INTELLIGENCE INFRASTRUCTURE
The reusable systems around an AI-assisted task: organizational instructions, repository context, intelligence execution, and coordinated work.
Discuss your AI engineering infrastructure ↗Each system has a different responsibility. The catalog holds the knowledge. The Bridge delivers it. DevBuddy maps the software. Fabric executes intelligence. EDO coordinates the steps and results.
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Package standards, patterns, recipes, roles, and quality rubrics as versioned assets. Global Prompt Catalog organizes them; AI Dev Bridge brings relevant instructions into the tools where developers and assistants work.
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Deterministic repository discovery creates inventories and observations. Selective model-assisted enrichment adds understanding without requiring an expensive full re-analysis every time.
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Fabric separates an application’s intelligence need from provider, prompt, and configuration choices. Execution records make behavior inspectable; evaluation asks whether a candidate configuration is actually better. ModelGauntlet explores that comparison work.
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EDO connects steps, checks, progress, and recovery. The runner performs an action; the workflow records what succeeded, what failed, and what should happen next. Observed results guide continuation.
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Use interpretation, generation, and judgment where they add value. Once a procedure is understood, encode and validate what can be repeated. The aim is useful verified change per unit of effort and cost.
The underlying projects include working implementations and historical proofs. The shared architecture, portability work, and complete cross-product workflows are at different stages. Product pages describe those states individually.
A useful first engagement is a walkthrough of the relevant implementation, followed by a bounded pilot with explicit validation.
Inspect the source and evidence notes ↗MAKE IT CONCRETE