An AI-native
infrastructure company.

INTERTIE.ai 是一家 AI 原生基础设施公司，正在为生产环境中的 AI Agent 建设云。三项业务分别服务生产推理与 Agent 运行、企业后训练、多模型接入与路由，客户可按需要选择。推理与 Agent 平台工程是公司的内部能力建设方向，GPU 持有、运营和更新继续构成交付模式的一部分。开放模型按许可部署，闭源模型通过获准 API 接入。各项服务及集成平台仍在建设中，实际支持范围、设备权利、可用容量和服务承诺按项目验证及约定。

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.

在客户授权边界内运行模型与工具，管理执行隔离、状态、恢复及可观测性，按完整任务验证质量、延迟和成本。

Run models and tools within customer authorization. Manage execution isolation, state, recovery and observability. Evaluate quality, latency and cost for the complete task.

以获准模型和数据改善具体任务，建立基线与留出评测，经验证后按版本部署并保留回滚能力。

Improve task performance with authorized models and data. Establish a baseline and held-out evaluations, then deploy validated versions with rollback available.

通过统一接口管理模型与供给方选择、使用记录及结算。开放模型按许可部署，闭源模型通过获准 API 接入。

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.

INTERTIE.ai 负责客户关系、产品与平台工程、服务运营及所选 GPU 交付模式。客户确定任务、授权范围及验收标准；供给与交付伙伴承担合同约定的范围。公司设备权利与站点、设施等项目权利分别核实，不由统一服务入口推定。

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.

以真实客户工作负载组织产品、工程和交付。公司可持有、运营和更新 GPU，也可按客户及项目条件使用第三方供给；设施、设备与服务的具体权利分别核实。

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.

面向准备将 AI Agent 投入生产、改善专有任务表现或统一模型接入的产品与技术团队。现阶段客户拓展以美国需求方为重点，数据、模型和部署区域逐项目评估。

For product and engineering teams putting AI agents into production, improving performance on their tasks or simplifying model access. Current outreach focuses on US customers. Data, model and deployment-location requirements are assessed for each project.

拟议收入分别来自生产推理及 Agent 运行服务、后训练计算及评估适配服务、多模型接入的平台或企业服务。客户可独立购买其中一项。第三方 API 流水与平台保留费用、自有算力用量及确认收入分开计量；实际定价和收入确认取决于合同及履约角色。GPU 运营纳入能源、网络、支持、维护和更新成本。服务收入、续费和贡献利润仍需客户与运营证据验证。

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.

先明确需要生产运行、后训练或模型接入中的哪项服务，形成工作负载概要、技术适配判断与验证计划。记录任务、模型、工具、数据权利、质量、延迟、可靠性及成本目标，先交流非敏感信息。

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.

让一个地方真正拥有更多可靠的生产能力，并让建设它的人、使用它的人和当地社区都能长期受益。

Help places build more reliable productive capacity, so the people who build it, those who use it, and local communities can benefit for the long term.

让充裕、可靠的算力拓展人类创造的边界，让承载它的地方共享进步。

A future where abundant, reliable compute expands what people can create, and the places that host it share in the progress.

开发、持有、运营并持续更新 AI 基础设施与 GPU 算力，将可靠电力转化为客户可用的计算能力，并让当地社区长期受益。

To develop, own, operate and continually renew AI infrastructure and GPU compute, turn reliable power into useful computing capacity, and create lasting value for local communities.

让 AI 需求成为基础设施。

Where AI demand becomes infrastructure.

面向模型部署、优化、调度和 API 服务的建设方向。先验证模型质量、延迟、吞吐与单位工作负载成本。

Planned model deployment, optimization, scheduling and API services. Validate model quality, latency, throughput and cost per workload.

面向隔离执行环境、工具调用、状态管理和编排的建设方向。先验证权限隔离、失败恢复、可观测性及端到端工作流。

Planned isolated execution, tool use, state management and orchestration. Validate access isolation, recovery, observability and end-to-end workflows.

围绕工作负载规划 GPU、网络、区域与运行责任。将设备采购、权利、部署、维护及更新纳入项目交付。

Plan GPU capacity, networking, location and operating responsibilities around the workload. Connect procurement, rights, deployment, maintenance and renewal to project delivery.

确定服务范围、任务与数据权利，以及质量、延迟、可靠性和成本验收条件。

Select the service scope, task and data rights. Agree acceptance criteria for quality, latency, reliability and cost.

验证模型接入、执行与训练边界，检查隔离、恢复、版本管理和运行责任。

Verify model access, execution and training boundaries. Check isolation, recovery, version management and operating responsibilities.

依据已验证需求选择供给及专属 GPU，持续管理利用率、维护、服务成本和设备更新。

Select supply and dedicated GPUs against validated demand. Manage utilization, maintenance, service cost and equipment renewal over time.

先理解条件，把工程、合同和服务做扎实，减少仓促承诺造成的返工。

Understand the conditions first. Build sound engineering, contracts and services, and reduce rework caused by premature commitments.

明确责任、日期与服务条件，按约验收，及时处理故障并复盘。

Define responsibilities, dates and service conditions. Verify acceptance against the agreement, address faults promptly and learn from them.

分别记录所有权、开发容量、送电、部署、客户可用与收款，主动纠正过期表达。

Record ownership, development capacity, energization, deployment, customer availability and cash receipts separately. Correct outdated statements.

投资时同时考虑维护、设备更新、现金回收、人员能力与退出路径。

Consider maintenance, equipment renewal, cash recovery, staff capabilities and exit options alongside the initial investment.

在选址、建设及运营中落实当地就业、培训、采购、安全和资源影响管理。

Address local employment, training, procurement, safety and resource impacts throughout site selection, construction and operations.