AI thatfinisheswhat it starts.
Marketing's AI, engineering's AI, finance's AI. None of them know what the others are doing. GUST turns your scattered company data into one reasoning context graph, so every agent and every person works from the same knowledge, under one set of rules, with data that stays yours.
Data is the key to scaling enterprise AI.GUST makes it one ecosystem.
GUST transforms data from scattered sources across the company into a reasoning context graph. When a budget changes, every dependent decision is re-evaluated. When a policy updates, every governed agent inherits the new rule. When something goes wrong, you can trace exactly what happened, why and under whose authority.
- [01]
Token ROI
Know what every agent costs and what outcome you got for your money. Cost attributed by scope, team and project; shared context ends the re-fetching, so you pay for outcomes, not repetition.
- [02]
Regulatory compliance
Every action traces back to its scope, its policy and the person who signed off. Built for EU AI Act high-risk system requirements from the start, not bolted on after a procurement review.
- [03]
Data sovereignty
Complete ownership and control of your data, regardless of infrastructure. No model, framework or platform lock-in: switch at message and task level. People decide what propagates.
The massive cost of organisational misalignment.
Out-of-control costs, budget overruns and missed business objectives, because departments and their AI systems are not aligned. Every team is happy with its own AI. Individually they work from scattered, siloed data, build up different memories, produce conflicting analyses and keep their evidence in separate locations. The agents are working. But who is in control, what is truly reliable, and what are they working from?
- 95%of AI pilots fail to produce financial impactMIT NANDA
- 98%of enterprises have unsanctioned AI in use across departmentsISACA
- 3×more cost savings when AI is cross-functionalG2 / Salesmate
- TodayMany tools, many memories, conflicting output. CFOs cannot assign unit costs to outcomes or determine ROI. AI risk grows with every tool, and data sovereignty is locked to vendors.
- With GUSTEvery AI tool reads from one live organisational context graph. Changes propagate with tailored governance checks. Token cost is assigned to outcomes. Your data remains yours, whatever the infrastructure.
Coordinate and control. Replace nothing.
GUST sits underneath the AI apps you already run, just above the models and frameworks. Any agent, in any framework, binds to a scope over MCP and A2A. So every agent, model and human user works from the same current state of the business.
- [01]
One organisational context. Every AI agent.
A scope represents a team, a project or a policy domain. GUST connects these areas and their dependencies, so the relevant knowledge, decisions and rules are shared across the organisation. Every agent starts with the specific slice of context it needs, already connected to the bigger picture.
- [02]
Specialist agents from context slices.
Agents are created from slices of the ingested context, so every department works from the same knowledge base. Memory follows the agent; boundaries follow the scope.
- [03]
Live propagation, not a static ontology.
Context updates are pushed immediately to every system and specialist agent that is permitted to receive them. Per scope you choose the rule, for example: auto-propagate low-risk updates, recommend material changes for human review, block dangerous updates outright.
- [04]
No lock-in, full sovereignty.
Model, framework and platform neutral: switch at message and task level. ChatGPT, Claude, LangChain, CrewAI, LangGraph, the OpenAI Agents SDK or your own build. Complete ownership and control of your data.
- [05]
Agents and people as participants.
Cross-system collaboration between agents and people under controlled orchestration, with governance and compliance built in from the start, not bolted on.
- [06]
Token cost assigned to outcomes.
Efficient token use through scope-based context filtering, and cost attributed by scope, team and project, so every decision traces to an outcome and its price.
Every decision, on the record.
Marketing cancels Campaign X. GUST re-evaluates every scope that depends on it: engineering gets a recommendation, sales is updated automatically, finance is notified. Each step is logged with who, what, when and under which rule.
propagation | marketing/campaign-x
- marketingcampaign-x cancelled | by alice@acme.com
- graph4 scopes affected | depth 2 | 180 ms
- engineeringsprint-47 | feature Y deprioritised | recommend
- salespipeline-q3 | pitch deck refreshed | auto
- financeq3-budget | reallocation logged | notify
- reviewerbob@acme.com accepted sprint-47
- # chain complete | 4 scopes | 1 auto | 1 notify | 1 reviewed
- → eu-ai-act category=high-risk | retention=7y
propagations logged1214
- ORG | acme
- SCOPE | marketing/campaign-x
- RULE | budget-reallocation
- 4 SCOPES | 1 CHANGE
- [ HUMAN ]
People
Owners, reviewers and operators who decide, approve and sign off.
- [ NATIVE ]
GUST agents
Chat, developer, planner, memory crawler, scope manager: specialists are created from your context, scoped exactly to your intent and what they need.
- [ A2A ]
Remote agents
The agents you already run elsewhere, in your own enterprise apps or as part of a SaaS platform, all coordinated over GUST's A2A and MCP.
A software release changes an approval chain. Everything that depends on it follows.
- 01
Find
GUST finds every document, workflow and agent that depends on the change.
- 02
Draft
Specialist agents draft the updates: training decks, SOPs, help-desk articles.
- 03
Approve
GUST can be configured so each update goes to its business owner for review and approval.
