AI Workflow Design
Design and deploy AI workflows across engineering, operations, product, and support. Task decomposition, quality gates, human-in-the-loop checkpoints, and measurable outcomes for every function.
The future of AI is local.
ctx is an AI transformation practice. We help organizations adopt AI across engineering, operations, product, and strategy — with local-first architecture as the default. On-premise models. Portable context. No vendor lock.
Thesis
The Local Thesis
The first wave of enterprise AI was cloud-first by default. Organizations sent their most sensitive data to third-party APIs, accepted opaque pricing, and traded control for convenience. That era is ending.
The next wave belongs to organizations that treat context as a durable asset. Portable, reusable context artifacts — prompt libraries, workflow specs, evaluation sets — survive each generation of models and tools. They compound in value instead of expiring with the next vendor pivot.
AI adoption is not an engineering-only problem. It spans operations, product, strategy, and culture. The common thread is architecture: local-first by default, cloud when justified, and never locked to a single provider. Models like Claude, GPT, and open-weight alternatives are interchangeable components — not dependencies.
Our thesis: the organizations that own their infrastructure and their context will outperform those that rent both. We exist to make that transition fast, safe, and repeatable.
What We Do
Four tracks, one engagement
Local-First Infrastructure
Architecture and deployment for on-premise, private-cloud, and air-gapped environments. Sovereign data, portable model routing, compliance-ready designs, and zero vendor lock-in.
Context Engineering
Build the durable context layer that survives model generations: prompt libraries, workflow specs, evaluation sets, and knowledge artifacts — all portable, all versioned, all yours.
Open Academy
Every engagement ships free, public learning content. Reusable prompts, labs, playbooks, and implementation checklists — all markdown, never paywalled.
Process
How we deliver
01
Discover
Audit systems, teams, data sensitivity, and the current AI footprint. Identify the highest-leverage use cases and local deployment readiness.
02
Design
Define the context layer, deployment model, governance framework, and success metrics. Local-first by default.
03
Ship
Run a 30-day transformation sprint with measurable milestones and working deliverables at every checkpoint.
04
Publish
Release open academy modules, harden workflows, and document the operating model in portable markdown.
Open Academy
Curriculum
Every module produces open artifacts: skill docs, playbooks, and implementation checklists.
01
Foundations — AI model basics, context limits, prompt engineering, tool use patterns.
02
AI Workflow Thinking — Task decomposition, orchestration, delegation boundaries, and risk design.
03
Local-First Deployment — On-premise models, security assumptions, data classification, and governance.
04
Context Architecture — Designing reusable context layers and workflows for engineering, support, operations, and sales.
05
Evaluation & Reliability — Output quality, hallucination control, test harnesses, rollback patterns.
06
Scale & Culture — Team operating models, centers of excellence, metrics, and capability ladders.