Private intelligence.Clear boundaries.
- Hosting
- Customer-controlled, dedicated cloud, or on-premises
- Boundary
- Defined per deployment, before implementation
- Retrieval
- Permission-checked, with source references
- Assurance
- In progress — no completed audit
Bring AI into your environment.
Useful AI should fit the boundaries your organization needs.
Artifact Intelligence can build internal AI systems, privately hosted language models, and retrieval-augmented generation systems around your organization's security requirements. We design these environments to give you greater control over where information is processed, who can access it, and how it is used.
We can design and deploy private AI environments within customer-controlled infrastructure, dedicated cloud environments, or on-premises systems, depending on operational requirements. That can include an internally hosted large language model, a private knowledge assistant, or an intelligence layer connected to selected institutional systems.
For deployments that require it, the architecture can be designed to keep model inference and knowledge retrieval within an agreed environment, without sending prompts or retrieved content to external model APIs. Supporting services, integrations, and network connections are reviewed as part of that boundary.
Make internal knowledge useful, with controlled access.
Approved sources
User permissions
Source references
Knowledge maintenance
RAG is an approach to using knowledge. Its security depends on the controls around ingestion, storage, retrieval, and generation.
Define the full data boundary.
A private model is one part of a private system.
Before implementation, we work with your team to establish how information moves through the complete environment.
Inside the agreed environment
- Document processing
- Embeddings
- Search indexes
- Prompts
- Responses
- Logs
- Backups
The design addresses:
- 01Where each component runs and which providers are involved.
- 02What information each component receives.
- 03Who can administer the system and access its data.
- 04How long information is retained and how it is removed.
- 05Whether any information may be used for training or fine-tuning.
- 06Which external connections are permitted.
Deployment-specific safeguards and responsibilities are agreed as part of the engagement.
Protect the people behind the information.
Learning and workforce intelligence can involve sensitive information about students, faculty, employees, and organizational activity.
Our approach starts with a defined purpose: what the system needs to understand, what information it requires, and who should be able to see the result.
We design for limited collection, appropriate visibility, and human review of consequential outputs. Where individual records are unnecessary, aggregated or de-identified information may better serve the question.
AI-generated answers and modeled pathways require evaluation. People remain responsible for decisions that affect learning, support, opportunity, and employment.
Advancing our security maturity.
We are moving toward a more formal, evidence-based security program alongside our technical capabilities.
Our development priorities include:
- Documented access policies
- Provider reviews
- Vulnerability management
- Incident response procedures
- Recovery planning
- Security testing
We are also evaluating the requirements and readiness work needed for independent assurance, including SOC 2.
This direction is a commitment to improving our practices. It is not a claim of a completed audit, certification, or available assurance report. Any achieved milestone will be identified with its scope and supporting evidence.
Licensing considered from the start.
Private deployment still requires careful attention to the terms governing the technology.
Model weights, software libraries, datasets, and commercial services can carry different requirements for use, modification, hosting, and redistribution.
As part of solution planning, we review the applicable licenses and provider terms for the intended use, identify restrictions, and clarify any ongoing subscriptions or licensing costs.
Ownership and usage rights for custom work are defined in the engagement agreement.
Start with your requirements.
Your IT, security, and procurement teams should have a clear picture of the proposed system before implementation.
Bring us your requirements for hosting, data access, retention, licensing, vendor review, or security assurance. We will explain what we can support today, what needs further work, and how those requirements shape the architecture.
Prefer email? Write to hello@artifactintelligence.co
Looking at the architecture itself? Explore Private AI →