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The AI Hiveby IT Labs India
Product

How The AI Hive works

A governed shared memory that your people and their AI assistants read from and write to, with every step checked against who is asking, where they are working and what they have been granted.

1. Scopes

Seven scopes, from company-wide to private

Plain words: company-wide, then project, then client, then team, then person, then the person's AI app, then private notes. Each piece of knowledge lives in exactly one place, and is inherited by everyone it applies to.

FIG. 1 · MEMORY SCOPES AND FLOWSSEVEN SCOPES
Memory scopes and the three flows Memory is organised from organisation at the top, through project and client, team, user and agent. Personal memory sits apart and is private. Up arrows show promotion with approval, down arrows show inheritance, and the across arrow shows team knowledge being synthesised into project knowledge. Organisationcompany-wideProjecta programmeClientkept apartTeama group of peopleUserone personAgentthat person's AI appPersonalprivate notes, never promotedDOWN · INHERITUP · PROMOTEACROSSdefault offgovernance approvesproject lead approves3 people confirm Memory scopes and the three flows Memory is organised from organisation at the top, through project and client, team, user and agent. Personal memory sits apart and is private. Up arrows show promotion with approval, down arrows show inheritance, and the across arrow shows team knowledge being synthesised into project knowledge. Organisationcompany-wideProjecta programmeClientkept apartTeama group of peopleUserone personAgentthat person's AI appPersonalprivate notes, never promotedDOWN · INHERITUP · PROMOTEUP · governance approvesACROSSdefault offUP · project leadapprovesUP · 3 people confirm
Memory scopes and the three flows: up with approval, down by inheritance, across by approved synthesis.
ScopeWhat lives here
OrganisationCompany-wide knowledge: standards, policies, shared definitions.
ProjectKnowledge for one programme of work, shared by everyone on it.
ClientEverything about one client, walled off from every other client.
TeamWhat a group of people has learned and confirmed together.
UserA person's own working memory.
AgentThe memory of that person's AI app, working on their behalf.
PersonalPrivate notes. Visible only to the person, and never promoted.

Available now All seven scopes are built and running on our pilot deployment.

2. Flows

Three governed flows move knowledge between scopes

Up: promotion

Validated insights move up only with approval.

  • To Team when at least 3 people independently confirm it
  • To Project by a project lead
  • To Organisation by governance

Personal memory is private and is never promoted.

Down: inheritance

When a higher scope changes, every affected person's AI gets the update. New members inherit context immediately, instead of spending weeks being briefed.

Across: synthesis

Team knowledge can be merged into project knowledge. This is off until an admin switches it on, and it always needs approval.

3. A worked example

From a private note to an organisation standard

An illustrative scenario showing how a single useful insight travels through the scopes. The names and details are invented for explanation.

  1. A personal note

    An engineer jots down a private note: a particular supplier's drawings need a specific revision check before release. It sits in her Personal scope. Nobody else can see it, and it will never be promoted on its own.

  2. Her AI app proposes it

    At the end of a session, her AI app drafts a session summary. She reads it and confirms it before anything is saved. The useful insight is proposed as a fact for her team, not written straight in.

  3. Team: after 3 confirmations

    As colleagues hit the same issue and independently confirm it, the fact reaches the threshold of at least 3 people. It then becomes part of Team memory, and every team member's AI inherits it.

  4. Project: a lead approves

    The project lead reviews the fact and approves it for the whole project. Everyone working on the programme now starts with it, including people who join next month.

  5. Organisation: governance decides

    If the insight deserves to become a company standard, governance approves it at Organisation level. Every earlier version is kept, and the audit log records who approved what and when.

4. Context pack

A running start for every AI task

When an AI app begins a task, it asks The AI Hive for a context pack: the memory inherited for its place in the organisation, meaning its organisation, project, client, team and user. The pack comes with a manifest, so the app can refresh cheaply later instead of fetching everything again.

The result is less repeated context-setting. Memory is returned labelled as data, and text that looks like instructions is flagged, so retrieved memory is never mistaken for a command.

Available now

Behind every result

  • Hybrid search: meaning plus keyword, re-checked against permissions on every result
  • Client walls: a client's memory is returned only while working on that client or a linked project
  • Audience labels: internal (default), restricted (named people only, bots never read) and external_ok (safe for outside replies, set by a lead)
  • Versioning: every update keeps the previous version
5. Spaces

The right memory loads wherever a person works

People work in repositories, drives, chat projects and local folders across several AI apps. The AI Hive maps each of these spaces to the company's places, so an AI app knows which project, team or client it is working for.

