- Role
- Translated customer conversations and messy operations into scoped tools, scripts, and runbooks.
- Outcome
- Teams left with usable internal systems instead of a generic AI demo.
- Evidence
- Sanitized delivery material, workflow maps, dashboards, and handoff docs.
- Context
- Clients brought unclear processes, scattered data, manual steps, and broad AI expectations.
- Built
- Workflow maps, prototypes, dashboards, scripts, runbooks, and handoff docs.
- Stack
- TypeScript, Python, REST APIs, dashboards, forms, automation scripts, runbooks.
- Proof
- Sanitized delivery material; client details removed.
back home
AI adoption systems
Acelera: client workflows turned into tools.
Client work where the first job was understanding the operation, then shaping the smallest useful system around it.
case notes
The work starts before the tool.
Acelera projects started with messy client operations. The useful output was not a generic AI demo, but a clear workflow and a tool the team could run.
DiagnosisInternal toolsDashboardsRunbooksHandoff
what I focused on
- Find the real process before choosing the implementation.
- Separate what should be automated from what needed human review.
- Build small internal tools, dashboards, and scripts around that workflow.
- Leave runbooks and handoff docs so the system could keep operating.
fde signal
- Customer conversations.
- Unclear requirements.
- Data and API glue.
- Practical delivery over AI theater.