Extending autonomous engineering from code to production.
GIIP is an autonomous AI engineering infrastructure platform that extends AI coding into deployment, cloud, databases, monitoring, incident response and continuous production optimization.
AI coding agents have made writing software increasingly autonomous. GIIP extends that autonomy past the point where most tools stop — into deployment, cloud, databases, monitoring, incident response and continuous production optimization.
AI can now write software. GIIP is building the system that keeps it running.
Early, real, paying traction.
3
Paying customers
¥500K
Monthly contract value per company
3
Active PoC engagements
¥18M
Annualized Revenue Run Rate
Annualized Revenue Run Rate is calculated from current monthly contract value (3 companies × ¥500K/month × 12) — it is a run-rate projection based on today’s contracts, not a confirmed or contracted annual figure.
Coding is becoming autonomous. Production engineering is not.
AI coding agents (Codex, Claude Code, Devin, GitHub Copilot, Cursor and others) have made writing and editing code dramatically more autonomous. But the work that happens after code is written — cloud provisioning, database operations, deployment, monitoring, incident response and performance tuning — still runs largely on manual, human-driven processes. As AI-generated code output grows, the gap between how fast software can be written and how fast it can be safely operated in production keeps widening.
Why this gap is opening now.
- 1
Coding agents are commoditizing code generation
As multiple credible AI coding agents converge on similar coding capability, the competitive differentiation is shifting downstream — toward what happens to that code after it is written.
- 2
Enterprises are generating code faster than they can operate it
Teams are adopting AI-assisted development faster than their operations capacity is growing, creating a widening backlog of deployment, infrastructure and reliability work.
- 3
Cloud, database and infrastructure operations remain expertise-scarce
Production-grade cloud, database and infrastructure engineering is still a scarce, expensive, and error-prone skill set when performed at human speed and human scale.
- 4
Production-grade AI execution harnesses are only now becoming feasible
The combination of capable models, structured execution frameworks and accumulated operational knowledge needed to run infrastructure work safely with AI has only recently become technically achievable.
GIIP closes the gap with a continuous, cyclical lifecycle.
GIIP does not treat deployment as the end of a project. It runs development, infrastructure and operations as one continuous loop — Forward Deployed Engineers (FDEs) and AI agents work through each stage, then feed what they learn back into the next cycle.
Business Requirement
GIIP FDE
Planning / Coding / Testing
Cloud / DB / Infrastructure
Deployment
Monitoring
Incident Response
Performance Optimization
Continuous Improvement
Business Requirement
Continuous Improvement feeds directly back into the next Business Requirement — this is a loop, not a one-way pipeline.
A full-lifecycle architecture, six layers deep.
GIIP’s orchestration and execution harness sits at the center of the architecture — coordinating models, tools and human oversight rather than depending on any single AI model or vendor.
GIIP’s orchestration and execution harness — not any single LLM brand — is the architectural center that makes the other five layers work together.
Where GIIP sits relative to existing tool categories.
GIIP is not positioned against any single vendor — it covers a broader span of the lifecycle than either coding-agent tools or DevOps/observability tools address individually.
| Category | Examples | Scope |
|---|---|---|
| AI Coding Agent tools | Codex, Claude Code, Devin, GitHub Copilot, Cursor | Coding-focused |
| DevOps / Observability tools | CI/CD, monitoring, incident management platforms | Handles part of operations |
| GIIP | Development + Infrastructure + Database + Deployment + Operations + Optimization | Full lifecycle |
Enterprise contracts, priced per company per month.
Current enterprise contracts: ¥500K / month per company.
What’s included in current contracts:
What compounds over time.
These are the areas we expect to compound as GIIP runs more production workloads — not claims about data scale or learning effects that are already established.
Production Engineering Knowledge
Judgment and procedures for handling real production incidents, scaling decisions and cost trade-offs, captured in a form AI agents can execute against.
Execution Harness
The orchestration, approval-policy and tooling layer that lets AI agents safely take action in customer cloud, database and infrastructure environments.
