Source-linked mobile AI assistant and admin console

For

IT teams and developers who need a private, source-linked AI assistant on mobile devices

Solves

Teams rent several mobile AI apps for chat, voice, search, generation and automation, so prompts, data and answers sit in separate tools without source links or an admin view.

Delivers

Source-linked assistant answers and automation runs

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce tool sprawl and keep answers source-linked and reviewable.

1. Accept natural-language queries and commands.
2. Support back-and-forth dialogue.
3. Capture voice input for hands-free use.
4. Accept typed text input.
5. Retrieve web sources for answers.
6. Generate written content.
7. Generate images from prompts.
8. Process combined text, image, audio and video inputs.
9. Automate scheduling, messaging and reminders.
10. Connect to calendar services.
11. Connect to email services.
12. Run selected models on-device.
13. Let users choose from approved models.
14. Assist prompt writing with builders and completion.
15. Customize response format, tone and length.
16. Save chat history.
17. Provide basic offline mode.
18. Support multiple languages.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned source-linked assistant answers and automation runs with source references and unresolved questions.

Everything these tools do, in one app

• Natural Language Understanding Allows users to interact using everyday language for queries and commands.Found in Qwen Chat App, My Better AI for iOS, Microsoft Copilot for Android and 4 more
• Conversational Interface Enables back-and-forth dialogue for assistance and information retrieval.Found in Qwen Chat App, My Better AI for iOS, Microsoft Copilot for Android and 4 more
• Voice Input Supports spoken commands and queries for hands-free interaction.Found in Microsoft Copilot for Android, Q - Ultimate AI Voice Chatbot mobile app
• Text Input Allows users to type queries and commands.Found in Qwen Chat App, My Better AI for iOS, Microsoft Copilot for Android and 4 more
• Web Search Retrieves information from the internet to answer questions.Found in Qwen Chat App, Perplexity Assistant, Version 2
• Content Generation Creates written content such as articles, essays, or marketing copy.Found in Qwen Chat App, Llamao
• Image Generation Generates visual content based on user prompts.Found in Qwen Chat App
• Multimodal Input Processes combined inputs like text, images, audio, and video.Found in Qwen Chat App
• Task Automation Automates routine tasks such as scheduling, messaging, and reminders.Found in Microsoft Copilot for Android, Grok for Android, Bo AI
• Calendar Integration Connects to calendar services to manage events and reminders.Found in Microsoft Copilot for Android, Bo AI
• Email Integration Connects to email services to access and manage emails.Found in Microsoft Copilot for Android, Bo AI
• On-Device Processing Runs AI models locally on the device for privacy and offline use.Found in Groq for iOS, Version 2
• Model Selection Allows users to choose from multiple AI models.Found in My Better AI for iOS, Version 2
• Prompt Assistance Helps users craft effective prompts with visual builders or auto-completion.Found in My Better AI for iOS
• Response Customization Adjusts the format, tone, or length of AI responses.Found in My Better AI for iOS, Llamao
• Chat History Saves conversations for future reference and quick access.Found in My Better AI for iOS, Version 2
• Offline Mode Provides basic functionality without an internet connection.Found in Q - Ultimate AI Voice Chatbot mobile app
• Multi-Language Support Supports content generation and interaction in multiple languages.Found in Llamao

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted model endpoints, device capabilities and organization policies

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted model endpoints

4. 3

   Device capabilities and organization policies

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Source-linked assistant answers and automation runs

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. One approved model set and device profile; final factual, legal and security checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Assistant chat and voice, Source-linked answer view, Admin console. Use a mobile-first chat thread with a large input bar, a source panel that opens beside each answer, and an admin console for model endpoints, policies, users and logs. Let users compare model outputs side by side. Display draft, needs review and approved states. Provide a share link with comments anchored to the relevant answer. Make the task-specific outcome source-linked assistant answers and automation runs visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, model endpoints, user roles, client comments, approval states, usage allowances, revision 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

Organization-owned model endpoints, device capabilities and permitted data sources. Cloud storage, calendar and email services, and mobile operating system APIs. 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: accept natural-language queries and commands; support back-and-forth dialogue. 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 developers who need a private, source-linked AI assistant on mobile devices use it to solve "teams rent several mobile AI apps for chat, voice, search, generation and automation, so prompts, data and answers sit in separate tools without source links or an admin view"?
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 corrections after assistant output.
4. Measure, then decide. Track accepted answers per reviewer hour and corrections after assistant output; 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 approved model set and device profile; final factual, legal and security checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: accept natural-language queries and commands; support back-and-forth dialogue. Support the remaining modules with operator review. 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 assistant answers and automation runs. Retain the explicit scope boundary: One approved model set and device profile; final factual, legal and security 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 production requires specialist security QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved model set and device profile; final factual, legal and security 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 assistant 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 assistant answers and automation runs. 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

Reduce tool sprawl and keep answers source-linked and reviewable. Demonstrate a concrete source-linked assistant answers and automation runs using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT teams and developers 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 assistant answers and automation runs from a small authorized input set, with a transparent calculation of accepted answers per reviewer hour and corrections after assistant output and no promised savings.

The first 30 days

1. Week 1: interview five IT teams and developers who need a private, source-linked AI assistant on mobile devices and inspect a recent example of rented mobile AI apps for chat, voice, search, generation and automation, so prompts, data and answers sit in separate tools without source links or an admin view.
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 corrections after assistant output, 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 corrections after assistant output. 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 corrections after assistant output; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked assistant answers and automation runs. 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 prompts, model configurations and review examples, together with reliable delivery for a narrow IT niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for IT teams and developers who need a private, source-linked AI assistant on mobile devices. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Qwen Chat App, My Better AI for iOS, Microsoft Copilot for Android, Grok for Android, Groq for iOS, Q - Ultimate AI Voice Chatbot mobile app, Llamao, Perplexity Assistant, Version 2 and Bo AI. Compare this product with the buyer's present method on accepted answers per reviewer hour and corrections after assistant output. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, on-device processing, storage, 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 assistant answers and automation runs. 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 approved model set and device profile; final factual, legal and security checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.