CloudGoA cloud knowledge graph.
Connect provider guidance with your infrastructure, dependencies, documentation, and internal standards. Give AI the relationships and constraints behind the task.
CLOUDGO CONTEXT
Your AI is capable.
Make it context-aware.
Give existing agents and copilots a reusable foundation of cloud knowledge and decision context. Help your team spend less time repeating the background and more time reviewing useful, traceable outputs.
For teams already using AI. API and MCP requirements are scoped through a technical evaluation.
YOUR SOURCE CONTEXT
Infrastructure & IaC
Architecture & runbooks
Standards & policies
Business goals & costs
CloudGo Context
Knowledge graph + context graph. One shared foundation.
YOUR EXISTING TOOLS
Internal AI assistants
Copilots & agents
Custom applications
Your own workflows
YOUR KNOWLEDGE SHOULD COMPOUND
What you know.
What you learn.
Ready for the next task.
A cloud recommendation depends on relationships, standards, and prior decisions. CloudGo connects that knowledge and its source history, then makes it reusable across the workflows you scope.
An evolving context graph.
Preserve conversations and recorded decisions alongside the knowledge that informed them. Carry lessons and priorities into later work, instead of starting a new chat from zero.
Evidence you can work with.
Inspect the source context behind recommendations, identify the inputs your team trusts, and refine priorities. Keep people in control of the review and decision.
BUILT AROUND YOUR ORGANIZATION
Give your AI
the missing business context.
A cloud recommendation needs more than provider documentation. It needs to understand the availability promise, the approved region, the delivery deadline, and the reason the system was built that way.
Ground it in your environment.
Combine infrastructure and architecture with the internal knowledge that gives them meaning.
Make the constraints part of the answer.
Make internal standards and business priorities explicit alongside technical considerations, so reviewers can assess whether the answer fits.
Keep the source context available.
Follow an output back to the information that informed it. Give reviewers a basis for checking assumptions and explaining decisions.
PROVE VALUE IN ONE WORKFLOW
Prove the fit. Then expand.
Choose the decision.
Choose a past assessment or a current AI workflow. Agree a baseline for delivery time, review effort, and source traceability.
Define the knowledge.
Agree the data sources, relationships, access boundaries, and evidence the workflow needs.
Evaluate the integration.
Run a comparison with your existing tools and the agreed context. Review output quality, effort, and usage before deciding where to expand.
ONE WORKFLOW. A MEASURABLE START.
Keep your tools.
Add the full picture.
Choose one workflow. Agree what better looks like, compare it with your baseline, and build from the results.
Plan your evaluation