Local AI Workbench First-Run Checklist
Safe File Handling for Small Teams
Small teams want to use AI for document analysis, report drafting, and data processing — but they also handle client files, internal documents, and sensitive business data. This checklist helps you safely set up a local AI workbench, classify files before processing, manage model keys, and decide when to stay local versus move to a hosted trial.
How can small teams safely use a local AI workbench for file processing?
The safe starting point is file classification first, processing second. Before any AI tool touches a file, classify it into one of four tiers (public, internal, confidential, regulated). Use customer-managed model keys to control costs and data exposure. Choose local install for any file above public classification. Surinch inchWorker provides a local-first workspace where documents, images, and tables are processed entirely on the user's machine — files never leave local storage, and model keys remain under user control.
File Classification Guide
Classify every file before processing. The classification determines where and how AI can be used.
Public
Marketing materials, public reports, open-source documentation. Safe for hosted AI services and online trials.
Examples: Blog posts, product pages, public PDFs, open datasets
Internal
Team documents, meeting notes, internal process guides. Use locally or with private deployment. Hosted OK only if non-sensitive.
Examples: Internal wikis, team procedures, draft reports without client data
Confidential
Client data, financials, contracts, source code, business strategy. Local processing only. Never use hosted services.
Examples: Client reports, financial spreadsheets, NDAs, proprietary algorithms
Regulated
PII, PHI, GDPR-protected data, national security data. Local or private deployment only. Requires additional compliance review.
Examples: Personal data, health records, payment information, legal case files
Model Key Management
Your model keys are your cost and data boundary. Manage them carefully.
Use Your Own Keys
Always bring your own model provider keys (OpenAI, DeepSeek, etc.). Do not share keys across team members. Each user should have their own key with usage limits.
Set Spending Limits
Configure monthly spending caps at the provider level before connecting keys. Monitor usage weekly during the first month.
Separate Keys by Use Case
Use different keys for development/testing versus production work. This prevents a test loop from consuming production budget.
Never Embed Keys in Files
Store keys in environment variables or secure key managers. Never hardcode keys in scripts, config files, or shared documents.
Online Trial vs Local Install: Decision Tree
Are you just learning how the tool works?
Yes -> Use the online trial with official sample data. No -> Continue.
Do your files contain any internal business data?
Yes -> Install locally. No -> Continue.
Do your files contain client or third-party data?
Yes -> Install locally. Online is not appropriate. No -> Continue.
Do your files contain PII, financials, or regulated data?
Yes -> Local install only. Consider private deployment. No -> Online trial or local install, either is acceptable.
Summary: If any file is classified as confidential or regulated, install locally. The online trial is for learning with sample data, not for processing business files.
Sensitive File Handling Boundaries
Do
- Classify files before processing
- Use local install for confidential data
- Bring your own model keys
- Review AI output before sharing
- Keep audit logs of processed files
Do Not
- Upload client files to hosted AI
- Share model keys across the team
- Process regulated data without review
- Assume AI output is ready to publish
- Skip file classification because it is convenient
Important Boundaries
- Local-first means no cloud features. When running locally (inchWorker), cloud-based collaboration, shared knowledge bases, and remote model routing are not available. This is a security feature, not a limitation.
- Not for regulated data without private deployment. Local install on a laptop is not equivalent to a private-deployed, audited environment. Regulated industries may need additional infrastructure.
- Model keys = cost boundary. You control spending through your provider. inchWorker does not add middleware charges for local processing, but your model provider bills apply.
- You own the output. AI-generated content from local processing is your responsibility to review, validate, and approve before use in business decisions.
Frequently Asked Questions
Can I switch from online trial to local install later?
Yes. The online trial is for learning the tool. When you are ready to process your own files, download and install inchWorker locally. Your learning carries over; your sample data from the trial stays in the trial environment.
When should a small team move from inchWorker to InchStack?
Move to InchStack when you need multi-user workflows, shared governance rules, formal approval chains, delivery receipts, or audit evidence that spans multiple people. inchWorker is for individual or small-team local work; InchStack adds the control plane for team governance.
Does local install mean no internet connection is needed?
Local processing means files and documents stay on your machine. However, AI model API calls (to OpenAI, DeepSeek, etc.) still require internet unless you are using a fully local model. The key distinction is that your files are not uploaded to Surinch servers.
What about team collaboration on the same files?
inchWorker is primarily a single-user local workspace. For team collaboration with permission controls, audit trails, and shared governance, InchStack provides the multi-user control plane. You can start with inchWorker and migrate workflows to InchStack as the team grows.
Start with a local-first AI workspace
inchWorker keeps your files local. Your data, your machine, your keys.