Search internal knowledge
Find useful passages in a controlled collection of documents, with references back to the source.
Private AI on your own hardware
Your documents.
Answers you can check.
Explore private document search, local assistants, and AI workflows on hardware you control. Match the system to the job before buying more GPU than you need.
Choose the right form factor
Compare an expandable workstation with a turnkey appliance. Memory capacity, response speed, and upgradeability all matter.
Connect the workflow
Plan the model, inference software, access controls, document pipeline, and handoff together.
Find the fit
Start with the job.
Start with the task, document types, number of simultaneous users, and acceptable response time. We validate the proposed setup against a representative workload before calling it a fit.
Scope & pricing
Local AI hardware and setup are quoted separately. Some workflows may still need cloud services; we identify those connections and what information they carry before implementation.
Read a local AI reference build →Prove the fit before the full deployment.
1 · Scope a paid pilot
One document collection, one user group, and an agreed set of test questions. You receive a written recommendation to proceed, revise, or stop. Pilot price is confirmed before work begins.
2 · Deploy the validated system
Hardware, model setup, document ingestion, access controls, and training are quoted together with clear line items. Reuse suitable hardware where possible.
3 · Keep it useful
Optional Private AI Care covers the agreed systems: monitoring, document freshness, approved updates, and periodic answer-quality checks. Usage, support hours, and backup responsibilities are defined in your quote.
Work out your next step
Start with the job. Then choose the hardware.
A short fit check gives you a starting recommendation. No documents or contact details required.
Work out your next step
An answer you can check.
Explore a fictional workshop handbook. This is a preset browser demonstration, not a running AI model.
Sample handbook · section 2
Workshop delivery process
Before pickup, the technician records the installed components and completes a stability check. A walkthrough covers basic operation and the agreed support contact. Delivery dates are confirmed individually.
Fictional training material. These sentences illustrate document search and are not customer results or service terms.
Before pickup
The technician records components and completes a stability check.
Source: Workshop delivery process, section 2.
A deployed system needs access controls, source checks, and testing on your documents. This demo sends nothing to an AI service.
Clear ownership
Know what you receive.
Your quote identifies the hardware, accounts, source or workflow exports, documentation, and access included in the handoff. Third-party licenses and reusable Ahern AI components are explained separately.
After delivery
We agree acceptance checks, support hours, and how to request changes before work starts. Ongoing help is optional and scoped. Existing client agreements keep their agreed terms.
Prepare for your project →Before we start
Will every task run without the internet?
Many models can work offline after setup. Updates, external integrations, and any cloud services are separate dependencies that need to be planned.
Does local AI automatically make my data secure?
No. User permissions, device security, backups, network configuration, and the document workflow still need attention. The design covers those requirements explicitly.
What does local AI mean?
The model runs on a computer you control instead of sending every request to a hosted AI service. The system can support document search, assistants, and other workflows; its capabilities depend on the model and hardware.
Do I need an expensive GPU workstation?
Not always. A compact appliance may fit your workload; an expandable workstation offers different performance and upgrade options. Model size, simultaneous users, and response-time expectations guide the choice.
Are there ongoing costs?
Running a local model avoids per-request cloud inference charges for that model. Electricity, maintenance, backups, support, and any separately selected cloud services still need to be considered.