The AI Model Release Wave: 42 models in 30 days
Independent analysis of 42 AI releases in 30 days: agentic work, cost per task, open deployment, specialization, and enterprise control.
- Agentic AI
- AI governance
- Enterprise architecture
- AI economics
Topic guide
Field guides for turning strategy, current-state evidence, target capabilities, transition choices, and governance into coherent enterprise change.
13 curated insights
Decision context
Architecture earns its place when it exposes dependencies and trade-offs before delivery makes them expensive to reverse. These articles connect enterprise structure, context, platforms, transformation, and evidence to the decisions leaders must make.
Explore enterprise architecture advisoryIndependent analysis of 42 AI releases in 30 days: agentic work, cost per task, open deployment, specialization, and enterprise control.
AI security budgets are rising, but platforms hold a buying advantage unless specialists close a material control gap with low operational drag.
Graph engineering turns intent into a recoverable system of decisions, typed transfers, human authority, evidence, and reusable operating paths.
A neutral guide to publicly documented Canadian NVIDIA partner status, buying routes, provider capabilities, and evidence limitations.
A practical operating guide for turning enterprise strategy into coherent architecture, investable transition states, governance, and evidence.
A field guide to deciding when graph-enhanced retrieval is worth the extra machinery and how to pilot it safely.
A practical decision guide for determining when graph retrieval solves a real query-shape problem that vector search cannot.
Retrieval-augmented generation still serves search-shaped work, but agents need governed context across tools, permissions, workflows, memory, and state.
A bridge-building metaphor for invisible engineering work: guidance, quality gates, controls, documentation, and structural decisions.
Solution architects are often miscast as pre-sales engineers despite working at the intersection of business strategy, technology, and delivery.
The article argues that complex LLM work has moved from single "magic prompts" to chained, tool-using, validated workflows.
AI augmentation is more than prompts. The article presents context engineering as the discipline that makes effective human-AI work possible.
What yottabyte-scale data growth means for architecture, infrastructure, governance, and decisions leaders must make before capacity becomes critical.
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Bring the active choice, constraints, and available evidence. A short conversation can establish what must be decided and what still needs validation.