We come on site and learn how your team really works.
Then we build the AI workflows that take the busywork off their desks,
install them on your premises, and train everyone to use them.
Becoming AI-native is not buying a model. It is knowing which parts of each job need your people’s judgment and which are busywork, then building AI into the busywork. We do that with you, on site. The result is a system your team hands an outcome to: prepare the monthly report, process a batch of invoices, or assemble a client file.
Define the finished work, the source material, and the systems it can access. Start it with a request, a schedule, or a configured event.
Read the inputs, use approved tools, and create the deliverable. Each workflow has defined permissions, checks, and stopping points.
Get the completed file or proposed update with an activity record. Sensitive actions wait for approval; exceptions go to your team.
We start with how your people use their computers today.
We finish when they run the new system on their own.
After launch we stay with you: we keep the system updated and refine the workflows as your work changes.
Documents prepared. Records updated.
Workflows carried through to completion.
Research an account, prepare a tailored proposal, and update the CRM. Queue outreach for your team’s approval.
Turn case files, intake forms, or policy documents into completed work products, with source references for review.
Work through approved applications: enter data, move files, and complete multi-step tasks using scoped computer access.
Clean a spreadsheet, run the analysis, and produce a report. Deliver the working file alongside the findings.
Read incoming documents, extract the fields, reconcile records, and route exceptions to the right person.
Run a configured workflow when a document arrives or a schedule triggers. Record what happened and escalate anything that needs judgment.
Built for human review. High-stakes decisions and external actions stay under your team’s control.
Start with a workflow your team knows.
Build confidence on your own documents.
Turn matter files into useful first drafts and cited research, within the permissions your firm sets.
Your documents, prompts, and answers can stay inside your network. We design the full workflow around that promise—not just the model.
Choose where intelligence runs, who can use it, and how you pay for it.
Explore performance & costs| What matters | Cloud model APIs | With Looski on site |
|---|---|---|
| Data boundary | Requests sent to a provider | Local inference within your network |
| Compute costs | Typically metered by usage | Owned hardware + agreed support |
| Model choice | Provider’s available models | Compatible open-weight models |
| Change control | Provider manages the service | You approve model and system updates |
| Compliance | Review provider and data-flow controls | Validate controls in your environment |
Local capacity is finite. Hardware, energy, maintenance, and support still have costs. Compliance obligations remain with your organization.
Your system runs on a Mac Studio inside your office. We chose it with care, but the value is in what we build on it with your team.
Secure boot, the Secure Enclave, and encrypted storage form a strong base for your deployment.
A large shared memory pool makes room for capable open-weight models. We validate fit, speed, and quality on your work.
A compact enclosure and standard AC power. No server rack required; capacity and cooling are assessed on site.
A capable workstation alongside inference, with resource planning. Redeploy or resell the hardware when your needs change.
Compare the total cost of becoming AI-native with the cloud tools your team would otherwise use. Discovery, the build, hardware, training, and support—all in the conversation.
A scope built around your team and workloads.
Cloud subscriptions, API usage, concurrency, and local operating costs tell different stories. Our comparison framework makes the assumptions visible.
Explore the comparisonLooskis is our open-source collection of tools for Apple silicon. Models, device management, messaging, and everything in between: small, local pieces that put capable AI on the Macs people already use.
Visit notes for social workers, written on the Mac. Transcribes Zoom and Google Meet sessions on the device, drafts the note with a local model, and fills the EHR form in Safari for review. No audio is kept by default, and nothing about a session leaves the Mac.
Send and receive Messages through a loopback API and CLI. AppleScript only: no injected libraries or private frameworks.
brew install looskis/tap/blueskiTurn a private iCloud Reminders list into a durable task inbox for agents, using public EventKit APIs.
brew install looskis/tap/taskiAn MCP server that lets AI assistants read and edit the workbooks open in Excel for Mac, unsaved edits and live formulas included.
cargo install --git https://github.com/looskis/gridskiFinancial modeling skills for AI agents: real estate, project finance, investment banking, and corporate finance, built in Excel.
npx skills add looskis/moolaskiA linked-device WhatsApp daemon with the same loopback API and CLI shape as blueski. Built on an unofficial protocol.
brew install looskis/tap/greenskiModels, device management, and anything in between.
Follow on GitHubScribeski is a free, open-source Mac app that transcribes a social worker's remote sessions on the device, drafts the note with a local model, and fills the EHR form for review. Nothing about the session leaves the Mac.
Read the storyEvery AI system that feels fast keeps a cache of your prompts in another form. Providers wall customers off from each other. Inside your own firm, the walls are a setting you have to choose.
Read the storyRegulators have told banks that generative AI is outside model-risk guidance and left the controls to them. Here is the evidence I think a firm should be able to produce.
Read the storyOnce you approve the discovery proposal, we build. We turn its specs into working workflows with your team, package them into one system, and present it to your leadership. Their feedback shapes the final adjustments before anything is deployed.
Yes, within workflows we configure and validate with your team. Those can use integrations or scoped computer access to prepare files, enter information, and update records. You decide which actions can run automatically, which require approval, and when the workflow must stop for a human. Available actions depend on your tools and deployment scope.
The customer deployment is designed to keep inference, retrieval, documents, and operational data on site. We verify that boundary with your team. Optional external tools require explicit approval. This public website and its sales channels use separate services.
We start with your workflows and evaluate local integrations. Computer-use permissions, external services, and automated actions are scoped with your team before activation.
No. Local hosting supports control over sensitive information, but it does not replace your organization’s legal obligations, risk assessment, or compliance program.
We begin with discovery: we come on site, map how your team works, and write a proposal. Hardware availability, integration scope, and your security review then set the timeline for the hackathon and deployment.
Bring us one recurring task. We’ll come on site and map the steps, tools, and approvals.
Start with discovery