- 04
Record
GUST keeps a record of what changed, why and who signed off.
The business owners still decide.
Virtual workers that run on one shared context.
Specialist agents for the workflows where misalignment costs most, for SME and corporate teams, and the patterns that make them reliable.
- [01]
KYC
Customer due diligence where every check reads the same customer record and the current policy, and every finding is traceable to its source and its reviewer.
- [02]
Credit check
Assessments that apply the same risk rules across teams and systems. A rule change reaches every agent that depends on it, with a record of who approved it.
- [03]
Documentation alignment
A release changes an approval. The training decks, SOPs and help-desk articles that depend on it are updated, with owner sign-off.
- [04]
Insurance claims
Claims handled by agents that share one view of the policy, the customer and the evidence, so outcomes are consistent and every decision holds up in an audit.
- [05]
Five agents, one task
Agents in different AI systems quietly do the same work. One shared scope makes the overlap visible and coordinates it.
- [06]
An audit that holds up
Show what each agent did, in which scope, under which policy and who approved it. When the auditor arrives, the answer is a query, not a six-week reconstruction project.
You know what your agents cost. Do you know what they did?
Agents re-fetch the same pricing policy, the same customer record and the same rules, again and again. Stop the re-fetching and you change the unit economics of enterprise AI.
- 5×Projected rise in inference cost per agentic workflow through 2028 (Gartner).
- 95%AI pilots that fail to produce financial impact (MIT NANDA).
- 98%Enterprises with unsanctioned AI use across departments (ISACA).
- 3×The cost savings when AI is cross-functional rather than siloed (G2 / Salesmate).
- 2Open standards. MCP and A2A, the Linux Foundation guardrails GUST coordination is based on.
- 4People who sign off. CIO, CTO, COO and CFO, from one record.
Built for the four people who must sign off on enterprise AI.
- CIO
Trust your agents before you scale them.
You cannot put agents into production if you cannot see what they know, what they did, why they acted, or under which rules.
- Prevent uncontrolled agent sprawl across departments
- Enforce policy and data boundaries at the scope level
- Audit-ready records of every agent action
- EU AI Act high-risk system readiness from day one
OutcomeAgents you can stand behind - CTO
Coordinate any agent. Replace none.
Your teams have invested in LangChain, CrewAI and OpenAI agents. GUST does not ask you to replace them. It gives them a shared context graph, a governance layer and a memory model they can all bind to.
- MCP and A2A protocol support (Linux Foundation standards)
- Any agent, any framework, can bind to a scope
- Standard integrations for JIRA, Slack, CRM, ERP
- Persistent agent memory within scope-governed boundaries
OutcomeAgents in production - COO
Decisions propagate at the speed of AI, not the speed of meetings.
When marketing changes a campaign, when engineering hits a breakthrough, when compliance updates a policy: every cross-functional decision is re-evaluated, every affected team is told why, every action is logged.
- Cross-functional change propagation, with governance
- Configurable rules per scope and per change type
- Real-time visibility into cross-team implications
- Decisions traceable from trigger to outcome
OutcomeOperations that stay aligned - CFO
Make AI spend visible, attributable and defensible.
Token costs grow without accountability. AI investments do not map to outcomes. GUST attributes cost by scope, traces decisions to outcomes and structures context so agents receive only what they need.
- Cost attribution by scope, team and project
- Measurable impact chains, trigger to outcome
- Token optimisation via scope-based context filtering
- Per-user-per-month TCO model on request
OutcomeToken-ROI you can prove
One governed system, not another AI island.
GUST's coordination is a horizontal enterprise AI infrastructure: the context and control layer that makes fragmented AI systems work together as one governed system. What sets it apart is the combination: one organisational context, vendor neutrality and governed change propagation, in one layer.
| Topic | Current AI systems | With GUST |
|---|---|---|
| The big picture | Current AI systemsDo not understand the business from strategy down to operational projects. | With GUSTOne organisational context, from strategic goals to operational projects. |
| AI islands | Current AI systemsDo not connect the AI islands in your organisation. | With GUSTEvery AI system works from the same shared, normalised context. |
| Dependencies | Current AI systemsFragmented external and third-party components, single-stack lock-in. | With GUSTModel, framework and platform neutral; switch at message and task level. |
| Change | Current AI systemsMostly static multi-component ontologies with memory windows. | With GUSTInstant propagation to the relevant agents, within permissions and privileges. |
| Collaboration | Current AI systemsNo seamless agent and human collaboration across systems. | With GUSTAgents and human users as participants, under the controlled orchestrator-as-a-service. |
| Building on it | Current AI systemsNo development platform with full knowledge of the business. | With GUSTAn SDK for in-house and third-party applications on the shared context. |
| Measurement | Current AI systemsNo data insights from operations and execution. | With GUSTEvery action logged, so performance and cost are measured against outcomes, and made available as best practice insights. |
| Sovereignty | Current AI systemsData ownership tied to the vendor and its infrastructure. | With GUSTEuropean data sovereignty and full company data ownership, whatever the infrastructure. |
One place to integrate agents across your platform.