FIG. 2 · COMPANY PLACES AND A PERSON'S SPACESILLUSTRATIVE EXAMPLE
Company places and a person's spaces, linked in a space graph On the left, the organisation's places (org, project, team) and the company's declared spaces: a repository, a Jira project, a shared drive and a team chat channel. On the right, the spaces reported by one person's own tools. A solid line shows that her clone is the same space as the company repository; dashed lines link her own repository, drive folder and chat project to the Shipping project; her local scratch folder has no line and stays private. Organisation (places)Org: Example CoProject: ShippingTeam: PlatformCompany spacesRepository shipping-apiJira project SHIPShared drive "Shipping docs"Team chat #shippingDeclared by the company andlinked to places by mapping rulesPerson: AliceA coding assistant on two PCs,a chat app on her phone anda review bot she sponsorsHer spacesClone of shipping-apiHer repo ship-toolsDrive folder "ship notes"Chat project "Shipping"Local folder ~/scratchReported by her own tools,never guessedSpace graph• one address per space, many aliases• owner: company, person or client• every link has evidence behind itsame space (her clone = company repo)linked to a placeno line: stays private Company places and a person's spaces, linked in a space graph On the left, the organisation's places (org, project, team) and the company's declared spaces: a repository, a Jira project, a shared drive and a team chat channel. On the right, the spaces reported by one person's own tools. A solid line shows that her clone is the same space as the company repository; dashed lines link her own repository, drive folder and chat project to the Shipping project; her local scratch folder has no line and stays private. Organisation (places)Org: Example CoProject: ShippingTeam: PlatformCompany spacesRepository shipping-apiJira project SHIPShared drive "Shipping docs"Team chat #shippingDeclared by the company and linked toplaces by mapping rulesSpace graph• one address per space, many aliases• owner: company, person or client• every link has evidence behind itsame space (her clone = company repo)linked to a placeno line: stays privatePerson: AliceA coding assistant on two PCs,a chat app on her phone anda review bot she sponsorsHer spacesClone of shipping-api= same spaceHer repo ship-tools→ ShippingDrive folder "ship notes"→ ShippingChat project "Shipping"→ ShippingLocal folder ~/scratchno line · privateReported by her own tools, never guessed
Illustrative example. Company places and spaces on the left, one person's spaces on the right. The AI Hive knows that her clone is the company repository, and that her own repo, drive folder and chat project belong to the Shipping project. Her scratch folder stays private.

Declared, not guessed

The company declares its spaces and mapping rules. A person's own tools report their spaces; The AI Hive never guesses where someone is working.

Every link has evidence

A link from a space to a place is declared or suggested with its evidence, and suggestions are confirmed before they count.

Private stays private

A space with no link, such as a scratch folder, stays in the person's own memory and is never shared.

In testing Spaces registry and space mapping are built and in testing.

6. Indexes

How a question finds its answer

The AI Hive keeps a searchable index for each kind of thing it knows: who and where, and what. Every index is searched by keyword and by meaning, and every entry carries its audience, so results only include what the person asking may see.

