Engineering
Keep environments separate, manage keys in one place, and switch approved models without rewriting business logic.
Solutions
Turn AI adoption into owned, repeatable work.
New API gives each team a practical route from one useful AI workflow to a shared operating model with clear access, integration, and review.
Built for the people doing the work
Different teams, one managed path.
Start from the work each group already owns, while keeping model access and operational context connected.
Data and ML
Move prototypes into repeatable workflows with approved access, visible usage, and a consistent integration boundary.
IT and Security
Set the approved access path centrally, review exceptions, and keep records ready for internal controls.
A practical rollout
Expand from one accountable workflow.
Adoption works best when each step has an owner, an observable result, and a clear decision for what comes next.
- 01
Choose one owned use case
Begin with a workflow that has a measurable outcome and someone responsible for its quality.
- 02
Connect the right team
Give developers, operators, or workspace users the access path that matches how they work.
- 03
Review before you scale
Use request records and usage context to improve the workflow, then extend the pattern with confidence.
What teams gain
Move faster without losing the operating picture.
A shared AI gateway helps every group contribute while the organization retains the context needed to guide adoption.
Clear ownership
Every workflow begins with a team, an intended outcome, and a defined access path.
Consistent integration
Compatible APIs let technical teams keep familiar request patterns as needs evolve.
Shared visibility
Usage and request records give the people responsible for rollout a factual place to review progress.
Start deliberately
Choose the next workflow your team can own.
Introduce New API where AI already has a useful job to do, learn from the first release, and reuse the operating path for the next team.
Adoption questions
Questions teams ask when turning AI into daily work.
A useful AI solution should fit the way teams already work, with a clear path from the first use case to a repeatable workflow.
Which teams can use New API solutions?
Engineering, product, operations, data, and support teams can use approved models through the access path that fits their work.
What kinds of work can teams connect?
Teams can connect assistants, analysis, extraction, search, image, and video workflows where the approved model capability fits the task.
Can teams keep the API formats they already use?
Yes. New API supports compatible request formats for supported model families, so teams can keep familiar integration patterns while using a managed access layer.
How can a team start without replacing existing applications?
Start with one approved workflow, route its requests through the documented endpoint, and expand after the request format, access path, and operating results are understood.
How should a team choose a model for a workflow?
Match the model family to the task, compare the published provider fields in the catalog, test representative inputs, and confirm the organization has approved the model.
What does a practical first rollout look like?
Choose one team and use case, issue an appropriately scoped key, set a quota, connect the compatible endpoint, and review the resulting requests and usage.
Can a solution expand after the first team succeeds?
Yes. Teams can add approved models, users, capabilities, and quotas as the workflow proves useful and the organization is ready to extend it.
Where can teams get help with a solution?
Contact New API for account, integration, billing, or rollout questions. The team can help clarify the documented path and the controls involved.