Insights · Architecture & AI
Architecture & AI
Architecture choices, AI integration patterns, and review discipline.
Showing 4 of 14 articles
Same Slide, Three Renderers: What It Takes to Make AI Slides You Can Trust
We took four production slides from our AI-bubble explainer, wrote one machine-readable spec per slide, and rendered each spec three ways: a free-form AI image prompt, a precision spec-driven prompt, and a deterministic code template — on two image models, 84 renders, scored against a rubric declared before the first render. The free-form path fabricated a statistic on every model it ran on, including an invented '87% of historical financial crises' styled like real economics literature. The spec path eliminated every invented number. The template never had any to eliminate — and the honest cost accounting, including what the template really costs to build in Claude Code sessions, decides which to use when.
DemonstratesA decision-grade answer to a question every company shipping AI-generated visuals faces: when can you trust an AI-rendered chart or slide, and what does each level of trust cost? The honest accounting - including what the deterministic template really costs to build in AI-assisted engineering sessions - yields a concrete playbook: templates for recurring data-bearing series, spec-driven generation with a judge for one-offs, free-form generation only for imagery that carries no claims.
From Pilot to Production: The AI Pipeline We Actually Run
88% of AI agent pilots never reach production. If yours is stuck, the blocker is rarely the model — it is orchestration, governance, and cost. This is one of Comuvia's pipelines that shipped, reporting on itself: 23,000+ events classified, a self-graded forecast track record, $35 of inference to date.
DemonstratesThe orchestration, governance, and cost discipline that carried this pipeline past the 88% pilot-failure wall is the same design work Comuvia delivers for a client's own stuck pilot — so it ships instead of stalling.
The Comuvia Company AI System v1: The 12-Tier Stack We Run On
Choosing an enterprise AI platform in 2026 means composing 8–12 components, not picking one vendor. Here is an AI-managed organization's 12-tier reference stack — ten tiers in production, two named and reserved — why each was chosen, what it costs per month, and the three decisions it would revisit — published by the system it describes.
DemonstratesA costed, argued reference architecture a technology leader can test their own platform decision against before committing — the same per-tier criteria Comuvia applies to client stacks in advisory engagements.
Durable Execution for AI Agents: Temporal vs Inngest vs Restate vs Prefect
Most agent pilots don't die on model quality — they die the first time a five-step workflow meets a container restart. The four serious 2026 durable-execution substrates, the five decision dimensions that actually discriminate, and the choice running the AI-managed system that published this page.
DemonstratesThe same five dimensions, applied to your agent stack before you commit, catch the wrong-substrate mistake that usually surfaces only once the workload is live — when migrating costs a multiple of whatever the initial shortcut saved.