DistributionOS
Turns trusted source material into grounded, reusable content without losing voice.
An AI-enabled editorial workspace for builders, founders, and small teams that keeps research, positioning, approved claims, drafting, editing, visual assets, publishing, and review inside one evidence-aware workflow.
Product architecture, retrieval workflow, AI interaction design, and analytics design
In development
Replaces scattered ideas and manual drafting with a source-grounded editorial workflow.
Current system design
What the system is meant to change.
For
Builders, founders, and small teams with notes, research, positioning, and past writing that should become a repeatable distribution system.
Replaces
Most AI writing tools start from a prompt and produce generic drafts. Builders and small teams have scattered notes, research, positioning guidance, and past writing, but no reliable way to reuse that context without losing evidence or voice.
Produces
A repeatable editorial workflow producing source-grounded drafts, reusable voice rules, platform-ready content, supporting visuals, and a measurable path from source ingestion to repeat use.
Make context a maintained product layer rather than a longer prompt.
DistributionOS ingests notes, briefs, uploads, links, and saved signals into a searchable source library with safe-to-share controls. Project profiles define audiences, positioning, approved claims, themes, and writing constraints. Retrieval supports grounded drafting, comparative editing, style learning, visual generation, publishing, and performance review.
Trusted sources
Grounded drafting
Publish and review
Behavioural product loop
Design constraints
- 01Respect safe-to-share boundaries when retrieving private source material.
- 02Keep generated claims traceable to supporting context.
- 03Capture the difference between an AI draft and the final human edit.
- 04Measure the full behavioural loop without turning early usage into unsupported product claims.
Engineering decisions
- 01Privacy-bounded retrieval: Every source carries an explicit sharing boundary before it can enter a generation context.
- 02Project context as a contract: Audience, positioning, approved claims, themes, and writing constraints remain reusable across drafts.
- 03Comparative editing: Original and edited versions remain side by side so recurring changes can become explicit voice rules.
- 04Behavioural loop: Source use, drafting, editing, export, and return behaviour create a measurable product workflow.
System behaviour and reliability
- 01Trusted sources are ingested with explicit safe-to-share boundaries.
- 02Project profiles constrain audience, positioning, claims, and writing rules.
- 03Retrieval selects relevant evidence before drafting begins.
- 04Comparative editing captures differences between generated and final writing.
- 05Publishing and review events form the behavioural analytics loop.
- 06Private sources must remain outside prompts unless explicitly allowed.
- 07Generated claims require traceable source context.
- 08Draft, edit, and publishing states remain recoverable independently.
Evaluation approach
- 01Measure retrieval relevance and source coverage.
- 02Track unsupported claims and generic phrasing.
- 03Evaluate activation, draft completion, return usage, and time to first useful output.
Interface views for the workflow and its decisions.
Design representation — replace these views with production screenshots as the product matures.
Source context and drafting remain visible together instead of disappearing behind a single prompt box.
Generic opening
The draft states the idea but loses the source detail and Allen’s direct tone.
Evidence-led opening
The final version begins with the observed workflow problem and keeps the supporting source attached.
A proposed comparison view for turning repeated human edits into maintainable writing rules.
What exists now, what remains limited, and what comes next.
This separation is intentional: planned evaluation and reliability work is not presented as completed evidence.
Current evidence
Limitations
- 01The product is in development and has no public live environment.
- 02Style-learning and performance loops require real repeated usage before evaluation.
Next validation
- 01Validate retrieval quality against a fixed source-and-claim test set.
- 02Measure how editing behaviour changes reusable style rules.
- 03Define activation and retention events before presenting product-performance conclusions.
Send the current workflow, source material, report, or product idea.
A polished specification is not required. The rough version is enough to establish the problem boundary and useful next step.