01
Embedded delivery, not slideware
I get close enough to feel the bottlenecks with the team, then improve the workflow where the work actually happens.
Embedded delivery consulting · Agile + AI without theater
Inside the work. Sharper delivery. Useful AI.
I work hands-on with software teams that need to adapt faster without turning delivery into chaos: better feedback loops, clearer technical decisions, and AI leverage grounded in real work.
clean.dev/diagnose
$ run embedded-delivery --with-ai --no-theater
Checking delivery friction, decision latency, brittle architecture, and AI-readiness. Recommendation: join the real workflow, fix the system from inside, keep humans in judgment loops.
signal/01ok
AI does not fix broken process. It exposes vague ownership, slow feedback, and unclear interfaces faster.
signal/02ok
Agile means learning speed and adaptive execution — not ceremony theater or transformation theater.
signal/03ok
Technical depth stays in the room so speed does not quietly become production risk.
$ cat operating-model.md
A senior operator inside the delivery system.
The work combines team-level process, architecture, and AI-enabled workflows because those problems do not fail in separate boxes.
02
Decision quality under pressure
Architecture, ownership, trade-offs, and escalation paths become explicit so teams can move without guessing.
03
AI with judgment loops
Agents, personas, and workflow automation are useful when the team can steer them with context, review discipline, and clear boundaries.
$ grep -R "credible" ./experience
Built for complex organizations, not demo-only narratives.
20+
Years of Experience
19
Client Engagements
14
Companies
current proof vector: Douglas / enterprise delivery friction
The strongest current direction is persona-driven backlog shaping and workflow augmentation around tools teams already live in: Jira, Confluence, Azure DevOps, and the messy reality around them.
Signals from past delivery environments
$ less principles.txt
No consulting theater. No AI dogma. No commodity staff augmentation.
Useful change comes from inside the work. Detached recommendations often sound clean and fail on contact with organizational reality.
The goal is not to install a framework. It is to help a team learn faster, decide clearer, and ship safer while keeping technical credibility in the loop.
“Hands-on enough to feel the bottlenecks, senior enough to fix them.”
$ ./talk --fit-check
If your team is being asked to move faster with AI, but the delivery system is already noisy, we should talk.
I work selectively with engineering leaders and change sponsors who want practical improvement from inside real delivery — not another deck about transformation.