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For institutions

Built for data that must stay inside

Universities, hospitals and statistics offices get SPSS-class analysis and an AI orchestrator without sending a single record to someone else's cloud.

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Who it's for

One workbench, five kinds of obligation

Each field answers to someone different: a supervisor, a reviewer, an ethics board, a minister or a client. OrcStat is built for all of them.

Students and theses

Guided test selection, plain-language help with a four-part structure for every procedure, and APA 7 output that drops straight into a thesis.

A typical thesis: open the supervisor's .sav file with its labels intact, let the Statistics Coach suggest the test, check its assumptions, then paste the APA 7 table and results paragraph into Word. The syntax journal keeps every step for the methods chapter.

  • Statistics Coach and case studies
  • APA 7 tables and figures
  • Student discounts, with course groups coming
More for students and theses

Academic research

Mixed models, repeated measures, SEM, mediation and reliability, with a syntax journal that makes every analysis reproducible for reviewers.

Reviewers can re-run everything: the journal turns each dialog and approved AI step into syntax that reproduces the output. Multiple imputation pools results across imputed datasets, and Bayesian procedures report Bayes factors alongside p values.

  • Reproducible syntax journal
  • SEM, IRT, mixed models
  • Bayesian and meta-analysis
More for academic research

Health and clinical

Survival analysis, ROC, Bland-Altman and complex survey data on patient-level records that never leave the hospital network.

Patient-level data stays on hospital machines, in AES-256 encrypted projects, with a Local Only AI pointed at an on-premises model server. Kaplan–Meier curves, Cox models and ROC analyses come with their assumption checks and APA-style output.

  • Kaplan–Meier and Cox
  • ROC, Bland-Altman, agreement
  • Local Only AI and encryption
More for health and clinical

Government statistics

Complex samples, custom tables and large datasets with audit trails, offline operation and admin-enforced policy.

Complex Samples gives design-correct estimates and standard errors from weights, strata and clusters. Headless jobs run on a schedule with launchd or cron, and one admin policy keeps every install's AI settings the same.

  • Complex Samples with design effects
  • Large datasets on a laptop
  • Offline, audited, policy-controlled
More for government statistics

Market research

Custom tables with significance letters, multiple response sets, weighting, conjoint and AI-assisted coding of open-ended answers.

Custom tables with significance letters export straight to Word or Excel. For open-ended answers, the AI suggests codes and themes for you to review, so a verbatim file becomes variables you can tabulate.

  • CTABLES and multiple response
  • Conjoint and segmentation
  • Open-ended text coding
More for market research

Privacy and security

Controls your governance team will ask about

Every control works offline, is set by policy, and leaves an audit trail.

Security and data handling

Local Only egress guard

AI requests are pinned to approved hosts. DNS, redirects, IPv6 and IPv4-mapped addresses are re-checked, and blocked attempts are logged.

Exposure levels 0–3

Projects choose how much an AI model may see, from variable metadata only to full data, with a preview of every request.

Encryption at rest

AES-256-GCM project files with scrypt-derived keys, encrypted autosave and recovery, and SPSS-compatible password-protected .sav.

Secrets in the keychain

API keys and passwords are stored by the operating system, never in project files or logs. Passwords are redacted from the journal.

Audit log

Each AI request records what was shared and what was approved. Export it for governance, or purge it.

Signed extensions

Extension packages are hash-checked and Ed25519-signed. An admin policy can refuse unsigned packages entirely.

Local Only · egress log
14:09:12ollama.lab.internal:11434 — allowed, private network
14:09:40llm.hospital.local — allowed, admin policy
14:10:03api.example-cloud.com — blocked, Local Only

Deployment

Fits the way your IT team works

A single admin policy file and offline operation. A licence check sends only the licence ID, a hashed device ID, the app version and the operating system, never your data. Silent installs and permanent offline activation for air-gapped machines are coming.

Ask about deployment

Desktop app

For macOS 13 or later, on Apple Silicon and Intel. Windows, Linux and a Mac App Store build are coming.

Browser version

The engine runs in the browser through WebAssembly after users sign in, for datasets of up to a few hundred thousand rows. Nothing is uploaded, which suits locked-down teaching labs. SAS and Stata files open in the desktop app.

Model server

Point every seat at a shared on-premises model server, or let each user run a local model.

Batch and scheduling

Headless job runs from the command line, with launchd or cron schedules for production reporting.

/Library/Application Support/OrcStat/policy.json
{
  "local_only": true,
  "allowed_cidrs": ["10.20.0.0/16"],
  "locked_endpoints": ["http://llm.internal:8000/v1"],
  "allow_cloud": false,
  "max_cloud_exposure_level": 1,
  "disabled_features": ["text_analysis"],
  "ai_permissions": { "preset": "ask", "locked": true }
}

Admin policy

Set it once, enforce it everywhere

Keep AI on your own network, lock the model endpoint, decide whether cloud models are allowed and how much they may see, switch off AI features you don't want, and set what the AI may do without asking. The policy always wins over user settings, and a file that can't be read turns AI off.

Pilot programme

Plan a pilot with your team

We're working with a small number of universities and research groups during the beta.

Talk to us See pricing