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There is no universally best private AI assistant for a strict no-cloud business. The right choice is the one whose complete data path you can verify: prompts, uploaded files, document indexes, embeddings, model inference, logs, telemetry, connectors, backups, and update checks must stay within an approved boundary. For a small team, a local desktop assistant may be sufficient. A technical team may prefer self-hosted software with a local model runtime. Regulated or large organizations usually need an on-premises or air-gapped platform with identity controls, permissions, audit records, and an operating process for offline updates.
The products below represent those three approaches. Their deployment and privacy features are vendor-described capabilities, not independent security audits. Treat each as a candidate for a controlled pilot, then have IT and security staff verify the actual network connections and configuration before processing sensitive information.
What does “private AI” mean when a business cannot send data to the cloud?
A private assistant is suitable for a no-cloud policy only when every service that handles the business data runs inside an approved environment. Hosting the chat screen on a company server is not enough if a connector sends documents to an external API, an embedding service is cloud-based, or diagnostic logs leave the network.
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- Prompt and response inference: Identify the model server that receives text, images, audio, or code.
- File processing: Check where parsers, OCR, transcription, conversion, and temporary files run.
- Indexes and embeddings: Verify the vector database and embedding model, not just the chat model.
- Logs and telemetry: Determine whether prompts, file names, usage data, crash reports, or device identifiers are collected.
- Connectors and actions: Map email, storage, ticketing, databases, and browser tools to their network destinations.
- Backups and retention: Confirm where conversation history, indexes, and uploaded files are copied and how they are deleted.
- Updates and licensing: Plan how software, models, certificates, and security fixes reach a disconnected environment.
“Local,” “self-hosted,” “on-premises,” and “air-gapped” describe different arrangements. None automatically proves that a configured deployment makes no external connection.
Which deployment model is best for a no-cloud business?
Choose the simplest model that meets your data, governance, and workload requirements. Local desktop tools minimize infrastructure, self-hosted systems provide more control, and enterprise platforms add governance and support at the cost of greater operational complexity.
| Deployment model | Examples identified | Capabilities described by the vendor or project | What you must verify |
|---|---|---|---|
| Local desktop assistant | LocalMind; Pia | Local models, document assistance, and offline use are described. LocalMind says it works offline after the initial model download; Pia describes local operation with optional synchronization. | Feature-by-feature offline behavior, synchronization settings, telemetry, update checks, and local storage protection. |
| Self-hosted assistant or local runtime | Open Assistant; Kiln with Ollama, vLLM, or llama.cpp | Deployment on infrastructure you control and local inference are described. Kiln documents optional cloud-assisted features and desktop analytics. | Configured model provider, connectors, auxiliary services, analytics, and all outbound network traffic. |
| Enterprise private AI platform | VDF AI; Fosnie; Lokus | Private retrieval, on-premises or air-gapped modes, governance, audit features, and local document workflows are described in different combinations. | Architecture diagrams, deployment scope, identity integration, contractual commitments, support model, and evidence that the delivered build matches the proposal. |
Which local desktop assistants should a small business consider?
Desktop software can be the fastest way to keep routine document work on a workstation, but it places responsibility for device security, model files, access control, and backups on the business.
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LocalMind describes local document indexing, answers with citations, editable assistants, an audit log, and offline operation after the model has been downloaded. It also describes manually transferring update packages into a closed network. Its published memory recommendations are product-specific: 6 GB, 8 GB, and 16 GB RAM across its tiers, with no separate graphics card required for those tiers.
Those figures are not a general minimum for local AI. Actual needs vary with model size, context length, number of simultaneous users, document volume, and whether indexing runs alongside inference. Confirm where its index, temporary files, audit records, and update checks are stored before deployment.
Pia
Pia is described as a Windows assistant that supports local models and optional synchronization to the vendor’s server or to a server operated by the customer. A business with a hard no-cloud rule should identify which functions require a server and ensure synchronization is disabled or directed only to an approved internal endpoint.
Ask for a network-flow list covering sign-in, model downloads, crash reporting, update checks, synchronization, and any integrations. Test the application with outbound connections blocked, then confirm that the required features still work.
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Self-hosting is appropriate when your team can operate model servers, storage, identity, patching, and monitoring. It gives you control over the execution environment, but it does not make a system private by itself; the model provider and connectors selected in configuration determine where data travels.
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Open Assistant
Open Assistant presents itself as self-hosted and offers local inference options, while also referencing remote model providers. For a strict policy, configure it exclusively with an approved local model service, local embeddings, and internal connectors. Remove or block remote-provider settings rather than relying on users to avoid them.
Review the source and deployment documentation for every plugin or connector you enable. A locally hosted interface can still send a file to a remote search, storage, or model endpoint if that integration is selected.