Your interface uses GUST components through the GUST client. Your services keep their own business logic and call the GUST SDK. Behind it, the GUST API runs the agents, knowledge retrieval and model orchestration.
- Step 1: From your interface to your services.
- Step 2: From your business logic to the GUST SDK.
- Step 3: From the SDK to the GUST API.
Three ways to deploy the GUST coordination layer.
Delivered directly and through our partner system integrators and transformation consultancies. Reach out to discuss the right fit for your team and infrastructure.
- [01]
GUST Core
Coordinated agents for business teams.
A chat interface and direct integrations with CRM, HR, marketing and workflow platforms. Designed for the non-developer interfaces that enterprise coordination requires.
- Best for
- Marketing, HR, finance, operations, customer-facing teams
- [02]
GUST SDK
Embed coordinated agents into your own applications.
A programmable way to add scopes, change propagation, memory and audit to existing products and internal systems, and to build in-house or third-party vertical business applications on top of the platform.
- Best for
- Platform teams, SI partners, enterprise engineering
- [03]
VS Code Extension
Coordinated agents inside the developer workflow.
Run GUST-powered coding agents directly in VS Code for code generation, refactoring, review, documentation, QA and DevOps tasks. Where the platform first proved out, with scope boundaries and audit trails built in.
- Best for
- Developers, engineering leads, QA, DevOps teams
Working architecture, standards-aligned.
GUST is in beta operations and growing each day. The scope engine is running, the graph model is proven, and the architecture is built on the open standards now governing the agentic stack.
- 7
Typed graph edges. CONTAINS, HAS_PROMPT, DEPENDS_ON, RELATED_TO, STARTS_AT, INCLUDES, OWNED_BY. Neo4j-backed, with non-breaking migration from existing hierarchies.
- 10+
Agent types running. Chat, developer, planner, memory crawler, scope manager. All inside the scope governance model, with three-tier memory.
- MCP + A2A
Standards-aligned. Built on Linux Foundation AAIF standards. Framework-agnostic by architecture, not by marketing claim.
- Alpha
VS Code extension live. Where the platform first proved out. SDK in build. Enterprise integrations in conversation with selected design partners.
You cannot supervise what you cannot see.
Every scope change, every propagation decision, every agent action is logged with who, what, when, why and under which rule. Built for EU AI Act high-risk system requirements from the start. When the auditor arrives, the answer is a query, not a six-week reconstruction project.
- EU AI Act high-risk readiness
- Full decision history
- Owner approval flows
- Per-agent audit trail
- Agentic AI
- Scope graph
- Change propagation
- Persistent memory
- MCP
- A2A
- EU AI Act
- Audit trail
- Token-ROI
- Production-grade
Questions, answered.
GUST is the coordination layer for enterprise AI: the organisational context graph that connects the AI islands inside your company. Every agent runs inside a scope it cannot leave. Memory persists, changes propagate with governance checks, and every action is logged.
Organisations that have implemented, are implementing or plan to implement AI tools, but cannot make them work together as one system. GUST accelerates the rollout with one shared, normalised knowledge base with reasoning capabilities.
Most platforms want to hold your data and operations in their own stack. GUST coordinates the AI systems you already run from one shared organisational context, with vendor neutrality and governed change propagation in one layer, while each agent keeps its own identity.
You do. GUST is built for complete company data ownership and European data sovereignty, regardless of the infrastructure it runs on, with no model, framework or platform lock-in.
No. GUST coordinates the agents and AI systems you already run. Any agent in any framework binds to a scope: remote agents take part over A2A, tools connect over MCP, and nothing has to be replaced.
GUST is model, framework and cloud neutral. It works with LangChain, CrewAI, LangGraph, the OpenAI Agents SDK and custom agents, and with OpenAI, Anthropic or private models. Scopes are portable across providers, so there is no vendor lock-in.
A scope is a context boundary: a team, a project, a policy domain. It holds the context, memory and policies for one piece of work, inherits from the scope above it, and connects to other scopes through typed relationships such as depends-on, influences, subscribes-to and governed-by.
Token-ROI is not just token cost. It is knowing what your agents cost and what they did for the business: cost attributed by scope, team and project, and decisions traced to outcomes. Because agents share one scope, they stop re-fetching the same information.
Every scope change, propagation decision and agent action is logged with who, what, when, why and under which rule. GUST is built for EU AI Act high-risk system requirements from the start, so the audit trail exists as the work happens.
GUST is in beta operations and the developer platform is published. The VS Code extension is live, the SDK is in build, and enterprise integrations are in conversation with selected design partners. The developer platform is open to a small group of partners and select users: write to info@slimcloud.net.
GUST is built by SlimCloud B.V., a pre-seed company headquartered in the Netherlands. Three founders: Michael Halley (CEO), Jurmen Hughes (CTO) and Mika Alapiessa (CRO), with an advisory board from eBay, SentinelOne, Restless Marketing and Tenable. See the About page.
Move your agents from pilotto production.
GUST is opening access to a small group of alpha partners: teams who want to make agentic AI reliable, governed and useful in real workflows. We respond within two business days.