The short answer
OpenAI and Claude implementation support covers the task, interface, data path, integrations, evaluation, and controls needed to use model capability in a defined workflow.
Model implementation
OpenAI and Claude implementation support
Specify the software, evaluation, and handover you need around the model service.
Cortez Lake
Define implementation support for OpenAI, Anthropic’s Claude, and other frontier models: task design, integration, evaluation, human review, release, and operating handover.
What this means in practice
Agree on deliverables and responsibilities with the implementation partner. Model-provider access, commercial terms, and the operation of your application are separate parts of the arrangement.
Bring H2H in when…
- A team has model access and needs a usable feature or internal tool.
- The task needs business-system integrations and human review.
- Engineering and operations need an implementation and handover plan.
Related H2H pages
Frontier AI deployment partnerExplore H2H services for implementing the agreed model task and supporting software.Frontier AI implementation checklistCheck that the scope covers data, integrations, evaluation, controls, and operation.AI-native product developmentDefine an AI feature and build it against agreed release criteria.
Specify the work the partner will deliver
Start with the task and the user. Agree on whether the engagement produces a decision, a scoped pilot, or a production feature. Then define the interface, integrations, evaluation, documentation, and handover needed for that outcome.
The statement of work should distinguish implementation responsibilities from the model service itself. Identify who provides accounts and access, who reviews the applicable provider terms, and who is responsible for ongoing usage and support.
Build the interface and data path around the task
Users need a way to complete their work: a review queue, a draft with supporting context, an internal assistant, or a controlled action. Define how they inspect the result, correct it, and continue when the model or an integration is unavailable.
Map the information the task requires and the permissions needed to obtain it. Implementation support should address that data path and its failure cases. Avoid treating a successful API call as evidence that the surrounding workflow is complete.
Agree on evaluation before release
Choose representative inputs, expected outcomes, known exceptions, and unacceptable failures. Decide who reviews results and what evidence supports the release decision. Include the time spent verifying or correcting output when assessing usefulness.
Plan re-evaluation when prompts, models, integrations, or workflow requirements change. The handover should tell the operating team how to run the agreed checks and when to escalate a result that falls outside the accepted limits.
- Define the quality and workflow measures the team will review.
- Test human approvals and failures in the integrations used by the feature.
- Assign ownership of evaluation, support, and subsequent changes.
Understand H2H’s role
H2H provides workflow design, engineering, integration, and governance support for teams implementing OpenAI, Anthropic’s Claude, and other frontier model capabilities. H2H is an independent implementation provider; references to these services do not imply a provider affiliation or resale arrangement.
The engagement is defined by the software and decisions your team needs. It should leave clear acceptance criteria, an account of remaining limitations, and an agreed division of operating responsibility. Tutela is considered when its supported controls fit the requirements.
Next step
Define the implementation you need around the model.
H2H helps scope the task, integrations, evaluation, and handover so your team can assess the work and the next release decision.