Private on-device AI assistant console

For

Teams and individuals who need a private, on-device AI chat assistant for everyday questions, drafting and document interaction

Solves

Cloud AI assistants send conversations and documents to external servers, require accounts and internet access, and cannot be inspected or self-hosted.

Delivers

Reviewed assistant answers and exports

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

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

Keep everyday AI chat, drafting and document questions on the device while retaining source-linked review and administrator control.

1. Run AI models locally on the device.
2. Work offline after initial setup or model download.
3. Use the assistant without an account or login.
4. Load open-source models such as Llama, Mistral or DeepSeek.
5. Drag and drop PDFs or markdown files and ask questions about their content locally.
6. Accept text, images and files in the same chat interface.
7. Store selected conversational memories for later reference.
8. Import and search previous ChatGPT conversation histories offline.
9. Provide instant full-text search across all messages.
10. Accept voice input through the microphone.
11. Select from a pool of models or link custom model files.
12. Run on iOS, Android, macOS and Windows.
13. Process conversations inside hardware-protected enclaves with end-to-end encryption.
14. Verify the exact code and configuration against a transparency log.
15. Expose an inference API with SDKs for common languages.
16. Self-host on your own infrastructure.
17. Integrate with iOS and macOS, Siri, Apple Shortcuts and x-callback-url.
18. Automate task assignment, deadline reminders and progress tracking.
19. Provide automated data analysis, real-time reporting and visualization.
20. Compare the reviewed result with the recorded baseline and value assumptions.
21. Capture corrections and named-owner approval before consequential use.
22. Export a versioned reviewed assistant answers and exports with source references and unresolved questions.

Everything these tools do, in one app

• Local on-device processing Runs AI models directly on your device so conversations and data stay private and never go to external servers.Found in AnonAI, Colloqio, RecurseChat and 4 more
• Offline functionality Works without an internet connection after initial setup or model download, useful in remote locations or during travel.Found in Colloqio, RecurseChat, Secret Llama and 2 more
• No account required Lets you use the assistant without creating an account or logging in, reducing tracking and friction.Found in AnonAI, xPrivo
• Open-source models Uses publicly available AI models such as Llama, Mistral, or DeepSeek for transparency and community improvement.Found in AnonAI, xPrivo, Ollama Desktop App and 1 more
• Document chat Allows you to drag and drop PDFs or markdown files and ask questions about their content locally.Found in RecurseChat
• Multimodal input Supports interacting with text, images, and files within the same chat interface.Found in Ollama Desktop App, AnonAI
• Conversation memory Stores selected conversational memories so the assistant can reference past points in later interactions.Found in Colloqio
• Chat history import Lets you import and search through previous ChatGPT conversation histories to continue them offline.Found in RecurseChat
• Full-text search Provides instant search across all messages to quickly retrieve relevant information.Found in RecurseChat
• Voice input Includes microphone support so you can speak your queries instead of typing.Found in Colloqio
• Model selection Allows choosing from a pool of AI models or linking custom model files to best fit the task.Found in xPrivo, RecurseChat, Ollama Desktop App
• Cross-platform availability Offers apps for multiple operating systems such as iOS, Android, macOS, and Windows.Found in AnonAI, Ollama Desktop App, Personal GPT
• Encrypted inference Processes conversations inside hardware-protected enclaves with end-to-end encryption so operators cannot read session data.Found in Tinfoil
• Attestation and transparency Enables clients to verify the exact code and configuration running in the protected environment against a transparency log.Found in Tinfoil
• Inference API Provides an API for integrating private AI inference into applications, with SDKs for common languages.Found in Tinfoil
• Self-hosting Allows running the software on your own infrastructure for complete local control and customization.Found in xPrivo
• Apple ecosystem integration Works across iOS and macOS and supports Siri, Apple Shortcuts, and x-callback-url for automation.Found in Personal GPT
• Automated task management Automates task assignment, deadline reminders, and progress tracking to keep projects on schedule.Found in fullmoon
• Data analysis and reporting Provides automated data analysis, real-time reporting, and visualization tools for insights.Found in Apollo AI, fullmoon

What goes in, what comes out

The workflow

1. In
   Start with

   Local models, permitted documents and administrator policies

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect local models

4. 3

   Permitted documents and administrator policies

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed assistant answers and exports

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. Final judgment and consequential actions remain with the named human owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Assistant console, Source and evidence panel, Administrator console. Use a conversation list, a central chat and document canvas, and a right-hand panel for sources, model, memory and review state. Let users compare model answers side by side. Display draft, changes requested and approved states. Provide an administrator view for models, policies, access and audit. Make the task-specific outcome reviewed assistant answers and exports visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, model versions, document versions, conversation history, memory settings, approval states, usage allowances, export limits, download 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

Author-owned documents, permitted research sources and administrator policies. Cloud asset storage, design-file import/export and publishing destinations. 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 on the device; work offline after initial setup or model download. 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

   10 days

   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 teams and individuals who need a private, on-device AI chat assistant for everyday questions, drafting and document interaction use it to solve "cloud AI assistants send conversations and documents to external servers, require accounts and internet access, and cannot be inspected or self-hosted"?
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 answers per reviewer hour and material error rate.
4. Measure, then decide. Track accepted answers per reviewer hour and material error rate; accepted-output rate; material error 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 open-source model; final judgment and consequential actions remain with the named human owner. Implement one approved input format, a bounded representative case set and the first two task modules: run AI models locally on the device; work offline after initial setup or model download. Support the third module with operator review: use the assistant without an account or login. 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 reviewed assistant answers and exports. Retain the explicit scope boundary: One device platform and one approved open-source model; final judgment and consequential actions remain with the named human owner.

What the build depends on. Asset upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist creative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One device platform and one approved open-source model; final judgment and consequential actions remain with the named human owner.

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 assistant package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist review separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed assistant answers and exports. 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

Keep everyday AI chat, drafting and document questions on the device while retaining source-linked review and administrator control. Demonstrate a concrete reviewed assistant answers and exports using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Teams and individuals who need a private, on-device AI chat assistant professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed assistant answers and exports from a small authorized input set, with a transparent calculation of accepted answers per reviewer hour and material error rate and no promised savings.

The first 30 days

1. Week 1: interview five teams and individuals who need a private, on-device AI chat assistant and inspect a recent example of cloud AI assistants send conversations and documents to external servers, require accounts and internet access, and cannot be inspected or self-hosted.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted answers per reviewer 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 answers per reviewer 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 answers per reviewer hour and material error rate; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed assistant answers and exports. 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 models, administrator policies and review examples, together with reliable delivery for a narrow private-assistant niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for teams and individuals who need a private, on-device AI chat assistant. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

fullmoon, AnonAI, Colloqio, RecurseChat, xPrivo, Secret Llama, Ollama Desktop App, Tinfoil, Personal GPT and Apollo AI. Compare this product with the buyer's present method on accepted answers per reviewer 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 compute, 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 reviewed assistant answers and exports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve user voice, source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and publication scope. One device platform and one approved open-source model; final judgment and consequential actions remain with the named human owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.