FIG. 3 · INDEXES AND HOW A QUESTION IS ANSWEREDILLUSTRATIVE EXAMPLE
Indexes, the link graph, and how a question is answered Catalog indexes for people, agents and bots, places, spaces and topics on the left; memory, knowledge and source indexes on the right; a link graph in the middle; short index cards loaded at session start; and the five steps of answering a question: question, who and where, filter by permissions, search, and an answer that links to its source. Index cards, loaded at session startA short card for the person and their current place, sent with the space manifest. Details are fetched only when needed.Catalog indexes: who and wherePeoplenames, e-mails, accounts, ask-me-aboutAgents · task bots · workerskind, master or sponsor, purpose, placesPlacesorg, projects, teams, clients, membersSpacesspace IDs, aliases, owner side, linksTopics and glossarysystems, customers, terms, codenamesContent indexes: whatMemory indexitems from people, agents, botsand workers, stamped withtheir memory addressKnowledge indexdecisions, actions, facts,procedures, digests, who to ask(from space content)Source indexcleaned mail, chat, documentand issue text, with access rules,for finding the originalLink graphperson member of team / projectagent / bot / worker works for personspace linked to placeitem created by actoritem created in spaceitem / record counts toward placerecord about topicrecord evidence from sourceEvery index: keyword + meaning search,every entry carries its audience,results filtered to what you may seeAnswering a question1 Question“What did we decideabout canary deploys?”from her coding app,in shipping-api2 Who and wherecatalog indexes:“canary” = topic“we” = Alice’s placesShipping = 3 spaces3 Filteronly places andspaces Alice maysee; client walls;groups of 3 or more4 Searchmemory + knowledgeindexes, keywordand meaning, insidethat scope5 Answerfollow links to thesource: “blue-green,5-min canary — 3 Oct,#shipping (Alice);7 people agree” Indexes, the link graph, and how a question is answered Catalog indexes for people, agents and bots, places, spaces and topics on the left; memory, knowledge and source indexes on the right; a link graph in the middle; short index cards loaded at session start; and the five steps of answering a question: question, who and where, filter by permissions, search, and an answer that links to its source. Index cards, loaded at session startA short card for the person and theircurrent place, sent with the space manifest.Details are fetched only when needed.Catalog indexes: who and wherePeoplenames, e-mails, accounts, ask-me-aboutAgents · task bots · workerskind, master or sponsor, purpose, placesPlacesorg, projects, teams, clients, membersSpacesspace IDs, aliases, owner side, linksTopics and glossarysystems, customers, terms, codenamesLink graphperson member of team / projectagent / bot / worker works for personspace linked to placeitem created by actoritem created in spaceitem / record counts toward placerecord about topicrecord evidence from sourceEvery index: keyword + meaning search,every entry carries its audience,results filtered to what you may seeContent indexes: whatMemory indexitems from people, agents, botsand workers, stamped withtheir memory addressKnowledge indexdecisions, actions, facts,procedures, digests, who to ask(from space content)Source indexcleaned mail, chat, documentand issue text, with access rules,for finding the originalAnswering a question1 Question“What did we decide about canary deploys?”from her coding app, in shipping-api2 Who and wherecatalog indexes: “canary” = topic,“we” = Alice’s places,Shipping = 3 spaces3 Filteronly places and spaces Alice may see;client walls; groups of 3 or more4 Searchmemory + knowledge indexes, keywordand meaning, inside that scope5 Answerfollow links to the source:“blue-green, 5-min canary — 3 Oct,#shipping (Alice); 7 people agree”
Illustrative example. Catalog indexes (who and where) and content indexes (what), tied together by a link graph. A question is resolved to places and topics, filtered to what the person may see, searched, and answered with a link back to the source.

In testing Index cards and capture with routing are built and in testing.

Built and in testing. Anything that does not pass testing will be removed.

7. Connectors

Read-only by design

The AI Hive learns from the systems you already use, but it never writes into them.

Available now Available now

  • Jira Cloud
  • PostgreSQL views
  • IMAP mailboxes: chosen folders only, with the sender domain verified
  • Git repository addresses mapped to projects (the code itself is not read)

Connectors feed proposed facts into an approval queue. Nothing they find becomes shared memory until a person approves it.

Built and in testing In testing

  • Microsoft 365 mail, Teams and drives
  • Google Drive
  • GitHub

Built and in testing. Anything that does not pass testing will be removed.

See the integrations page for the full picture.

8. Caller rights

An AI app never has more rights than the person behind it

Every request carries who is asking, which app is calling and where they are working. The AI Hive gives an AI app the smaller of the person's own rights and a time-limited grant. An agent can therefore never see or change more than its user could.

Bots and digital workers have their own identity and a human sponsor. They can read and propose within their grants, but they can never approve. If an app misbehaves, a kill switch suspends that app or bot immediately.

Controls that follow from this

  • Governed writes: nothing reaches a shared scope without a role, a time-limited grant or an approval
  • Feature switches per level (organisation, project, team, client, user, bot); lower levels can only restrict
  • Session summaries confirmed by the person, or the bot's sponsor, before they are saved
  • Audit of every allow and deny

See how The AI Hive could work in your organisation

We are onboarding a small number of design partners to the pilot programme. Tell us about your teams and the AI tools they use, and we will scope a pilot together.

Book a demo Apply for the pilot programme