AI NATIVE CONSULTING
AI Native Consulting
Design how people and AI work together, validate services against real work, and build the foundation to operate them reliably. Connect organization, product, and platform in one delivery flow.- Organization & workflows
- AI · RAG · Agents
- Platform & operations
WHY JJGO
Connect organizational change to products and operations.
AI Native takes clear ownership, evaluation against real work, and repeatable delivery. Drawing on product development, AI productization, and cloud platform operations, I help teams build a system they can keep running and improving.- 16 yrs
- Product engineeringFrom Daum, Kakao, and NCSOFT to AI product organizations
- ~2 months
- Product redesignReframed a product operated for more than two years as a new AI-based product
- About 200
- Kubernetes clustersDesigned, built, and operated through NCSOFT KaaS
- 1,000+
- Cluster nodesAcross AWS, GCP, OpenStack, and bare-metal environments
- 15+
- Cloud servicesIntegrated into an internal micro-frontend cloud portal
- Up to 80%
- Model lead-time reductionReduced the time to bring new AI models into service through standardization
Source: self-reported project records in Jungju Lee’s portfolio and résumé.
EXPERTISE
How I can help
Organization & Workflow Design
Define human and AI roles, decision rights, and review responsibilities. Embed reusable AI workflows in planning, development, review, testing, and knowledge sharing.
Real Work & Evaluation
Build evaluation sets from real questions, documents, and edge cases. Prioritize workflows using quality, cost, latency, and security constraints.
AI, RAG & Agent Delivery
Compare models, prompts, retrieval, and reranking. Connect task decomposition, routing, tool calls, failure handling, and human review into an operational service.
Developer Platform & Automation
Design shared environments and self-service paths around developer needs. Connect CI/CD, IaC, service catalogs, and Kubernetes where needed.
Quality & Operational Governance
Define data access, evaluation logs, monitoring, and SLOs. Manage quality regressions, incidents, deployment, recovery, and cost.
Adoption & Capability
Turn validated workflows into playbooks, training, and coaching. Use adoption, business outcomes, and delivery time to improve how teams work.
WHAT YOU GET
What we build together
AI Native operating model
An actionable roadmap covering principles, roles, data boundaries, approval and review criteria, and priorities.
Validated workflows & evaluation sets
Real-work scenarios, quality, cost, and latency criteria, AI and RAG configurations, agent flows, and reusable playbooks.
Repeatable delivery & operations
Reference architecture, development and deployment automation, observability, incident response, and cost management.
WHEN TO START
When to start
- 01
AI expertise stays with individuals and needs to become a shared way of working.
- 02
An AI or RAG proof of concept needs to become a dependable service for real work.
- 03
Inconsistent development and deployment practices create operational friction as AI adoption grows.
PROCESS
How we work
Scope and timeline are adjusted to your context.- 01Context & goals
Discover & prioritize
Review workflows, data, and delivery practices. Select one high-value workflow and agree on quality, cost, and latency criteria.
- 02Roles & architecture
Design the system
Define human and AI ownership, AI and RAG configurations, platform boundaries, and evaluation. Match scope to the current stage.
- 03Real-work validation
Build & pilot
Implement and evaluate the workflow with one team. Connect review, exception handling, and development and deployment automation.
- 04Continuous improvement
Operate & expand
Track quality, adoption, cost, and feedback. Expand validated practices through standards and training, and refine operating criteria.
DEVELOPMENT METHOD
Choose the setup that fits the goal and stage.
0→1, 1→10, and 10→100 involve different unknowns and operating responsibilities. Match the team, technology, and operational complexity to the current goal.Validate the core hypothesis
- Goal
- Use the smallest viable product to test the problem and core value.
- Team
- A small cross-functional group with product, design, and engineering working closely
- Technology
- Proven tools, managed services, and a simple application architecture
- Operations
- Short release cycles, essential telemetry, and direct user feedback
Build repeatable delivery
- Goal
- Turn validated value into reliable use and a repeatable delivery flow.
- Team
- Clear product ownership, adding specialist roles as needs emerge
- Technology
- Clear domain boundaries, automated tests and CI/CD, baseline observability
- Operations
- Basic SLOs and incident response, usage and quality metrics, gradual standardization
Operate at scale
- Goal
- Manage quality, speed, and cost as users and teams grow.
- Team
- Autonomous product teams supported by shared platform, SRE, and security capabilities
- Technology
- Split only where load or ownership boundaries are proven, with self-service platforms
- Operations
- Capacity, cost, and SLO management; policy automation; progressive delivery and recovery
The ranges describe stages of product validation and scale, not literal company size. The right setup depends on the domain, risk, and team capability.
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