Go AI Native

10x your team’s productivity. Tackle the exciting work, automate the boring stuff, on your premises.

Go AI Native See how we work
looski · on-site workflowsLocal model

AI paralegal

Discovery chronology

Matter 24-118 · 412 files arrived

  1. Read discovery files3,106 pages
  2. Extract dated events1,284 events
  3. Check against depositions6 conflicts
  4. Draft the chronologyEvery entry cited

Chronology — draft

  • 03 Mar 2022Supply agreement signedDEF-0042
  • 19 Jul 2022First late-delivery noticePL-0318
  • 02 Sep 2022Termination letter sentDEF-0107
  • 14 Nov 2022Delivery log conflictsReview
Waiting for review: Supervising attorney

Not a machine in a box. A firm that runs on AI.

Becoming AI-native is not chatting with or buying an AI model. It’s knowing which parts of each job need your people’s judgment and automating the rest. We do that with you, on site. Your team focuses on work AI can never take, like using intuition to solve problems or building trust with clients.

How we work

Three phases. Making you AI-native.

We start with how your people use their computers today. We finish when they run the new system on their own.

  1. 01

    DiscoveryWe come to you.

    • We sit with your team and learn how each role uses its computer: the tools, the files, the hand-offs.
    • We separate the work that needs their judgment from the busywork AI should take on.
    • You get a written proposal showing where on-site AI fits into the workflows you already run.
  2. 02

    HackathonWe build it with you.

    • Once you approve the proposal, we turn its specs into working workflows.
    • We package the harnesses, skills, and software into one system your whole team can use, sometimes replacing tools you no longer need.
    • We present the system to your leadership and make the final adjustments with them.
  3. 03

    DeploymentWe make your team fluent.

    • Your device arrives and we install the system on your premises.
    • We record training videos on your own workflows, so new hires learn it the way your team works.
    • We train your people hands-on until they run it without us.

After launch we stay with you: we keep the system updated and refine the workflows as your work changes.

What it does

Built for the work on your desk.

Documents prepared. Records updated. Workflows carried through to completion.

  1. 01

    Sales & marketing

    Research an account, prepare a tailored proposal, and update the CRM. Queue outreach for your team’s approval.

  2. 02

    Your domain. Your expertise.

    Turn case files, intake forms, or policy documents into completed work products, with source references for review.

  3. 03

    Computer use

    Work through approved applications: enter data, move files, and complete multi-step tasks using scoped computer access.

  4. 04

    Data analytics

    Clean a spreadsheet, run the analysis, and produce a report. Deliver the working file alongside the findings.

  5. 05

    Back-office work

    Read incoming documents, extract the fields, reconcile records, and route exceptions to the right person.

  6. 06

    Everyday automation

    Run a configured workflow when a document arrives or a schedule triggers. Record what happened and escalate anything that needs judgment.

Different work. The same control.

Start with a workflow your team knows. Build confidence on your own documents.

Your AI paralegal.

More time for the case.

Turn matter files into useful first drafts and cited research, within the permissions your firm sets.

  • Build a chronology from discovery documents
  • Compare clauses against your preferred language
  • Draft a client update from case notes
Build your local AI workflow
Example workflowLocal + human review

Discovery files → reviewed case chronology

Discovery files → reviewed case chronologyFiles arrive, then the AI steps read and organize and checks: every event sourced? If the check fails, a person handles "fill evidence gaps" and processing retries. If it passes, the AI prepares "draft chronology", a person gives attorney approval, and approved work goes to "save to matter". Requested changes send the draft back for revision.Trigger → processing → checkDeliverable ← approval ← draftTrigger01Files arriveApproved matter folderAI02Read and organizeExtract dates and source pagesCheck03Every event sourced?Date and page for each eventHuman04Fill evidence gapsAttorney supplies the sourcesAI05Draft chronologyCited document for reviewHuman06Attorney approvalVerify events and citationsDone07Save to matterApproved file in matter folderPassFailRetryApprovedChanges requested → revise

Built for human review. High-stakes decisions and external actions stay under your team’s control.

Where it runs

Remarkable silicon. Built with privacy and security in mind.

Your advantage is yours to keep. The system runs inside your office, so the data others would pay millions for stays out of reach, ours included.

Illustration of a compact Mac Studio on an ordinary office desk beside a monitor and keyboard, plugged into standard wall outlets.

The machine

An office, not a data center.

A compact enclosure on standard AC power, with no server rack. Its large shared memory pool makes room for capable open-weight models; we validate fit, speed, and quality on your work.

It stays a capable workstation alongside inference. Redeploy or resell it when your needs change.

Illustration of an office workstation, files, and network gear sealed inside a glass case.

The office

Your walls. Your rules.

Your documents, prompts, and answers can stay inside your network. We design the full workflow around that promise, not just the model.

  • Local models, retrieval, logs, and backups
  • Access aligned with your existing permissions
  • Explicit control over updates and support access
Explore the security approach

Your infrastructure. Your terms.

Choose where intelligence runs, who can use it, and how you pay for it.

Explore performance & costs
Typical cloud API deployment compared with a Looski local deployment
What mattersCloud model APIsWith Looski on site
Data boundaryRequests sent to a providerLocal inference within your network
Compute costsTypically metered by usageOwned hardware + agreed support
Model choiceProvider’s available modelsCompatible open-weight models
Change controlProvider manages the serviceYou approve model and system updates
ComplianceReview provider and data-flow controlsValidate controls in your environment

Local capacity is finite. Hardware, energy, maintenance, and support still have costs. Compliance obligations remain with your organization.

Looskis · Open source

Distribute intelligence. In the open.

Looskis 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.

github.com/looskis

scribeski

Social work

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.

Download for MacRead the launch post

blueski

iMessage

Send and receive Messages through a loopback API and CLI. AppleScript only: no injected libraries or private frameworks.

brew install looskis/tap/blueski

taski

Reminders

Turn a private iCloud Reminders list into a durable task inbox for agents, using public EventKit APIs.

brew install looskis/tap/taski

gridski

Excel

An 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/gridski

moolaski

Agent skills

Financial modeling skills for AI agents: real estate, project finance, investment banking, and corporate finance, built in Excel.

npx skills add looskis/moolaski

greenski

WhatsApp

A linked-device WhatsApp daemon with the same loopback API and CLI shape as blueski. Built on an unofficial protocol.

brew install looskis/tap/greenski

More on the way.

Models, device management, and anything in between.

Follow on GitHub

A little more clarity.

All insights

A few good questions.

What happens in the hackathon?

Once 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.

Can the AI take actions in our systems?

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.

Does our data leave the office?

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.

Can we use our existing tools?

We start with your workflows and evaluate local integrations. Computer-use permissions, external services, and automated actions are scoped with your team before activation.

Does on-premises mean we are automatically compliant?

No. Local hosting supports control over sensitive information, but it does not replace your organization’s legal obligations, risk assessment, or compliance program.

How fast can we get started?

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.

What would you take off your desk?

Bring us one recurring task. We’ll come on site and map the steps, tools, and approvals.

Go AI Native