Built for teams and enterprise
Three ways organizations put GCTRL to work as the shared, access-controlled memory layer for their entire AI workforce - from live team knowledge to airtight client projects to decades of locked-away legacy data.
Featured use case
Agentic Team Memory
Run one central GCTRL, on your own hardware. Every colleague drops an individual scoped token into their Codex, Claude or Hermes. Each gets their own Wiki-LLM base and their own knowledge graph - and the knowledge of all employees can be merged into one company-wide KG and Wiki.
When people of different clearance query that shared graph, classification stays intact - everyone sees exactly what they're cleared for, nothing more. Full audit trail. GDPR-compliant. Fully on-prem. A real push for data sovereignty.
Your team
Engineer
Claude Code · scoped token
Analyst
Cursor · scoped token
Exec
Hermes · scoped token
central · on-prem · audited
Per-person KG + Wiki
each colleague's own knowledge base
Merged company KG + Wiki
classification enforced per clearance
Why nothing else does this
Plenty of tools let an agent have a memory and write to it. But none of them ingest at scale. GCTRL gives you the raw storage for deterministic context and, in parallel, every organised memory layer - including a curated Wiki-LLM of company knowledge - on a high-performance graph + vector core.
Per-employee scoped tokens
Each colleague connects their own agent with their own KB scope - own wiki, own graph.
One merged company brain
Fuse everyone’s knowledge into a single company KG + Wiki, deduplicated and cross-linked.
Classification-preserving queries
Clearance is enforced at query time on the merged graph - same data, different views per person.
Full audit trail
Every access and every denial is logged with token, action, resource and outcome.
GDPR by design
Incognito sessions stay in browser memory; personalization is opt-in and erasable.
On-prem & sovereign
Local inference, your storage, your network - no data leaves the building.
Ideal for
- • Regulated industries needing on-prem AI with audit + clearance control
- • Engineering & research orgs that want a shared, compounding knowledge base
- • Teams standardizing on agents (Claude Code, Cursor, Codex, Hermes)
How to set it up
- 1. Install GCTRL on a central machine
- 2. Create scoped tokens per colleague
- 3. Connect each agent over MCP
Agency use case
One Source of Truth, Airtight Projects
Run your whole agency on one platform - a single source of truth instead of a sprawl of disconnected tools per client. Every project and client gets its own walled knowledge base, and every colleague or agent connects with a token scoped to exactly the projects they’re on.
Classification and fine-grained access control mean project knowledge can never get mixed up - not even by accident. An agent working Client A’s project literally can’t retrieve, cite, or leak Client B’s data: over-clearance queries return nothing, and every node, edge, chunk and wiki page is gated at query time. One source of truth, zero cross-project bleed - with a full audit trail on every access.
one source of truth · on-prem
Project Atlas
Client A
Engineer · Claude Code
scoped · class-gated
Project Bolt
Client B
Analyst · Cursor
scoped · class-gated
Internal R&D
Confidential
Exec · Hermes
scoped · class-gated
No cross-project bleed
an agent on one project can’t see, cite, or leak another - by accident or otherwise
Why nothing else does this
Folder permissions and per-client workspaces rely on someone never making a mistake - one wrong share, one pasted doc, one agent with too-broad context, and a client’s data ends up where it shouldn’t. GCTRL makes isolation structural: clearance lives on the data itself and is enforced at retrieval, so a leak across projects isn’t discouraged - it’s not representable.
Per-element classification
Every node, edge, chunk and wiki page carries its own clearance - gating is on the data, not a folder rule someone can forget.
Enforced at query time
Over-clearance results vanish during retrieval - an agent can’t surface what its token isn’t cleared for, even with a perfect prompt.
Project-scoped tokens
A token is bound to its project’s knowledge bases; every other project is invisible, not merely hidden.
Accidental-leak proof
If it’s out of scope it can’t be retrieved, cited, or fused into another project - there is no “oops, wrong client.”
One platform, not ten
A single source of truth and one ops surface - instead of a siloed tool per client that never compounds into shared value.
Audit every access
Token, action, resource and outcome - every grant and every denial is logged, for your client and your auditor.
Ideal for
- • Agencies & consultancies running many clients on one platform
- • Multi-client work under NDA or strict confidentiality
- • Chinese-wall separation between projects, deals or case teams
- • Anyone who can’t risk one client’s data in another’s deliverable
How to set it up
- 1. Install GCTRL once as your agency’s source of truth
- 2. Create a KB per project + scoped tokens per colleague
- 3. Set classification per element; connect agents over MCP
Enterprise use case
Activate Your Legacy Data
Every enterprise sits on decades of locked-away knowledge - old mailservers and email archives, a decade-old SharePoint, legacy SQL databases, orphaned file shares and network drives. It's exactly the data your AI needs, and exactly the data nobody can use.
GCTRL ingests that mess at scale. FUSE resolves the duplicates and contradictions - matching records that describe the same entity across systems and reconciling them - into one clean, canonical knowledge graph, and serves it to your agents with provenance, lineage and retention preserved, and clearance enforced at query time.
Locked-away legacy data
Mailserver
email archive
SharePoint
decade-old archive
Legacy SQL
old databases
File shares
orphaned drives
ingest · FUSE resolve · canonical
Clean canonical KG + Wiki
queryable by your AI agents
Provenance + clearance intact
lineage & retention preserved
Why nothing else does this
Most “chat with your data” tools choke on messy legacy at volume - they index a handful of clean docs and call it done. GCTRL is built to ingest the mess and resolve it deterministically into structured, queryable knowledge - turning a liability into a moat, without breaking compliance.
Ingest at volume
Decades of mailservers, SharePoint, SQL and file shares - pulled in at scale, not a sample.
Deterministic entity resolution
FUSE matches records describing the same entity across systems and merges them, repeatably.
Contradictions reconciled
Conflicting and duplicate facts collapse into one canonical, trustworthy version.
Provenance & lineage
Every fact traces back to its source system and document - nothing is a black box.
Retention preserved
Original retention and deletion rules carry through, so compliance stays intact.
Clearance enforced
The resulting graph is queried under the same classification rules as the rest of GCTRL.
Ideal for
- • Migrating or decommissioning legacy systems without losing the knowledge inside
- • M&A data consolidation across two organizations' overlapping systems
- • Making 10+ years of archives finally AI-usable
- • Regulated orgs needing provenance + retention preserved end-to-end
How to set it up
- 1. Install GCTRL and point it at your legacy sources
- 2. Run KEX & FUSE to ingest and resolve at scale
- 3. Serve the graph under access control to your agents