Custom AI project
Design and build domain-specific AI workflows, prototypes, production applications, and integration layers.
AI delivery that reaches production
Discovery, architecture, RAG, LLM serving, GPU platforms, MLOps, and managed operations so Indonesian enterprises can move from pilots to accountable services.
AI, data, and platform services
RAG and chatbot implementation
Create retrieval pipelines, vector stores, prompt workflows, citations, guardrails, and conversational interfaces.
LLM deployment
Deploy open or commercial models with vLLM, GPU sizing, quantization choices, API patterns, and observability.
GPU infrastructure solution
Plan and implement cloud or private GPU environments aligned to workload, security, and operations needs.
Kubernetes AI platform
Build GPU-enabled Kubernetes platforms with scheduling, namespaces, RBAC, ingress, storage, and model-serving patterns.
MLOps pipeline
Enable repeatable model build, test, deploy, registry, monitoring, and retraining workflows.
Managed service
Support operational continuity, monitoring, incident handling, platform administration, and service improvement.
Training and enablement
Equip engineering, operations, and business teams with practical AI platform and GPU operations knowledge.
Frequently asked questions
What does an engagement typically include?
A typical engagement includes discovery, architecture, sizing, delivery planning, implementation, integration, security review, validation, enablement, and production readiness.
Can Professional Services support existing platforms?
Yes. Services can integrate with existing data, cloud, Kubernetes, security, identity, observability, and application environments where technically feasible.
How should we start?
Start with a focused architecture review covering business outcomes, users, source data, workload shape, integration, security, service levels, timeline, and operating ownership.