Kiln with Ollama, vLLM, or llama.cpp
Kiln documents a local workflow that can use Ollama, vLLM, llama.cpp, or an OpenAI-compatible local server. It also discloses optional cloud-assisted features and desktop UI analytics, while stating that dataset content and model input/output are not included in that analytics.
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In a closed environment, disable cloud-assisted functions, inspect analytics settings, and verify the claim against observed network behavior. Permit connections only to the local model server, internal storage, and approved identity or monitoring systems. Keep a written configuration baseline so an upgrade cannot silently re-enable an external feature.
Which enterprise platforms offer on-premises or air-gapped options?
Enterprise platforms are aimed at organizations that need centralized permissions, auditability, and operational ownership in addition to local inference. Their marketing descriptions are useful starting points, but procurement should require technical and contractual evidence.
VDF AI
VDF AI describes on-premises, sovereign-cloud, and air-gapped deployment choices, private retrieval with permission filtering, and audit evidence. These features could fit organizations that need document access to follow existing permissions rather than giving every user the same search scope.
Request a deployment diagram showing the retrieval service, embedding service, model runtime, identity provider, audit store, administration plane, and update path. Confirm which elements are included in an air-gapped installation and which require a temporary connection during installation or support.
Fosnie
Fosnie describes a self-hosted workspace and says its data-boundary claim applies when it is configured entirely with local services. Its documentation also notes that external providers or APIs receive data according to configuration, and it describes enterprise governance and air-gap options.
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Make “local services only” an explicit acceptance criterion. List every provider setting, connector, webhook, and optional API, then have security staff approve the final values. Clarify retention, administrator access, audit export, and the procedure for removing a document from indexes and backups.
Lokus
Lokus describes local document workflows, citations, and offline operation after required software and model packages are delivered. Its page says cloud-connected features remain disabled until an administrator enables them.
That administrative default is useful, but it should be verified in the delivered build. Confirm supported document types, package provenance, model licensing, local storage locations, and the controls that prevent an ordinary user from enabling a cloud feature.
How do you verify that an assistant really stays off the cloud?
Use a documented verification process rather than a “private” label. The following sequence works for a desktop pilot or an enterprise installation.
- Define the boundary. Write down the networks, servers, workstations, identity systems, storage locations, and support channels that are approved. State whether a temporary connection is allowed for installation or updates.
- Inventory data types. Separate public material, internal documents, personal data, regulated records, source code, and credentials. Decide which categories may be used during testing.
- Draw the data-flow diagram. Include the user interface, model server, embedding model, vector store, parsers, connectors, logs, backups, monitoring, licensing, and update services. Mark every hostname and port.
- Inspect defaults and configuration. Disable cloud models, external APIs, synchronization, telemetry, crash uploads, remote search, and automatic update checks unless each is approved. Store the configuration under change control.
- Verify identity and permissions. Test single sign-on or local accounts, role boundaries, document-level permissions, administrator access, and service-account scope. Confirm that retrieval cannot expose a file merely because it is indexed.
- Observe network behavior. Run the assistant with outbound traffic denied, then monitor DNS requests, connection attempts, and proxy logs during chat, indexing, search, export, error handling, and shutdown. Repeat after changing models and connectors.
- Inspect local artifacts. Check conversation databases, temporary directories, caches, embeddings, model files, audit records, and crash dumps. Verify encryption, permissions, retention, and deletion behavior.
- Test updates and recovery. Perform an offline installation or package transfer, roll back to a known version, restore an index from backup, and rotate credentials without opening an unapproved connection.
- Document acceptance. Record the tested version, configuration hash, network rules, model provenance, test data, observed connections, and the person who approved production use.
What hardware and infrastructure does local AI require?
There is no universal hardware minimum. A small quantized model answering occasional questions has very different requirements from a large model serving many users with long documents. Plan around model size, context length, concurrent sessions, indexing speed, storage, and acceptable response latency.
| Published specification | What it applies to | How to interpret it |
|---|---|---|
| 6 GB RAM | One LocalMind tier | A product recommendation for that tier, not a general local-AI requirement. |
| 8 GB RAM | One LocalMind tier | Use only as a LocalMind planning figure; test the chosen model and document workload. |
| 16 GB RAM | One LocalMind tier | Still product-specific; simultaneous users or larger models may need more memory. |
| No separate graphics card | LocalMind’s listed tiers | The vendor says those tiers do not require a separate GPU; other products and models may differ. |
For a self-hosted deployment, measure memory use while loading the model, building embeddings, and serving the largest expected context. Reserve storage for model files, indexes, versioned backups, logs, and temporary conversion files. If several users share one runtime, test concurrency rather than extrapolating from a single-user demo.
What security and governance controls should you compare?
Model quality is only one buying criterion. A private assistant must fit the organization’s existing control system.
