Shared agent knowledge library and stewardship console

For

Teams running multiple AI assistants and agents that need shared, governed knowledge across tools and sessions

Solves

Each assistant and agent keeps its own context, so knowledge is duplicated, stale or lost between tools and sessions.

Delivers

A reviewed shared knowledge library with source pointers and access scopes

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Give assistants and agents one governed place to store, retrieve and update knowledge so context survives tool and session changes.

1. Store shared knowledge in one place for agents and humans.
2. Connect assistants and agents through the Model Context Protocol.
3. Support real-time reads and writes from agents.
4. Encrypt stored data at rest and in transit.
5. Surface simultaneous writes for human or agent resolution.
6. Keep version history with change and author logs.
7. Mark old entries deprecated and link to the current version.
8. Share collections at read or read-write levels.
9. Export all knowledge to markdown without lock-in.
10. Retrieve only relevant tool schemas at query time.
11. Execute the selected tool after retrieval.
12. Run always-on agents in hosted containers.
13. Offer a skill marketplace and custom skills.
14. Maintain a living knowledge graph of people, projects and decisions.
15. Resolve entities across renames and duplicates.
16. Flag contradictions between sources.
17. Attach confidence scores and source pointers to agent writes.
18. Crawl and index permitted public URLs.
19. Chunk and embed content for semantic retrieval.
20. Control crawl schedules, depth, concurrency and robots directives.
21. Manage team sources, projects and access.
22. Store notes locally with offline access.
23. Let AI read, create and edit notes without API keys.
24. Provide a block-style editor with daily notes, manuals and calendar.
25. Scope agent access to specific pages or databases.
26. Provide context-aware automation templates.
27. Supply developer guides for deployment, API usage, batching and queuing.

Everything these tools do, in one app

• Shared knowledge base Stores knowledge in one place that multiple AI agents and humans can read from and write to.Found in Brainfork, OzBrain, N71 and 1 more
• MCP integration Connects to AI assistants and agents through the Model Context Protocol so they can access the knowledge.Found in Brainfork, OzBrain, Loop MCP by SimpliflowAI and 4 more
• Real-time read/write Lets agents both consume and update stored content in real time.Found in Notion MCP, OzBrain, N71
• Encryption Encrypts stored data at rest and in transit to keep it private.Found in Brainfork, OzBrain
• Conflict resolution Surfaces simultaneous writes to an agent or human for resolution instead of overwriting.Found in OzBrain
• Version history Keeps older entries and logs what changed and which agent made the change.Found in OzBrain
• Deprecation-based versioning Marks old information as deprecated and links to the current version instead of deleting it.Found in OzBrain
• Collection-level sharing Shares specific collections with teammates at read or read-write access levels.Found in OzBrain
• Export to markdown Exports all stored knowledge to markdown at any time without lock-in.Found in OzBrain
• Dynamic tool retrieval Fetches only the relevant tool schema at query time to keep the AI context small.Found in Loop MCP by SimpliflowAI
• Tool execution Runs the selected tool after retrieval to complete actions end-to-end.Found in Loop MCP by SimpliflowAI
• Always-on agents Runs agents in hosted containers so they keep working after local sessions end.Found in AGNT.Hub
• Skill marketplace Provides a marketplace of skills and supports custom skills to extend agent behavior.Found in AGNT.Hub
• Living knowledge graph Builds a graph of people, projects, and decisions that updates automatically as connected tools change.Found in N71
• Entity resolution Uses behavioral history to handle renames and duplicates when identifying entities.Found in N71
• Contradiction flagging Tracks fact evolution and flags when two sources present conflicting information.Found in N71
• Confidence scores Assigns confidence scores and source pointers to agent writes to prevent bad data from spreading.Found in N71
• URL crawl and index Crawls a public URL and indexes its content for AI assistants to query.Found in Yavy
• Chunk-based semantic indexing Splits content into chunks and embeds them to improve retrieval accuracy for meaning-based queries.Found in Yavy
• Crawl controls Configures scheduled refreshes, depth and concurrency caps, and respects robots.txt and sitemap directives.Found in Yavy
• Team access management Manages multiple sources, organizes projects, and shares access with teammates.Found in Yavy
• Local-first storage Stores notes locally on the device by default and allows offline access.Found in Novi Notes
• Zero-config AI integration Lets AI read, create, and edit notes directly from the app without API keys or complex setup.Found in Novi Notes
• Block-style editor Provides a block-style editor with support for daily notes, manuals, post-its, and a calendar view.Found in Novi Notes
• Workspace governance Scopes which pages or databases an agent can access with admin controls.Found in Notion MCP
• Automation templates Provides context-aware templates for creating docs, managing tasks, generating reports, and organizing knowledge.Found in Notion MCP
• Developer resources Offers guides and resources for deploying servers and handling integration details like API usage, batching, and queuing.Found in Notion MCP