Feedback Loop
Each engagement — including incidents, performance issues and deployments — feeds structured learnings back into the harness and knowledge base.
Operational Dataset
A growing, structured record of how production environments actually behave under real operational conditions across customer engagements.
Built on production experience, not AI-generated generalities.
GIIP’s harness is grounded in real, large-scale production engineering experience — not just what an AI model can generate about best practices.
40M DAU
Experience running a service at 40M DAU scale
600K
Up to 600K concurrent connections
300M/day
300M transactions per day at scale
Petabyte-scale
Petabyte-scale storage operations
Large-scale
Large-scale delivery infrastructure
¥200M/yr
Roughly ¥200M/year in infrastructure cost reduction delivered
Technology background: AWS, Azure, GCP · SQL Server, Oracle, MySQL, Aurora, PostgreSQL, TiDB
Nearly 30 years of production infrastructure and database engineering experience.
See profile & track recordFrom expert knowledge to repeatable infrastructure
A single founder’s expertise does not scale by itself. GIIP’s work is turning that expertise into infrastructure the whole system can run on, not a dependency on one person.
Decoupling customer growth from engineer headcount.
The traditional services model scales cost and headcount linearly with customers. GIIP is built to scale differently.
Traditional model
- Customers ↑
- Engineers ↑
- Cost ↑
GIIP model
- Customers ↑
- FDE instances ↑
- AI workload ↑
- Human intervention becomes exception-only
The goal is to decouple customer count from engineer headcount over time — we have not yet measured a specific human-intervention rate, so we don’t state one here.
Frequently asked questions
What is GIIP?
GIIP is an autonomous AI engineering infrastructure platform that extends AI coding into deployment, cloud, databases, monitoring, incident response and continuous production optimization — run by Forward Deployed Engineers (FDEs) and AI agents under enterprise governance.
Is GIIP an AI coding agent?
No. GIIP is not a code-generation tool like Codex, Claude Code or Devin. GIIP starts where coding agents typically stop — taking generated or existing code through deployment, infrastructure setup, monitoring and ongoing production operation.
How is GIIP different from Codex, Claude Code or Devin?
Those tools focus on writing and editing code. GIIP focuses on what happens after code exists: cloud provisioning, database operations, deployment, monitoring, incident response and performance optimization, run as a continuous lifecycle rather than a one-time handoff.
What does GIIP automate after coding?
Deployment, environment configuration, cloud and database operations, monitoring, incident response, and continuous performance and cost optimization — the full lifecycle after code is written.
Can GIIP operate production infrastructure?
Yes. GIIP FDEs and AI agents provision, deploy and operate customer cloud, database and infrastructure environments under an approval policy where high-risk actions require review.
Does GIIP manage databases and cloud infrastructure?
Yes. Database operations and cloud/infrastructure operations are both part of GIIP’s current enterprise contracts, alongside deployment, monitoring and incident handling.
Is GIIP an AI DevOps or AI SRE platform?
GIIP overlaps with DevOps and SRE work — deployment, monitoring, incident response — but its scope is broader: it also covers development assistance, database operations and cloud infrastructure as one continuous, full-lifecycle service rather than a single point tool.
Is GIIP already used by paying customers?
Yes. GIIP currently has 3 paying customers under enterprise contracts at ¥500K per company per month, plus 3 active proof-of-concept engagements.
What is GIIP’s business model?
GIIP is sold as a monthly enterprise contract, currently ¥500K per company per month, covering deployment, environment configuration, development assistance, infrastructure operations, database operations, monitoring, incident handling and AI response quality tuning.
Where is GIIP based?
GIIP Co., Ltd. is based in Seoul, Korea, with operations extending into Japan. Full legal entity details are on our company page.
What market is GIIP targeting?
Enterprises adopting AI-assisted software development that need the deployment, infrastructure, database and operations work after coding handled with the same level of automation as the coding itself.
How can investors contact GIIP?
Use the investor contact form on this page, or reach us directly at contact@littleworld.net.
For Investors
We share traction, architecture and roadmap details directly with interested investors. Reach out to start a conversation.