Roman Bordo

AI-assisted production / Workflow architecture

Workflows.

I design the operating layer behind AI-assisted creative work: source material, decisions, human/AI roles, review, QA, and reusable context.

Operating layer

Source to review
  1. 01 Source material
  2. 02 Decisions
  3. 03 Human / AI roles
  4. 04 Review + QA
  5. 05 Reusable context

Ways to work

04 current offers

Start with one broken workflow, or go directly to the operating layer that needs to be designed.

01 AI Workflow Diagnostic Find where one recurring AI-assisted workflow loses context, evidence, ownership, or review quality. 02 Context Systems Turn fragmented docs, chats, decisions, and source material into usable operating context for people and AI. 03 Decision Memory Turn meetings, calls, working sessions, and AI summaries into source-backed company memory. 04 AI-ready Brand Workflow Build governed brand context and reusable review logic for AI-assisted creative and marketing work.

Current studies

Proof / Working systems

Current proof and working studies across source-of-truth architecture, decision memory, creative brand context, and support knowledge.

01 Case study

LifeOS

Source-of-truth architecture / AI-assisted life and work

Context routingDurable memoryRaw evidenceReview loops

A working LifeOS system for keeping AI-assisted work grounded in source-of-truth architecture, context routing, durable memory, raw evidence, review loops, and system repair.

AI becomes more useful when it can reason against maintained operating context instead of scattered chats, notes, screenshots, and stale memory.

Read case
02 Case study

Proofline

Source-backed company memory from meetings, calls, and working sessions

DecisionsRationaleOwnersSource references

A source-backed workflow for turning meetings, calls, and working sessions into decision memory: decisions, rationale, owners, risks, open questions, review status, and source references.

Most meeting tools summarize conversation. The deeper workflow problem is preserving decisions and evidence so teams and AI systems can reason from the same source of truth.

Read case
03 Working study

AI-ready Creative Brand Workflow

Turning brand assets, taste, and review logic into reusable AI context

Brand assetsTasteReview logicReusable context

An AI-ready creative workflow for turning brand assets, guidelines, tone, visual examples, taste, and approval logic into reusable AI context.

Structure do and don’t patterns, review loops, source examples, and decision logic so creative AI output can be evaluated against maintained brand context instead of loose preference.

Diagnose this workflow
04 Working study

Support Knowledge Architecture

Source-of-truth, escalation, and review logic for AI-assisted support

Canonical answersPolicy boundariesEscalationHuman review

A support knowledge architecture for source-of-truth answers, policy boundaries, edge cases, escalation paths, human review, and feedback loops before support automation.

Define canonical answers and route failures back into the knowledge base so AI-assisted support has clear source material, review states, and escalation logic.

Diagnose this workflow
Start here Start with one broken workflow. AI Workflow Diagnostic Find where one recurring AI-assisted workflow loses context, evidence, ownership, or review quality. Diagnose one workflow