A cloud for agents
in production.
Building cloud services for production inference and agents, enterprise post-training, and model access and routing. Platform and services are in development. We start with the workload, then validate integration and production readiness.
Start a conversation →Workload validation and delivery
Validate the workload
Select the service scope, task and data rights. Agree acceptance criteria for quality, latency, reliability and cost.
Integrate and accept
Verify model access, execution and training boundaries. Check isolation, recovery, version management and operating responsibilities.
Scale and operate
Select supply and dedicated GPUs against validated demand. Manage utilization, maintenance, service cost and equipment renewal over time.
Three service areas
Choose production operations, task improvement or model access according to the workload. Each service can be scoped independently.
Production inference and agents
Run models and tools within customer authorization. Manage execution isolation, state, recovery and observability. Evaluate quality, latency and cost for the complete task.
Enterprise post-training
Improve task performance with authorized models and data. Establish a baseline and held-out evaluations, then deploy validated versions with rollback available.
Model access and routing
Manage model and provider choices, usage records and billing through a common interface. Deploy open models under their licences and access closed models through authorized APIs.
Services are in development. Discuss your workload →
Customer and delivery responsibilities
INTERTIE.ai leads customer relationships, product and platform engineering, service operations and its selected GPU delivery model. Customers define tasks, authorization and acceptance criteria. Supply and delivery partners perform their contracted scopes. Company equipment rights and site or facility rights are established separately.
Engineering the delivery plan
A project delivery plan connects the design basis to local execution and acceptance. The following framework describes our proposed approach. It does not represent completed facilities or a guaranteed construction programme.
Design basis
Record customer load, rack density, availability requirements and the boundary between facility and compute delivery.
A comparable delivery scope
Compare modular and site-built options on total installed cost. Include freight, local civil and MEP works, commissioning and maintenance.
Local execution
Identify local engineering, permitting, installation and commissioning responsibilities. Test the delivery programme against equipment lead times and site readiness.
Acceptance evidence
Define FAT, SAT, integrated systems testing and customer acceptance. Keep arrival, energization and service readiness as distinct milestones.
From power.
To useful compute.
Match customer services to compute, networking and the facilities they need. Equipment ownership and facility responsibilities are established for each deployment.
Establish the power basis.
Understand site rights, grid stage, power conditions and the engineering constraints that shape a project.
GPU deployment and cloud services are in development. Availability is confirmed project by project.
The technical delivery layers
Inference, execution and compute support the services. Model and data rights govern each deployment.
Inference
Planned model deployment, optimization, scheduling and API services. Validate model quality, latency, throughput and cost per workload.
Agent execution
Planned isolated execution, tool use, state management and orchestration. Validate access isolation, recovery, observability and end-to-end workflows.
Dedicated compute
Plan GPU capacity, networking, location and operating responsibilities around the workload. Connect procurement, rights, deployment, maintenance and renewal to project delivery.
Compute supply and operations
Real customer workloads guide product development, engineering and delivery. The company may own, operate and renew GPUs or use third-party supply where appropriate. Facility, equipment and service rights are established separately for each project.
Workload requirements
Translate accepted service requirements into GPU, network, region and facility needs.
Equipment and supply
Select company-owned GPUs or third-party supply. Confirm the equipment owner and deployment rights separately from facility rights.
Operate and renew
Manage reliability, utilization, support, maintenance and equipment renewal over the operating life.
The intended revenue model
Intended revenue comes from production inference and agent operations, post-training compute and evaluation services, and platform or enterprise services for model access. Customers may purchase each service separately. Third-party API spend, retained platform fees, owned-compute usage and recognized revenue are measured separately. Pricing and revenue treatment depend on contracts and delivery responsibilities. GPU operations include energy, networking, support, maintenance and renewal costs. Revenue, retention and contribution profit require customer and operating evidence.
Ownership comes with responsibility.
Capital must support maintenance, equipment renewal, skilled people and reliable service over time. Each project needs a clear allocation of delivery, operating and financial responsibilities.
Company materials
Plain answers
FAQWhat is INTERTIE.ai?
INTERTIE.ai is an AI-native infrastructure company building a cloud for AI agents in production. Our three service areas are production inference and agent operations, enterprise post-training, and model access and routing. Customers can choose the services their workloads need. We are developing inference and agent-platform engineering in house, with GPU ownership, operations and renewal as part of our delivery model. Open models are deployed under their licences and closed models are accessed through authorized APIs. Services and the integrated platform remain in development. Supported capabilities, equipment rights, capacity and service commitments are verified and agreed for each project.
What services are you building?
Building cloud services for production inference and agents, enterprise post-training, and model access and routing. Customers can choose each service independently.
How does post-training work?
We first establish a task baseline. Where training is appropriate and rights allow, we use authorized models and data, evaluate on held-out tasks and deploy a version with rollback.
How do you handle open and closed models?
Open models are deployed under their licences. Closed models are accessed through authorized provider APIs. API access does not establish model ownership or training rights.
Are the services available today?
Services and the integrated platform remain in development. Supported models, deployment readiness and acceptance criteria are verified before capacity, dates or service commitments are agreed.
How does infrastructure fit?
Real customer workloads guide product development, engineering and delivery. The company may own, operate and renew GPUs or use third-party supply where appropriate. Facility, equipment and service rights are established separately for each project.
How will INTERTIE.ai earn?
Intended revenue comes from production inference and agent operations, post-training compute and evaluation services, and platform or enterprise services for model access. Customers may purchase each service separately. Third-party API spend, retained platform fees, owned-compute usage and recognized revenue are measured separately. Pricing and revenue treatment depend on contracts and delivery responsibilities. GPU operations include energy, networking, support, maintenance and renewal costs. Revenue, retention and contribution profit require customer and operating evidence.
What can we discuss now?
The first discussion identifies the required service and produces a workload brief, a technical fit assessment and a validation plan. Describe the task, models, tools and data rights, with targets for quality, latency, reliability and cost. Begin with non-sensitive information.