Private AI deployments that keep sensitive data where it belongs.
We help enterprises choose and run the right deployment model for AI — provider APIs with strict data terms, private cloud endpoints, or open-weight models in your own infrastructure — with identity, access control, logging and a clear operating owner.
- Deployment options assessment
- Reference architecture
- Enterprise AI gateway
- Private model hosting
- Operating model and runbooks
Where this helps
What we deliver
How it works
- 01
Classify data and use cases
We work with security and data owners to classify what data each AI use case touches and what controls it requires.
- 02
Choose deployment per use case
Not everything needs self-hosting. Each use case gets the least complex deployment that meets its requirements.
- 03
Build the platform
Gateway, identity integration, networking and logging are built in your environment using infrastructure as code.
- 04
Security review
Architecture review and testing with your security team before production data is connected.
- 05
Onboard teams
The first use cases move onto the platform, with templates that make the next ones faster to approve.
Design decisions we make with you
Hosted, private endpoint or self-hosted
Frontier models are often available only through provider APIs or cloud endpoints. Not every model can be self-hosted, and open-weight models trade some capability for control.
Data residency
Where prompts, outputs, embeddings and logs are stored and processed, and how that is enforced.
Access control
Integration with your identity provider so AI access follows existing roles, and retrieved content respects document permissions.
Capacity and cost
Self-hosted models need GPU capacity sized for peak load; API usage scales with traffic. We model both before you commit.
Governance fit
The platform produces the inventory, logs and approval records your [governance](/ai-services/governance) process needs.
Applications
Related capabilities
- Responsible AI & GovernancePractical ownership, review, privacy and audit controls that let teams ship AI without losing track of what it does.
- AI IntegrationConnect AI models and assistants to your CRM, ERP, databases, knowledge stores and APIs — with identity, logging and fallbacks that production systems need.
- MLOps & LLMOpsEvaluation, release, monitoring and cost control for predictive models and LLM applications once they are in production.
- RAG & Enterprise Knowledge SystemsAnswers drawn from your own documents and data, with sources shown, permissions respected and content kept current.
Questions buyers ask
Usually not. The most capable commercial models are generally available only through provider APIs or cloud endpoints. Open-weight models can be self-hosted and are strong for many tasks, and we test them against your requirements.
Only with fully self-hosted models. With private cloud endpoints, data is processed in your cloud tenancy under the provider's enterprise terms. We document exactly where data goes for each option.
Your team, us, or a combination. We provide runbooks and training, and can support operations under an agreed scope. See MLOps & LLMOps.
From the start. They review the architecture before build and test before production. Our approach is outlined on trust and security.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.