Make business context usable
A model is only as useful as the context around it. Learn how to connect fragmented knowledge, preserve permissions, retrieve the right evidence, and safely act across operating systems.
Build the foundation
Start with the architecture and engineering patterns that turn scattered information into grounded, permission-aware context.
Download the whitepaper to learn why enterprise AI stalls without trusted business context, and how to build the foundation for AI that can be governed, measured, and put to work.
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RAG, fine-tuning, DPO, agentic RAG, long-context, prompt caching — the LLM customization stack got crowded. Use this decision guide to pick the right approach, then talk to metacto about implementing it inside your business.
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A unified context layer makes business data usable by AI by resolving objects, source priority, permissions, and evidence before a workflow asks the model to act.
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A production AI workflow needs more than prompts and tool calls. It needs a data model for cases, actors, context, decisions, actions, approvals, outcomes, and audit events.
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The AI context layer is the operational architecture that turns scattered business systems into usable evidence for agents, workflows, and human decisions.
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A spreadsheet should become an AI workflow only when the process has repeatable decisions, trusted inputs, clear owners, and a measurable operating constraint.
Read the guideReady to act?
Explore the architecture, field guide, and technology patterns for giving AI reliable access to the knowledge behind the workflow.
Design the context, intelligence, and control layers behind a production AI workflow.
Learn more →Read the practical guide to making enterprise knowledge available at the point of action.
Learn more →Compare the platforms and infrastructure used to connect, govern, and run AI workflows.
Learn more →Explore context engineering
Go deeper on retrieval, knowledge graphs, permissions, integrations, memory, data quality, and safe system actions.
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