Source-linked team AI assistant and admin console

For

Teams and individuals needing AI chat assistance for text generation, data analysis and collaboration

Solves

Teams rent several AI chat, agent, analytics and document tools that keep context and data in separate subscriptions.

Delivers

Source-linked assistant answers and admin-reviewed agent 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

Consolidate AI chat, multi-model access, team collaboration, data analysis and document chat into one owned assistant.

1. Provide conversational AI chat for questions and text generation.
2. Switch between multiple language models including GPT-4, Claude and Llama.
3. Adjust tone and style of generated text.
4. Support team collaboration on shared projects and chats.
5. Maintain shared chat history and context for team members.
6. Retrieve current web information during conversations.
7. Analyze data, compute metrics and generate insights.
8. Generate images from text prompts.
9. Build and customize AI agents for specific actions.
10. Connect internal and external data sources for context.
11. Automate routine tasks and workflows.
12. Check and correct grammar and spelling.
13. Show real-time analytics dashboards and reports.
14. Apply secure data handling and compliance controls.
15. Encrypt conversations and API keys end to end.
16. Chat with documents and websites.
17. Provide a prompt library for reuse.
18. Optimize the interface for mobile access.

Everything these tools do, in one app

• AI chat assistance Provides conversational AI to answer questions and generate text.Found in ChatGPT Team, Suna, Onyx and 4 more
• Multiple language models Allows users to interact with various AI models like GPT-4, Claude, and Llama.Found in 04-x, Onyx, Chord
• Customizable tone and style Lets users adjust the tone and style of AI-generated text to match preferences.Found in Suna, Inflection-2.5
• Team collaboration Enables multiple users to work together on projects and share AI interactions.Found in ChatGPT Team, Suna, Chord
• Shared chat history Maintains a common chat history and context accessible to all team members.Found in ChatGPT Team, Chord
• Web browsing Retrieves up-to-date information from the internet during conversations.Found in ChatGPT Team, Onyx, MindWhisper
• Data analysis Assists with analyzing data, computing metrics, and generating insights.Found in ChatGPT Team, Onyx, Joia and 1 more
• Image generation Creates images from text prompts using AI models like DALL·E.Found in ChatGPT Team
• Custom AI agents Allows building and customizing AI agents to perform specific actions.Found in ChatGPT Team, Onyx
• Data connectors Integrates with internal and external data sources for contextual responses.Found in Onyx, Joia, PortalX
• Workflow automation Automates routine tasks and workflows to reduce manual effort.Found in Onyx, Joia, PortalX
• Grammar and spell check Checks and corrects grammar and spelling in generated text.Found in Suna
• Real-time analytics Provides live dashboards and reports for data monitoring.Found in Joia, PortalX
• Secure data handling Ensures data privacy and compliance with security measures.Found in Joia, PortalX, 04-x
• End-to-end encryption Encrypts conversations and API keys for privacy.Found in 04-x
• Chat with documents Allows users to interact with documents and websites in chat.Found in MindWhisper
• Prompt library Provides a collection of useful prompts for easy access.Found in MindWhisper
• Mobile optimization Optimizes the interface for mobile devices with quick access.Found in 04-x

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted model access, team prompts, connected data sources and document sets

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted model access

4. 3

   Team prompts

5. 4

   Connected data sources and document sets

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked assistant answers and admin-reviewed agent 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 three 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 connector scope; 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 workspace, Agent and connector console, Admin and audit view. Use a left sidebar for conversations, agents and prompt library, a large central chat and document canvas, and a right-hand panel for sources, model choice, tone and review state. Let users compare model answers side by side. Display draft, changes requested and approved states. Provide a shared team history with comments anchored to the relevant message or document. Make the task-specific outcome source-linked assistant answers and admin-reviewed agent runs visible beside its evidence, review state and value baseline.

Accounts and administration

Organization ownership, model access, connector permissions, prompt library versions, agent definitions, usage allowances, member roles, audit 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

Team-owned documents, authorized data sources and permitted model APIs. Cloud storage, identity providers, chat and analytics 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: provide conversational AI chat for questions and text generation; switch between multiple language models including GPT-4, Claude and Llama. 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 teams and individuals needing AI chat assistance for text generation, data analysis and collaboration use it to solve "teams rent several AI chat, agent, analytics and document tools that keep context and data in separate subscriptions"?
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 assistant answers per reviewer hour and corrections after approval.
4. Measure, then decide. Track accepted assistant answers per reviewer hour and corrections after approval; 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 connector scope; final factual, legal and security checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: provide conversational AI chat for questions and text generation; switch between multiple language models including GPT-4, Claude and Llama. Support the third module with operator review: adjust tone and style of generated text. 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 admin-reviewed agent runs. Retain the explicit scope boundary: One approved model set and connector scope; final factual, legal and security checks remain human.

What the build depends on. Document upload and preview, asynchronous model 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 connector scope; 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 agent, connector or analytics work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked assistant answers and admin-reviewed agent 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

Consolidate AI chat, multi-model access, team collaboration, data analysis and document chat into one owned assistant. Demonstrate a concrete source-linked assistant answers and admin-reviewed agent runs using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Teams and individuals needing AI chat assistance for text generation, data analysis and collaboration 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 admin-reviewed agent runs from a small authorized input set, with a transparent calculation of accepted assistant answers per reviewer hour and corrections after approval and no promised savings.

The first 30 days

1. Week 1: interview five teams and individuals needing AI chat assistance for text generation, data analysis and collaboration and inspect a recent example of teams renting several AI chat, agent, analytics and document tools that keep context and data in separate subscriptions.
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 assistant answers per reviewer hour and corrections after approval, 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 assistant answers per reviewer hour and corrections after approval. 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 assistant answers per reviewer hour and corrections after approval; 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 admin-reviewed agent 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, connector configurations and review examples, together with reliable delivery for a narrow team-assistant niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for teams and individuals needing AI chat assistance for text generation, data analysis and collaboration. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

ChatGPT Team, Suna, Onyx, Joia, Chord, Inflection-2.5, PortalX, GPT Maxx, 04-x and MindWhisper. Compare this product with the buyer's present method on accepted assistant answers per reviewer hour and corrections after approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, image generation, 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 admin-reviewed agent 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 connector scope; 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.