A confidential LLM — your data, your tenancy, your guardrails.
Deploy LLMs in your own cloud account or on dedicated infrastructure. PDPA and MAS aligned by design.
Key takeaways
- A private LLM deployed in your own cloud account — your data, your tenancy, your guardrails.
- No prompts or completions ever leave your environment. No training on your data.
- PDPA, MAS and sector-specific compliance designed in from day one.
- Reference architectures for AWS Bedrock, Azure OpenAI and GCP Vertex AI.
- Pilot typically delivered in 4–6 weeks before scaling to additional use cases.
When public AI isn't acceptable.
If your data, customers or regulators forbid sending sensitive information to overseas APIs — go private.
Data confidentiality
Your prompts and completions never leave your tenancy. No training on your data.
Compliance
PDPA, MAS and sector-specific regulations addressed by design — audit trails included.
Retrieval-augmented generation
Ground answers in your own documents, knowledge bases and approved sources.
Model selection
Open-source (Llama, Mistral) or commercial (Anthropic, OpenAI) hosted privately — your choice.
Security by design
Network isolation, IAM, encryption at rest & in transit, prompt-injection mitigations.
Reference architectures
Battle-tested patterns for AWS Bedrock, Azure OpenAI and GCP Vertex AI.
Who this is for.
From risk assessment to production.
A four-phase engagement that produces a working pilot, not just a slide deck.
- 1
Discovery & risk assessment
Data flows, threat model, regulatory mapping, model and platform selection.
- 2
Reference architecture
Network design, IAM, encryption, prompt-injection mitigations, audit logging.
- 3
Pilot
A small, well-scoped use case running in a private environment. End-to-end in 4–6 weeks.
- 4
Production rollout
Scale to additional use cases, teams and document corpora with appropriate governance.
Let's discuss your data and constraints.
We will sign an NDA before any sensitive details are shared. Initial conversation is free.
Frequently asked questions
- What is a private LLM and why would a Singapore SME need one?
- A private LLM is a large language model deployed in your own cloud account or on dedicated infrastructure, instead of a shared public API. Singapore SMEs go private when their data, customers or regulators (PDPA, MAS, sector-specific) forbid sending sensitive information to overseas APIs.
- Is a private LLM hosted in Singapore?
- It can be. The default deployment runs in a Singapore AWS, Azure or GCP region so prompts, completions and document corpora never leave Singapore data centres. Cross-border deployments are also possible if your data residency allows.
- Which models can be deployed privately?
- Open-source models (Meta Llama, Mistral, Qwen) for maximum control, or commercial models hosted in your tenancy (Anthropic Claude via AWS Bedrock, OpenAI via Azure OpenAI Service, Google Gemini via Vertex AI). The choice depends on data sensitivity, cost and capability requirements.
- How is a private LLM kept secure?
- Network isolation, identity and access management (IAM), encryption at rest and in transit, audit logging, prompt-injection mitigations and retrieval-augmented generation (RAG) over your approved document set are all designed in by default.
- How long does a private LLM pilot take?
- A focused pilot on one use case typically takes 4–6 weeks: discovery and risk assessment, reference architecture, pilot deployment, and validation. Production rollout to additional use cases follows the pilot.
- What about PDPA and MAS compliance?
- The default architecture is designed for PDPA, MAS and sector-specific regulations. It includes consent capture, retention rules, audit trails and data-residency controls. We sign an NDA before any sensitive details are shared.