- Identity integration: Determine whether it supports your directory, multifactor authentication, service accounts, and rapid deprovisioning.
- Authorization: Check workspace, folder, document, and tool permissions. Test a user who should not see a sensitive source file.
- Audit records: Identify which prompts, retrieved sources, actions, administrator changes, and exports are recorded, and whether records can be protected from alteration.
- Operational ownership: Assign responsibility for patching, model replacement, backups, incident response, and user support.
- Connector scope: Approve each business-system integration separately. A connector that can read a database may also create a new exfiltration path.
- Model governance: Record model versions, licenses, quantization, system prompts, safety settings, and evaluation results for your own tasks.
- Evidence and contracts: Request architecture diagrams, data-processing terms, support-access procedures, vulnerability handling, and a statement of what is included in the air-gapped or on-premises package.
The available product descriptions do not provide a common independent security audit, pricing comparison, or model-performance benchmark. Run your own evaluation with representative documents and acceptance criteria.
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How should a business run a private-AI pilot?
A short, controlled pilot exposes data-flow and permission problems before production use.
Choose representative tasks
Use realistic work such as summarizing an internal policy, answering questions over a permission-restricted document set, extracting fields from a form, and producing a citation-backed response. Include long documents, scanned files, ambiguous questions, and a deliberately unauthorized file.
Measure the right outcomes
- Answer accuracy and citation correctness on a fixed test set.
- Response time for one user and for expected peak concurrency.
- Indexing time, storage growth, and recovery time.
- Whether unauthorized documents are retrieved or quoted.
- All observed network connections and failed connection attempts.
- Administrator effort for updates, user removal, backup restoration, and incident investigation.
Set a production gate
Require zero unapproved outbound connections, verified permission enforcement, documented update and rollback procedures, acceptable performance on internal tasks, and an owner for ongoing maintenance. If any condition fails, keep the system in a non-sensitive test environment.
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A local interface still calls a remote model
Symptom: Responses stop when internet access is blocked, or logs show connections to a model provider. Fix: Select a local runtime, remove remote-provider credentials, block the external domains, and retest every feature.
Indexing is local but embeddings are remote
Symptom: Chat appears offline, but document ingestion creates outbound requests. Fix: Configure a local embedding model and vector store, then monitor traffic during a complete re-index.
Synchronization is enabled for convenience
Symptom: A desktop assistant copies conversations or files to a vendor or customer-managed server. Fix: Disable synchronization, or route it to an approved internal server with retention and access controls.
Updates break an air-gapped installation
Symptom: A model or software update requires an internet check, or the team cannot verify package provenance. Fix: Establish a signed-package transfer process, a staging network, version records, rollback media, and a schedule for security fixes.
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Permissions do not follow source documents
Symptom: Users can retrieve content from files they could not open directly. Fix: Rebuild connectors with identity-aware filtering, test multiple roles, and stop indexing sources whose permission model cannot be enforced.
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Which option should you choose?
Choose a local desktop assistant when one or a few users need document help, the workload fits a workstation, and your team can secure and update each device. LocalMind’s documented offline workflow and Pia’s local-model option are examples to test, not guarantees for every feature.
Choose self-hosted software with a local runtime when you have administrators who can operate model servers and want control over models, storage, and connectors. Open Assistant and Kiln can fit this pattern only after remote providers and optional cloud features are removed or explicitly blocked.
Choose an enterprise private-AI platform when many users need centralized identity, permission-aware retrieval, audit trails, and a formal operating process. VDF AI, Fosnie, and Lokus describe capabilities in this category, but require architecture, security, and contract validation before sensitive data is loaded.
The strongest buying decision is therefore evidence-based: select the candidate that passes your network, permission, audit, performance, update, and recovery tests—not the one with the broadest privacy wording.
Frequently Asked Questions
Can an air-gapped AI assistant receive security updates?
Yes, but updates must be transferred through a controlled process, such as signed packages moved across an approved staging path. Plan for model files, software releases, certificates, vulnerability fixes, rollback media, and verification before production installation.
Do local AI assistants need a dedicated GPU?
Not always. LocalMind says its listed 6 GB, 8 GB, and 16 GB RAM tiers do not require a separate graphics card, but that is a product-specific statement; model size, concurrency, and latency goals can make different hardware necessary.
Is self-hosted AI automatically compliant with a no-cloud policy?
No. A self-hosted interface can still use remote model providers, cloud embeddings, external connectors, telemetry, or remote backups. Compliance depends on the configured data flows and the controls preventing unapproved changes.
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How can we prove that documents are not leaving the network?
Combine configuration review with observation: deny outbound traffic, monitor DNS and connection attempts during chat and indexing, inspect proxy and firewall logs, and repeat after upgrades or connector changes. Keep the test version, network evidence, and approval record with the deployment documentation.
Quick Recap
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