Deadwater sells owned AI infrastructure, not outsourced content labor. Start with a focused workflow, build an agent-ready context layer, or go all the way to a full Content OS install.
Both offers begin with a scoping call and end with systems your team can run without renting your intelligence back from a vendor.
Package A
Automate a specific growth or content bottleneck with a focused production workflow designed to do one job well.
From $22,000
Typical timeline: 2-3 weeks
Includes
Best for
Package B
Turn scattered internal knowledge into an owned, agent-ready markdown system your whole team can use with Codex, Claude Code, and other AI tools.
From $25,000
Typical timeline: 2-4 weeks
Includes
Best for
Package C
Install the deeper operating layer that governs content production, publishing, and execution so multiple AI workflows can run safely.
From $50,000
Typical timeline: 4-6 weeks
Includes
Best for
To fix one painful bottleneck fast
To automate a specific content or research job
A faster, narrower engagement with clear ROI
Execution without changing the whole system yet
To turn scattered company knowledge into something agents can actually use
To make Codex or Claude Code dramatically more useful inside your business
To own a markdown-based command center instead of another hosted knowledge tool
A strong first infrastructure step before a bigger system install
To rebuild the actual content operating layer
To standardize publishing, schemas, and execution across the whole system
To replace brittle CMS and prompt workflows with governed infrastructure
Compounding leverage across content production, not just internal knowledge access
For teams that want iteration after launch.
$6,000-$12,000 / month
Not required. Most teams start with one build, then add support once the system is live.
A workflow build automates one defined job. A context layer makes your company legible to agents by structuring internal knowledge. A Content OS adds the full operating layer for content execution, governance, and publishing. They solve different depths of the same problem.
A context layer makes your company legible to agents. It extracts and structures internal knowledge so AI can reason with it. A Content OS goes further by governing how content is stored, executed, validated, and published. One is the knowledge foundation. The other is the operating system running on top of it.
Yes. The context layer offer exists specifically because most companies already have valuable knowledge trapped inside tools like Notion, docs hubs, workspace exports, and scattered internal files.
You do. The point is to leave you with an owned markdown-based asset, not lock your intelligence inside a vendor platform.
Yes. We package the system with skills, structure, and operating guidance so it can function like an internal command center, not just a developer toy.
That is exactly why the context layer offer exists. It is a strong standalone deliverable and a natural precursor to a deeper install later.
If your bottleneck is speed, context, or full-system execution, there is now a clean entry point for each.