Service
AI Systems and Operational Infrastructure
Build dependable data, model, review, and deployment workflows for teams that must operate beyond a prototype.
LLM orchestrationData pipelinesSource trackingHuman reviewOperational monitoring
Build the whole operating loop
We design the path from source material and permissions through AI processing, human review, publication, and ongoing improvement. The goal is a system that can be understood and operated by its owner, not a hidden model call inside a demonstration.
Separate work into testable stages
Complex analysis is divided into bounded steps with defined inputs, outputs, and checks. This makes failures easier to diagnose and lets teams improve one part of the workflow without rebuilding everything.
Keep evidence and review close to the output
Where factual accuracy matters, source references and review status travel with the result. Low-confidence items and exceptions can be routed to an accountable reviewer before publication or use.
Prepare for operation
Deployment, access control, logging, monitoring, recovery, and content administration are included when the service requires them. The appropriate architecture depends on risk, data sensitivity, traffic, and the team that will own the service.
This approach informed the live KBS Jeju election reporting system, where AI-assisted analysis, public information design, and editorial controls had to work together.
Intended outcomes
A reliable path from source material to reviewed output
Traceable decisions, exceptions, and source references
An operating model the owning team can maintain
Typical deliverables
System and data-flow architecture
Working application or workflow
Review controls, runbook, and handover documentation
Operating architecture
- 01
Inputs and requirements
Clarify working materials, data, access, and review ownership.
- 02
AI workflow
Connect models, tools, sources, and validation rules.
- 03
Human review
Route consequential outputs and exceptions to accountable reviewers.
- 04
Operate and improve
Make deployment, logging, monitoring, and change management routine.
Who this is for
Service ownersEngineering teamsEditorial and review teams