On-device private model workspace

For

IT teams and privacy-sensitive professionals who must keep prompts, documents and model outputs on their own devices

Solves

Cloud AI tools send prompts, documents and outputs to external servers, and separate local-model, transcription, document and security tools fragment the workflow.

Delivers

Source-linked reviewed answers and transcripts

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Run private AI work on owned hardware instead of renting several cloud subscriptions.

1. Run AI models locally without an internet connection.
2. Keep conversations, documents and inputs on the device.
3. Provide a private chat interface.
4. Deliver fast on-device responses.
5. Support iOS, Android and macOS.
6. Keep the platform open-source and inspectable.
7. Transcribe and summarize audio recordings.
8. Run efficiently without overheating the device.
9. Offer a graphical interface for building and deploying models.
10. Support multiple model architectures.
11. Fine-tune models on custom datasets.
12. Test models in real time as they are built.
13. Connect with common machine learning frameworks.
14. Switch between models for different tasks.
15. Download GGUF models from Hugging Face.
16. Chat with PDFs for questions and summaries.
17. Detect and alert on potential security risks.
18. Store files and communications with strong encryption.
19. Block trackers and malicious sites during browsing.
20. Apply automatic software updates.
21. Expose customizable privacy settings.
22. Allow customizable AI behavior.
23. Compare the reviewed result with the recorded baseline and value assumptions.
24. Capture corrections and named-owner approval before consequential use.
25. Export a versioned source-linked reviewed answers and transcripts record with source references and unresolved questions.

Everything these tools do, in one app

• Offline AI processing Runs AI models directly on your device without needing an internet connection.Found in Private Mind, Neuron AI, Sanctum AI and 1 more
• Local data storage Keeps all conversations and inputs stored on your device, never sending them to external servers.Found in Private Mind, Neuron AI, Sanctum AI and 1 more
• Private chat interface Provides a secure chat environment to interact with AI models.Found in Neuron AI, Haplo AI
• Fast on-device responses Delivers quick AI responses due to local processing.Found in Private Mind, Neuron AI
• Cross-platform availability Available on multiple operating systems such as iOS, Android, and macOS.Found in Private Mind, Haplo AI
• Open-source platform Allows users and developers to inspect, modify, and contribute to the code.Found in Private Mind
• Audio transcription and summarization Automatically transcribes and summarizes audio recordings, useful for meetings or lectures.Found in Neuron AI
• Lightweight and optimized Runs efficiently without overheating the device.Found in Neuron AI
• Graphical model interface Offers an intuitive visual interface for building and deploying language models.Found in LM Studio
• Multiple model architectures Supports various types of language model architectures.Found in LM Studio
• Fine-tuning on custom datasets Enables users to fine-tune models using their own data.Found in LM Studio
• Real-time model testing Allows immediate interaction and testing with models as they are built.Found in LM Studio
• Machine learning framework integration Connects with popular ML frameworks for extended functionality.Found in LM Studio
• AI model switching Easily switch between different AI models to suit various needs.Found in Sanctum AI
• Hugging Face integration Access and download thousands of GGUF models from Hugging Face.Found in Sanctum AI
• Document interaction Chat with PDFs to ask questions and get summaries.Found in Sanctum AI
• Real-time threat detection Identifies and alerts about potential security risks promptly.Found in SecureAI Tools Individual Edition
• Encrypted data storage Stores personal files and communications with strong encryption.Found in SecureAI Tools Individual Edition
• Secure browsing tools Prevents tracking and blocks malicious websites during browsing.Found in SecureAI Tools Individual Edition
• Automatic software updates Keeps security protocols up to date automatically.Found in SecureAI Tools Individual Edition
• Customizable privacy settings Allows users to tailor privacy and protection settings to their preferences.Found in SecureAI Tools Individual Edition
• Customizable AI behavior Lets users adjust AI responses to suit their preferences.Found in Haplo AI

What goes in, what comes out

The workflow

1. In
   Start with

   Local model files, device resources, documents and audio

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect local model files

4. 3

   Device resources

5. 4

   Documents and audio

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked reviewed answers and transcripts

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Local model choice and device memory limit output quality; final legal, security and professional checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Model and device setup, Private chat and document workspace, Admin console. Use a model list with size and hardware fit, a central chat and document canvas, and a right-hand panel for sources, model settings and review state. Let users compare model answers side by side. Display draft, changes requested and approved states. Provide an admin view of models, storage, permissions and update status. Make the task-specific outcome source-linked reviewed answers and transcripts visible beside its evidence, review state and value baseline.

Accounts and administration

Model ownership, device profiles, asset versions, user comments, approval states, storage allowances, update history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Local model files, authorized documents and permitted audio sources. Device storage, file import/export and Hugging Face model downloads. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: run AI models locally without an internet connection; keep conversations, documents and inputs on the device. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will IT teams and privacy-sensitive professionals who must keep prompts, documents and model outputs on their own devices use it to solve "cloud AI tools send prompts, documents and outputs to external servers, and separate local-model, transcription, document and security tools fragment the workflow"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted outputs per operator hour and material error rate.
4. Measure, then decide. Track accepted outputs per operator hour and material error rate; accepted-output rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One device platform and one approved model family; final legal, security and professional checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: run AI models locally without an internet connection; keep conversations, documents and inputs on the device. Support the remaining modules with operator review: private chat, audio transcription, document chat and model switching. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around source-linked reviewed answers and transcripts. Retain the explicit scope boundary: One device platform and one approved model family; final legal, security and professional checks remain human.

What the build depends on. Asset upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity local inference requires adequate device memory and specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One device platform and one approved model family; final legal, security and professional checks remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined device and model package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist security review separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked reviewed answers and transcripts record. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Run private AI work on owned hardware instead of renting several cloud subscriptions. Demonstrate a concrete source-linked reviewed answers and transcripts record using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT teams and privacy-sensitive professionals professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample source-linked reviewed answers and transcripts record from a small authorized input set, with a transparent calculation of accepted outputs per operator hour and material error rate and no promised savings.

The first 30 days

1. Week 1: interview five IT teams and privacy-sensitive professionals and inspect a recent example of cloud AI tools sending prompts, documents and outputs to external servers.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted outputs per operator hour and material error rate, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted outputs per operator hour and material error rate. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted outputs per operator hour and material error rate; accepted-output rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked reviewed answers and transcripts. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved model configurations, device profiles and review examples, together with reliable delivery for a narrow privacy-sensitive niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for IT teams and privacy-sensitive professionals. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Private Mind, Neuron AI, LM Studio, Sanctum AI, SecureAI Tools Individual Edition and Haplo AI, plus cloud AI subscriptions. Compare this product with the buyer's present method on accepted outputs per operator hour and material error rate. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model download and storage, device testing, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of source-linked reviewed answers and transcripts. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and external actions. One device platform and one approved model family; final legal, security and professional checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.