Shared work context coordination portal

For

Engineering and operations teams coordinating people and AI tools across shared work

Solves

Work context lives in separate tools, so people and AI agents act on stale or partial information.

Delivers

A shared, permission-scoped context store with live activity and change summaries

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

Keep one permissioned, current picture of work that people and AI tools can act on.

1. Maintain one shared store of knowledge, decisions and context for people and AI tools.
2. Capture tasks automatically from meetings, email and chat.
3. Show real-time visual dashboards of work state.
4. Clean and prepare incoming data before analysis.
5. Build reports from adjustable templates.
6. Produce meeting transcripts, summaries and linked tasks.
7. Connect calendars and prompt users before meetings.
8. Let several teammates drive one AI session together.
9. Show what every teammate and agent is working on now.
10. Summarize what changed while a user was away.
11. Expose shared context to AI tools through an MCP server.
12. Work with multiple AI tools rather than one model provider.
13. Ingest information from connected tools without manual writing.
14. Scope what context is shared and with whom.
15. Store context as events and relationships instead of last-write-wins fields.
16. Give team members shared access and feedback.
17. Meet enterprise security and data residency requirements.
18. Store unlimited data with a capped file size per context document.
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 shared, permission-scoped context store with source references and unresolved questions.

Everything these tools do, in one app

• Shared context layer Keeps one up-to-date store of knowledge, decisions, and context that both people and AI tools can use.Found in Compendium, In Parallel MCP
• Automatic task capture Pulls tasks from meetings, emails, and chat without manual entry.Found in Hoop
• Interactive dashboards Shows data in real-time visual dashboards for easier interpretation.Found in Sense
• Automated data cleaning Prepares and cleans data automatically before analysis.Found in Sense
• Customizable reporting templates Lets users create reports from templates they can adjust.Found in Sense
• Meeting transcripts and summaries Provides transcripts, summaries, and context alongside captured tasks.Found in Hoop
• Calendar integration Connects to the calendar to prompt users before meetings.Found in Hoop
• Multiplayer AI sessions Lets multiple teammates drive one AI interaction together in real time.Found in Compendium
• Live activity view Shows what every teammate and agent is working on right now.Found in Compendium
• Change summaries Summarizes what changed while you were away.Found in Compendium
• MCP server access Exposes the shared context to AI tools through an MCP server.Found in Compendium, In Parallel MCP
• Model-agnostic access Works with multiple AI tools rather than locking users into one model provider.Found in Compendium, In Parallel MCP
• Passive integrations Ingests information from connected tools automatically without manual writing.Found in Compendium, In Parallel MCP
• Permission-scoped sharing Lets users control what context is shared and with whom.Found in In Parallel MCP
• Graph-based context model Stores context as events and relationships so updates reflect changes without last-write-wins logic.Found in In Parallel MCP
• Collaborative team access Allows team members to access and give feedback together.Found in Sense
• Enterprise security compliance Meets strict security and data residency requirements with certifications and policies.Found in In Parallel MCP
• Unlimited data storage Stores unlimited data with a capped file size per context document.Found in Compendium

What goes in, what comes out

The workflow

1. In
   Start with

   Connected tool activity, meeting records, decisions and task state

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect connected tool activity

4. 3

   Meeting records

5. 4

   Decisions and task state

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   A shared, permission-scoped context store with live activity and change summaries

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, permission checks and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final decisions on task ownership, priority 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: Connections and permissions, Shared context and live activity, Reports and change summaries. Use a workspace list, a central context timeline with linked tasks and decisions, and a right-hand panel for permissions, sources and comments. Let users compare current state against a previous snapshot. Display draft, in review and approved states. Provide a scoped share link for external collaborators. Make the task-specific outcome a shared, permission-scoped context store with live activity and change summaries visible beside its evidence, review state and value baseline.

Accounts and administration

Workspace ownership, connection scopes, member roles, agent access, approval states, retention rules, export history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data residency settings, audit logs and explicit approval for external actions.

Integrations and data access

Connected calendars, email, chat, meeting recorders and project trackers. Cloud storage, identity providers, AI tool endpoints and MCP clients. 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: maintain one shared store of knowledge, decisions and context for people and AI tools; capture tasks automatically from meetings, email and chat. 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 engineering and operations teams coordinating people and AI tools across shared work use it to solve "work context lives in separate tools, so people and AI agents act on stale or partial information"?
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: Coordination errors per week and time to reconstruct current work state.
4. Measure, then decide. Track coordination errors per week and time to reconstruct current work state; 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 team, one connected tool set and one approved AI tool; final task ownership and external sharing decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: maintain one shared store of knowledge, decisions and context for people and AI tools; capture tasks automatically from meetings, email and chat. 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 a shared, permission-scoped context store with live activity and change summaries. Retain the explicit scope boundary: One team, one connected tool set and one approved AI tool; final task ownership and external sharing decisions remain human.

What the build depends on. Connection setup and preview, asynchronous ingestion jobs, editable version history, reviewer access and tested export formats. High-fidelity coordination requires accurate source data and clear permission rules. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One team, one connected tool set and one approved AI tool; final task ownership and external sharing decisions 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 team and tool set. Offer a monthly coordination allowance after repeat demand. Quote complex enterprise security, data residency or custom integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded shared, permission-scoped context store with live activity and change summaries. 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 one permissioned, current picture of work that people and AI tools can act on. Demonstrate a concrete shared, permission-scoped context store with live activity and change summaries using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering and operations teams coordinating people and AI tools across shared work professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample shared, permission-scoped context store with live activity and change summaries from a small authorized input set, with a transparent calculation of coordination errors per week and time to reconstruct current work state and no promised savings.

The first 30 days

1. Week 1: interview five engineering and operations teams coordinating people and AI tools across shared work and inspect a recent example of work context living in separate tools, so people and AI agents act on stale or partial information.
2. Week 2: prepare a consented or synthetic demonstration of the stated task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure coordination errors per week and time to reconstruct current work state, 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: Coordination errors per week and time to reconstruct current work state. 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

Coordination errors per week and time to reconstruct current work state; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a shared, permission-scoped context store with live activity and change summaries. 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 connection mappings, permission patterns and review examples, together with reliable delivery for a narrow coordination niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering and operations teams coordinating people and AI tools across shared work. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Sense, Hoop, Compendium and In Parallel MCP, plus manual status updates and generic project trackers. Compare this product with the buyer's present method on coordination errors per week and time to reconstruct current work state. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Ingestion and processing, storage, reviewer hours, integration maintenance and support. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of a shared, permission-scoped context store with live activity and change summaries. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, permission boundaries, data residency and usage rights. Named owners approve task changes and external sharing. One team, one connected tool set and one approved AI tool; final task ownership and external sharing decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.