Oki Home vs Looski: personal AI and AI built for your firm

Oki Home and Looski share a useful starting point: people should be able to run AI on hardware they control. They turn that idea into different purchases. Oki packages personal computing into an appliance. Looski works with a firm to design, deploy, and maintain AI workflows around its people and systems.

The buying question is how much of the work you want a product to provide, and how much implementation help your organization needs.

Sources checked October 6, 2026. This is Looski’s comparison of published offerings, not a hands-on review or an independent benchmark. Features, prices, and delivery plans can change.

What you are buying

Oki Home combines personal files, search, and an action agent in a home computer. Oki Desk is described as handling browsing and multi-step tasks. Its developer environment also supports custom applications and models. It deserves consideration as more than a storage box or chat interface.

Looski offers an engagement: on-site discovery, a written proposal, a collaborative build, deployment on Apple silicon, team training, and ongoing support. The implementation scope starts with a recurring business process and the people responsible for its output.

These rows describe the published offers. They do not establish that Oki lacks a capability because its website does not document it. If you want either system to serve a team, request evidence of the access controls and support arrangements your team needs.

An appliance price and an implementation quote cover different work

Oki’s reservation page lists a $1,799 launch-batch price, including a 2 TB Memchip. The $59 refundable reservation is credited toward the purchase, leaving $1,740 due at final purchase. It lists $2,299 after the first batch, with storage upgrades costing extra. The first batch is expected in mid-December 2026; that is a plan, not a delivered-product availability claim.

Looski’s public offer calls for a proposal based on the engagement. There is no like-for-like service price on the homepage to place beside Oki’s hardware price. Figures in our cost calculator are planning assumptions, not a Looski quote.

For an individual, a packaged product can be the more proportionate purchase. For a firm, compare the full path to useful operation: hardware, setup, integration work, evaluation, training, maintenance, and the time employees spend reviewing outputs. Agree what each price includes before calculating savings.

Local AI still needs a defined data boundary

Oki’s FAQ says its model runs locally, while remote access, connected services, and optional cloud models can involve outbound information. Its privacy policy further distinguishes owned hardware from hosted or demo workspaces, and describes diagnostic configurations that can capture prompts, responses, and tool activity. Those are settings to understand when evaluating the product.

Looski’s intended customer architecture keeps inference, document processing, retrieval, histories, logs, and backups on premises, with external model fallback disabled. Optional integrations and support access are scoped separately. This is a deployment design to verify in your environment; the public website and sales systems have a separate boundary.

For either option, trace one real document through the entire workflow. Where does it enter? Where are searchable copies stored? What reaches an external tool? What appears in a diagnostic log? Who can restore a backup? A useful acceptance test follows the document all the way to its final output.

The hardware specifications do not settle the comparison

Oki advertises a 27-billion-parameter local model. Looski’s larger memory configuration is a reason to evaluate additional model and workload options, not proof of better answers.

Dedicated GPU memory and unified memory are different architectures. Dividing their capacities does not produce a speed or intelligence multiplier. Model format, context length, serving software, concurrent work, and the task itself all affect results. We have not run a matched Oki-versus-Looski benchmark and make no performance ranking here.

Choose evaluation cases before choosing a winner. Measure whether the output is usable, how long it takes, how much correction it needs, and what happens when the system cannot complete the task. A fast answer that needs extensive rework may have little operational value.

A document workflow makes the difference concrete

Consider a hypothetical firm that receives a packet of documents and needs a reviewed summary entered into its existing system. This is an illustrative evaluation case, not a claim about a deployed customer or a verified Oki integration.

The firm could investigate whether Oki’s configurable tools meet that need, then establish who will build and maintain the process. It could also ask Looski to scope that process as an engagement. Either route should be judged against the same acceptance criteria:

1. A permitted user supplies the packet, and an unrelated user cannot retrieve it.
2. The output identifies the source material supporting its claims and flags missing information.
3. A reviewer approves the result before an external action or final record update.
4. A failed integration leaves the work recoverable without creating duplicate records.
5. Someone is responsible for updating the workflow when the underlying application changes.

This is where implementation work earns its cost: connecting the model to a dependable process, with an owner and a way to recover. A packaged product may already cover enough of your process. A bespoke engagement should justify itself by closing specific gaps.

Which should you evaluate first?

Start with Oki if you want personal AI, prefer a packaged experience, and are comfortable evaluating its planned availability and configuring it around your needs. Its customization options also make it relevant to people who want to build for themselves.

Start with Looski if you want a partner to work on site with your team, implement a defined business process, establish the deployment boundary, and train the people who will use it. The proposal should name the deliverables and acceptance tests so you can judge whether the service is worth its cost.

Neither choice removes the need for human judgment. Before expanding a deployment, run a small pilot on representative work and count accepted results, corrections, and operating effort.

Bring us one recurring task, the systems it touches, and the person who approves the result. We can map what a Looski engagement would need to deliver.