Model-as-a-Service packaging
Metered inference endpoints, usage guardrails, and operational monitoring when owning GPU estates is not the winning constraint.
MaaS
Inference
Operational lane
0-to-1 GenAI and Model-as-a-Service packaging: inference governance, residency-aware deployment, metered inference when GPU fleet ownership is not the goal, and architecture advisory tied to hybrid-cloud and on-prem constraints. GenAI and Model-as-a-Service programs for regulated buyers: inference governance, residency-aware deployment, metered inference when GPU fleets are not the constraint, and architecture that ties models to identity, networking, and storage already in production.
Service lines
Each offering maps to how we mobilize on the ground: scoped, documented, and aligned to procurement and operations.
Metered inference endpoints, usage guardrails, and operational monitoring when owning GPU estates is not the winning constraint.
On-prem, private cloud, or hybrid patterns matched to PDPA-class policy and air-gapped requirements.
Authentication, rate limits, audit hooks, and change control suited to GLC and agency workloads.
Enterprise Claude access provisioned through our authorized reseller program, with procurement-friendly documentation and deployment guidance.
Provisioned outcomes
Partner alignment
Authorized where it matters, flexible where it helps. Programs we commonly align in this lane:
Related capabilities
Knowledge base
Answers to the positioning, delivery, and vendor strategy questions that come up most often.
Selection is architected from data sensitivity, latency, and operating model: on-prem or private cloud when residency dominates; MaaS when metered inference and deployment speed outweigh running GPU fleets internally. Read GenAI and MaaS and validate infrastructure under Partners.
Evidence and partners
Validate OEM alignment on the partners page. Review published delivery evidence under Case Studies in Infrastructure.