendor: ChatGPT, Claude and Gemini, with MCP and API connections to external tools.
• 50+ languages and team collaboration, with 40,000+ users stated and a named Malaysian legal entity.

Why Working Memory Beats A Better Prompt

Cleve describes itself as a personal AI workspace that builds a working memory of your ideas, drafts and context that you can reuse anytime.

The framing is right, and it identifies something prompt engineering cannot solve. A prompt is a few hundred words; your context is a project's history, a client's preferences, three previous drafts and the reason you rejected an approach in March.

You cannot type that each time, so in practice you type an abbreviated version — and the model produces work that is generically competent because it is working from a generic brief. The gap between good and generic output is almost always context rather than capability.

Accumulated memory changes what you are doing with the tool. Instead of briefing a stranger repeatedly, you are working with something that has read what you wrote, which is closer to how a colleague becomes useful over months.

The honest caution is that accumulated context is not automatically good context. A workspace that has absorbed three years of your work includes abandoned directions, superseded decisions and things you no longer believe — and unless you can see and prune what it is drawing on, stale context degrades output in ways that are hard to diagnose.

The comparison worth holding in mind is with a workspace such as Notion AI, where the assistant sits beside your documents but does not treat them as standing memory. The difference is whether the tool has to be told what you already wrote, or already knows.

The gap between generic output and good output is almost always context, not capability. And you cannot retype your context every morning.

Skills And Mentions Are The Control Surface

Cleve offers Skills, reusable prompt shortcuts invoked with a slash, and document references invoked with an @ mention.

Together these solve the practical problem that accumulated memory creates: if the workspace knows everything, how do you tell it what matters right now?

Mentions are the explicit answer. Rather than hoping relevant context surfaces, you name the documents that should inform this piece of work. That control is what makes a large workspace usable rather than unpredictable.

Skills address the other half. Most people who use AI regularly have three or four prompts they have refined over months and retype from memory each time, slightly differently. Saving those as invocable shortcuts turns a habit into a tool, and it makes them shareable with a team — which is how a good prompt stops being one person's private trick.

The two together give you a workspace with memory you can direct, which is more useful than either memory or prompting alone.

Why Does Multi-Model Support Matter Here?

Cleve supports ChatGPT, Claude and Gemini, and connects to external tools through MCP and an API.

Model choice matters less than it did — the frontier models are close enough that most knowledge work is served by any of them — but it still matters for two reasons.

The first is that they have genuinely different characters. Some people find one model's prose consistently better for their voice, and being locked to a vendor's choice means living with an output style you did not pick.

The second is durability. Frontier leadership has changed hands repeatedly, and a workspace welded to one provider inherits whichever bad quarter that provider has.

MCP support is the more forward-looking inclusion, and it is what stops this becoming another place your work gets stuck. It means your accumulated context can be reached by other AI tools — the site names Claude, ChatGPT, Cursor and Hermes — rather than being locked inside one application.

For a workspace product specifically, that openness matters more than for most categories. The whole value proposition is accumulation, and accumulation without portability is lock-in.

In practice this means putting a hard question in front of Claude and a routine one in front of something cheaper, without maintaining two subscriptions and two places where your context lives.

• Test the same task across the supported models; the difference in voice is larger than the difference in capability.
• Use MCP to reach your context from other tools rather than working only inside one interface.
• Save your three most-used prompts as Skills first; that is where the immediate time saving is.
• Use @-mentions explicitly rather than relying on the workspace to surface the right context.
• Prune old material periodically, since stale context degrades output invisibly.

The Question Any Workspace Product Must Answer

Cleve asks you to accumulate your thinking inside it, which is a materially bigger commitment than trying a generation tool, and the risks deserve stating plainly.

The company is Cleve Sdn. Bhd., a Malaysian registered entity with a company number published — more corporate transparency than many AI tools offer, and it can be checked in a public register. Forty thousand users is real traction for a product of this kind.

It is still a small company, and a workspace holding years of your drafts and context is not something to move casually. The questions that matter are not about features.

What export exists, and in what format? Does your accumulated context leave with you or only your documents? What happens to the workspace if the company is acquired or closes? None of these are answered on the pages we read, and for this category they are the ones to ask before committing.

The site does publish privacy, terms and a security page in its footer, which is where those answers would live. Read them before your workspace becomes something you cannot easily leave — which happens faster than people expect, because the value is precisely the accumulation.

The same question applies to anything that accumulates your work over years. Note apps like Goodnotes and documentation platforms like GitBook earn trust the same way: by making export boring and unremarkable rather than a support ticket.

Building A Workspace Worth Having

A memory-based workspace is only as good as what you put in it, and the first week determines whether it becomes useful or becomes a pile.

The bar is step four. If the output with your context is not noticeably better than the same request in a bare chat window, the accumulation is not doing its job.

1. Import documents from one real project rather than everything you have. Focused context beats comprehensive context.
2. Save your three most-used prompts as Skills immediately, since that is the fastest visible return.
3. Do a real piece of work using @-mentions to name the relevant documents explicitly.
4. Run the same request in a plain chat window with no context and compare. That difference is the entire product.
5. Try it across two of the supported models to see whether the voice difference matters for your writing.
6. Before committing more, find the export option and confirm what leaves with you — documents, context, or both.

Personal Tier, Team Tier, And The Model Question

Access is a subscription with personal and team tiers; amounts are not disclosed on the pages we read.

The team tier is the more interesting one because of what Skills become in a group. A prompt one person refined over months, saved and shared, is organisational knowledge rather than an individual habit — and most teams have no mechanism for that at all today.

Shared context has the same property and the same risk. A workspace holding a team's accumulated project knowledge is valuable, and it also means everyone inherits whatever stale material nobody pruned.

The multi-model support has a cost implication worth checking: whether model access is included in the subscription or requires your own API keys. Those are very different commercial arrangements and the site does not say.

We do not publish amounts. Establish the export path and the model-access arrangement before subscribing, since those two determine what you are actually buying.

Cleve: Features And Company

Who Gets Value From A Memory Workspace

Genuinely helpful when

• You re-explain the same project context to an AI several times a week, which is the exact problem it solves.
• You have three or four prompts you have refined and retype from memory, which Skills turn into tools.
• Your work is repeated within a consistent domain, where accumulated context compounds.
• You want to keep model choice rather than being locked to one vendor's output style.
• You work in a team where a good prompt should be shared knowledge rather than one person's trick.

Unnecessary when

• Your AI use is occasional and varied, where there is no context worth accumulating.
• You need structured team knowledge with databases and permissions, which is a different product.
• You are unwilling to commit accumulated work to a small company without a clear export path.
• You would import everything rather than curating, since stale context degrades output invisibly.
• A general assistant with project features already covers what you need.