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted sources, agent write policies and access rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted sources

4. 3

   Agent write policies and access rules

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   A reviewed shared knowledge library with source pointers and access scopes

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 MCP version and permissioned source set; final access and data-quality decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source and collection setup, Library and graph explorer, Agent access and audit. Use a searchable list of collections, a central entry view with version and deprecation state, and a right-hand panel for sources, confidence, conflicts and access scope. Let users compare versions side by side. Display draft, reviewed, deprecated and conflicting states. Provide a client or teammate preview link with comments anchored to the relevant entry. Make the task-specific outcome a reviewed shared knowledge library with source pointers and access scopes visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, agent and teammate comments, approval states, usage allowances, write limits, export 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

Customer-owned sources, authorized agent logs and permitted public URLs. Cloud storage, identity providers, MCP clients and export 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: store shared knowledge in one place for agents and humans; connect assistants and agents through the Model Context Protocol. 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 teams running multiple AI assistants and agents that need shared, governed knowledge across tools and sessions use it to solve "each assistant and agent keeps its own context, so knowledge is duplicated, stale or lost between tools and sessions"?
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: Retrieval accuracy on held-out questions, stale or conflicting entries resolved per week and agent task completion after context handoff.
4. Measure, then decide. Track retrieval accuracy on held-out questions and stale or conflicting entries resolved per week and agent task completion after context handoff; 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 MCP version and permissioned source set; final access and data-quality decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: store shared knowledge in one place for agents and humans; connect assistants and agents through the Model Context Protocol. Support the third module with operator review: support real-time reads and writes from agents. 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 reviewed shared knowledge library with source pointers and access scopes. Retain the explicit scope boundary: One fixed MCP version and permissioned source set; final access and data-quality decisions remain human.

What the build depends on. Source upload and preview, asynchronous indexing jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed MCP version and permissioned source set; final access and data-quality 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 source package. Offer a monthly production allowance after repeat demand. Quote complex migrations or specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed shared knowledge library with source pointers and access scopes. 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

Give assistants and agents one governed place to store, retrieve and update knowledge so context survives tool and session changes. Demonstrate a concrete reviewed shared knowledge library with source pointers and access scopes using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Teams running multiple AI assistants and agents professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample shared knowledge library with source pointers and access scopes from a small authorized input set, with a transparent calculation of retrieval accuracy on held-out questions, stale or conflicting entries resolved per week and agent task completion after context handoff and no promised savings.

The first 30 days

1. Week 1: interview five teams running multiple AI assistants and agents and inspect a recent example of each assistant and agent keeping its own context, so knowledge is duplicated, stale or lost between tools and sessions.
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 retrieval accuracy on held-out questions, stale or conflicting entries resolved per week and agent task completion after context handoff, 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: Retrieval accuracy on held-out questions, stale or conflicting entries resolved per week and agent task completion after context handoff. 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

Retrieval accuracy on held-out questions, stale or conflicting entries resolved per week and agent task completion after context handoff; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a reviewed shared knowledge library with source pointers and access scopes. 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 source mappings, access policies and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for teams running multiple AI assistants and agents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Brainfork, OzBrain, Loop MCP by SimpliflowAI, AGNT.Hub, N71, Yavy, Novi Notes and Notion MCP. Compare this product with the buyer's present method on retrieval accuracy on held-out questions, stale or conflicting entries resolved per week and agent task completion after context handoff. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, embedding and crawl processing, storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of a reviewed shared knowledge library with source pointers and access scopes. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, access permissions and data rights. Named owners approve access scopes and substantive changes. One fixed MCP version and permissioned source set; final access and data-quality decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.