Unified project context coordination portal

For

Product and project teams coordinating documents, visual plans and AI assistance across one shared workspace

Solves

Project context is split across documents, canvases, chats and trackers, so teams switch tools and lose decisions and sources.

Delivers

Reviewed project context workspace with source-linked AI answers and updates

Built in

about 5 weeks of creation time, MVP in 6 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 switching and context loss while keeping project decisions traceable.

1. Combine documents, tasks, chats and work items in one workspace.
2. Provide a shareable visual canvas for ideas, notes and structured documents.
3. Answer questions from surrounding content with context-aware AI chat.
4. Generate, rewrite and improve text inside documents.
5. Store searchable uploads and cite sources for AI suggestions.
6. Support structured data with table, board, gallery and list views.
7. Enable multiple team members to edit content simultaneously.
8. Surface relevant updates in a personalized feed and inbox.
9. Generate project updates and send them to team inboxes.
10. Remember information across conversations for continuity.
11. Let users choose AI tones and styles as personas.
12. Build connections between information in a knowledge graph.
13. Organize data in tables, boards, calendars and other layouts.
14. Offer encryption, local storage and offline work options.
15. Allow theme, styling and layout adjustments.
16. Connect with other tools and services through integrations.
17. Automate data processing and routine tasks.
18. Embed rich content from permitted publishers.

Everything these tools do, in one app

• Unified workspace Combines documents, tasks, chats, and other work items in one place to reduce app switching.Found in Integrity, Alpine, AppFlowy
• Visual canvas Provides a shareable canvas for mapping ideas, notes, and structured documents visually.Found in Integrity, illumi, Eververse Initiatives
• Context-aware AI chat AI chat that reads surrounding content to give relevant answers and edits in context.Found in Integrity, Alpine, doXmind
• AI writing assistance Helps generate, rewrite, or improve text directly within documents.Found in AppFlowy, doXmind
• Knowledge base with citations Stores searchable uploads and ties AI suggestions to source material with citations.Found in doXmind
• Database blocks Supports structured data with table, board, gallery, and list views plus custom properties.Found in doXmind
• Real-time collaboration Enables multiple team members to contribute and update content simultaneously.Found in AFFiNE AI, Eververse Initiatives, Nummi
• Personalized feed and inbox Surfaces relevant updates and filters noise to help users focus on what matters.Found in Alpine
• AI-generated updates Automatically creates project updates and sends them to team inboxes.Found in Eververse Initiatives
• Contextual memory Remembers information across conversations so users don't repeat details.Found in Nummi
• Customizable AI personas Lets users choose different AI tones and styles to match their work preferences.Found in Nummi
• Smart knowledge graph Builds connections between information to maintain continuity and relevance.Found in Nummi
• Flexible data views Organizes and visualizes data using tables, boards, calendars, and other layouts.Found in AppFlowy
• Privacy and data ownership Offers end-to-end encryption, local storage, and offline work options.Found in AppFlowy
• Customizable interface Allows users to adjust themes, styling, and layout options.Found in AppFlowy, Nummi, illumi
• Third-party integrations Connects with other tools and services to fit into existing workflows.Found in AFFiNE AI, illumi, AppFlowy
• Workflow automation Automates data processing and routine tasks to reduce manual effort.Found in AFFiNE AI
• Embedded rich content Supports embedding content from many publishers to enrich documentation.Found in Eververse Initiatives

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted project files, canvas notes, task records and chat history

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted project files

4. 3

   Canvas notes

5. 4

   Task records and chat history

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed project context workspace with source-linked AI answers and updates

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 fixed workspace schema and permission model; final project decisions and external sharing remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Workspace setup and permissions, Editable project canvas and documents, Review and delivery. Use a thumbnail gallery for workspaces, a large central editing canvas, and a right-hand panel for sources, tasks and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant asset. Make the task-specific outcome reviewed project context workspace with source-linked AI answers and updates visible beside its evidence, review state and value baseline.

Accounts and administration

Workspace ownership, asset versions, 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

Buyer-owned project files, authorized chat exports and permitted research sources. Cloud 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

   6 days

   One buyer segment, one recurring use case; first modules: combine documents, tasks, chats and work items in one workspace; provide a shareable visual canvas for ideas, notes and structured documents. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

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

4. 4

   Full product

   3 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 product and project teams coordinating documents, visual plans and AI assistance across one shared workspace use it to solve "project context is split across documents, canvases, chats and trackers, so teams switch tools and lose decisions and sources"?
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: Reviewed project updates per coordination hour and context corrections after handover.
4. Measure, then decide. Track reviewed project updates per coordination hour and context corrections after handover; 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 fixed workspace schema and permission model; final project decisions and external sharing remain human. Implement one approved input format, a bounded representative case set and the first two task modules: combine documents, tasks, chats and work items in one workspace; provide a shareable visual canvas for ideas, notes and structured documents. Support the third module with operator review: answer questions from surrounding content with context-aware AI chat. 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 project context workspace with source-linked AI answers and updates. Retain the explicit scope boundary: One fixed workspace schema and permission model; final project decisions and external sharing 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 coordination QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed workspace schema and permission model; final project decisions and external sharing 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 workspace package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist coordination separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed project context workspace with source-linked AI answers and updates. 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 switching and context loss while keeping project decisions traceable. Demonstrate a concrete reviewed project context workspace with source-linked AI answers and updates using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and project teams coordinating documents, visual plans and AI assistance across one shared workspace professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed project context workspace with source-linked AI answers and updates from a small authorized input set, with a transparent calculation of reviewed project updates per coordination hour and context corrections after handover and no promised savings.

The first 30 days

1. Week 1: interview five product and project teams coordinating documents, visual plans and AI assistance across one shared workspace and inspect a recent example of project context split across documents, canvases, chats and trackers, so teams switch tools and lose decisions and sources.
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 reviewed project updates per coordination hour and context corrections after handover, 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: Reviewed project updates per coordination hour and context corrections after handover. 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

Reviewed project updates per coordination hour and context corrections after handover; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed project context workspace with source-linked AI answers and updates. 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 workspace templates, permission rules and review examples, together with reliable delivery for a narrow product-development niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and project teams coordinating documents, visual plans and AI assistance across one shared workspace. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Integrity, Alpine, illumi, AppFlowy, doXmind, AFFiNE AI, Eververse Initiatives and Nummi. Compare this product with the buyer's present method on reviewed project updates per coordination hour and context corrections after handover. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, 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 reviewed project context workspace with source-linked AI answers and updates. 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 sharing scope. One fixed workspace schema and permission model; final project decisions and external sharing remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.