Models
Governed catalog, deployment options, adapters, and regional availability.
Platform / AI
AI that stays inside the approved boundary
Talvium's AI architecture spans models, agents, knowledge, services, training, evaluation, safety, infrastructure, and edge deployment. Every model and service must declare provider, license, region, data-use terms, and lifecycle.
Capability roadmap
8AI capability domains
Region-awaremodel availability
Human authorityfor consequential actions
Capability set
AI lifecycle
Agents
Runtime, orchestration, actions, connectors, and human oversight.
Knowledge
Search, retrieval, vector services, RAG, and permission-aware context.
AI services
Document, language, translation, speech, vision, and multimodal understanding.
Machine learning
Workbenches, training, deployment, and MLOps.
Safety & governance
Evaluation, policy filters, observability, inventory, and responsible AI.
AI infrastructure
GPU capacity, inference, private AI, and availability.
Local & edge
Small models, local inference, and disconnected AI.
Evidence matrix
Availability record for every model
A model name alone is not enough to support a mission decision.
| Field | Published answer |
|---|---|
| Provenance | Provider, model, version, license, and retirement date |
| Data terms | Training use, retention, logging, and customer controls |
| Placement | Permitted regions, environments, and deployment modes |
| Evaluation | Quality, security, safety, and mission-specific findings |
| Operations | Capacity class, observability, incident path, and change notice |
Evidence note
Responsible by evidence
Talvium plans to map AI governance to NIST AI RMF and relevant management-system standards. A mapping or target is not a certification.
NIST AI Risk Management FrameworkNext decision
Start with the mission and the data boundary
Select the workload, model constraints, region, operator model, and human-approval points before choosing infrastructure.