01Workflow example
Shared inbox triage
Classify incoming requests, assign an owner, prepare a grounded response draft, and keep a person in control of sending.
Outlook / Teams / Power Automate
Microsoft 365 workflow implementation
Make the work your team repeats run by itself.
We turn recurring work across Outlook, Teams, SharePoint, Power Platform, and Azure into reliable AI workflows—and stay responsible for how they run.
Start remotely with one process. Keep human approval where judgment matters.
Where to start - Your Microsoft tools are already there. The workflow between them is not.
Begin with work people already repeat every week. A narrow first scope reveals the real permissions, exceptions, and approval points without turning into a company-wide transformation project.
02Workflow example
Document review and approval
Move a SharePoint document through the right reviewers with version history, approval evidence, and a clear final decision.
SharePoint / Approvals / Power Automate
03Workflow example
Recurring management reporting
Collect updates in a consistent format, flag missing inputs, and generate a reviewable first draft for management.
Teams / SharePoint / Azure OpenAI
From one process to production - Small first scope. Full operational responsibility.
The work is not finished when a flow runs once. We design for ownership, security, failure recovery, and change from the beginning.
Customer request workflow
Microsoft 365
Request received
Outlook
Complete
Classify and draft
Azure OpenAI
Complete
Human approval
Teams
Needs review
Log and handoff
SharePoint
Waiting
Customer tenant permissions applied
- Discover. Map the real process, decision points, users, data, and cost of manual work.
- Pilot. Build a focused workflow with realistic test data, approval points, and success criteria.
- Deploy. Fit the workflow to tenant permissions, security policies, ownership, and production support.
- Operate. Monitor runs, resolve failures, measure adoption, and improve the workflow over time.
A focused starting offer - One workflow. Two weeks to working evidence.
The PoC answers one practical question: should this workflow move into production? The two-week window begins after scope, access, test data, and a customer owner are ready.
01
Current-state map
A shared view of the process, owners, decisions, and exceptions.
02
Working prototype
A Microsoft-stack workflow tested with agreed sample data.
03
Production decision
Test results, risks, ROI assumptions, and the next implementation scope.
The experience behind this studio
I built one AI workflow that had to keep working across 17 Microsoft repositories and 55 languages. Reliable Workflow began with a simple question: why should only translation benefit from that operating discipline?
Minseok Song
Microsoft AI MVP · Maintainer of Azure/co-op-translator · Builder of Localizeflow
These figures describe Co-op Translator and Localizeflow—engineering work completed before Reliable Workflow. They are not customer results from this service.
- 17
- Microsoft learning repositories using the translation workflow
- 55
- languages supported by Co-op Translator
- 8,000+
- translation tasks operated through Localizeflow
- Azure
- production hosting and distributed execution stack
Built for accountable automation - Automation should remove repetition, not responsibility.
- Customer-owned environment. Identity, permissions, and data policies remain centered on the customer’s Microsoft environment.
- Explicit approval points. Actions with customer, financial, legal, or security impact remain reviewable before commitment.
- Observable operations. Runs, exceptions, failures, and ownership stay visible after the workflow goes live.
Bring one repetitive process.
We’ll decide whether it is a good automation candidate, what a focused PoC should prove, and where human judgment must remain.