TriforAI Enterprise
Your company’s knowledge. Your controls. Your AI.
Build a governed AI environment around your organization’s data, permissions, systems, models, and infrastructure requirements.
Public AI knows the internet. Your business runs on private context.
Policies, procedures, contracts, technical documentation, customer records, operational history, and institutional knowledge are what make an AI system useful inside a company. They are also what make architecture, permissions, governance, and deployment choices important.
Give AI access to the right knowledge—not unrestricted access to everything.
TriforAI designs enterprise AI around approved sources, defined user roles, traceable answers, explicit provider boundaries, and the organization’s infrastructure requirements.
Private company knowledge
Connect approved documents and systems to a governed retrieval layer.
Source-backed answers
Return citations or source references so users can inspect the information behind an answer.
Model choice
Select hosted commercial models, privately deployed open-source models, or a controlled combination based on the requirement.
Access controls
Align AI access with user identity, organizational roles, source permissions, and approved use cases.
Auditability
Record important activity, decisions, sources, and administrative actions according to the project’s governance requirements.
Infrastructure control
Assess deployment inside customer-controlled cloud, private-cloud, or on-premises infrastructure.
Private retrieval, private models, and model training are different decisions.
Retrieval-augmented generation
AI searches approved company sources at request time and uses the retrieved information to answer. This is often the fastest path to useful company-specific AI without retraining a model.
Common starting pointPrivate model deployment
An open-source or otherwise deployable model runs inside infrastructure controlled by the customer or its approved provider. This can increase control but also adds hardware, operations, monitoring, and model-management responsibilities.
Infrastructure-dependentFine-tuning
A model is trained further on carefully prepared examples to change its behavior or improve performance on a defined task. Fine-tuning does not replace secure retrieval, permissions, or data governance.
Used when justifiedChoose where each layer runs.
| Layer | Managed option | Customer-controlled option |
|---|---|---|
| Application | TriforAI-managed environment | Customer cloud or on-premises environment |
| Source documents | Approved managed storage | Customer-controlled storage |
| Search and retrieval | Managed retrieval services | Private retrieval stack |
| Model inference | Approved hosted model API | Privately deployed model |
| Identity | Application accounts and roles | Customer SSO and identity provider |
| Logs and audit history | Managed application logging | Customer-controlled logging and monitoring |
The final architecture is documented before implementation. Using an external model API is not described as fully isolated or on-premises.
Automation should have defined authority.
Enterprise agents should operate within explicit permissions, budgets, source boundaries, approval gates, and audit requirements. High-consequence actions remain subject to human authorization.
Begin with the requirement, not a model name.
The enterprise assessment identifies the business use case, approved data sources, users, permissions, model requirements, infrastructure constraints, integration points, governance controls, testing requirements, and expected operating cost.
- Business outcome and success criteria
- Approved knowledge and data sources
- Identity, roles, and permissions
- Hosted versus private model requirements
- Cloud, private-cloud, or on-premises constraints
- Integrations and agent authority
- Logging, audit, retention, and deletion requirements
- Performance, availability, and cost expectations
- Human testing and acceptance process