AI Engineering
A useful AI feature needs more than a convincing demonstration. We help you build it into a business workflow, test how it behaves, and give the people using it a clear way to review the result.
What AI Engineering does
AI engineering combines model capabilities with application code, approved data, integrations, evaluation, and operating controls. It turns a model into a feature people can use inside a system with real permissions and responsibilities.
Where it fits
An illustrative use case might help a team find information, prepare a draft, or summarize material for review. We begin with the task and the quality standard, then consider whether AI adds value and where a person must remain responsible for the decision.
Explore Development technology, Development services, and Platform Security for the surrounding application and controls.
Define success in terms your team can test.
Choose a task worth solving
We identify the users, the work they are trying to complete, and the information the feature would need. We establish data boundaries, review requirements, and acceptance criteria before committing to a build.
This step also makes limitations explicit. If a wrong answer could interrupt work or mislead a user, the design needs a way to detect uncertainty, offer supporting information, and hand the task back to a person.
Treat permissions and failure handling as part of the application.
Connect the feature to the workflow
We design how the feature accesses approved information and interacts with services. The interface should make the action clear, respect existing permissions, and avoid giving a model more authority than the workflow needs.
We build the surrounding application behavior: input validation, tool boundaries, error handling, and review steps. Model responses are evaluated as application output rather than assumed to be reliable because they read fluently.
Use representative cases to see where the feature succeeds and fails.
Evaluate before and after release
We create an evaluation approach using examples of the work your team expects to perform, including difficult inputs and failure modes. That evidence guides revisions to the application, prompts, retrieval, and review process.
Deployment includes monitoring, consumption controls, and ownership for changes. Models, data, and business rules can change, so the team needs a repeatable way to check behavior when the feature is updated.
What you take away
You receive a use-case and application design, an evaluation approach, delivered components within scope, deployment guidance, and operating documentation. We identify the human review points and the evidence used to judge readiness.
Before we start
We review access to data, privacy requirements, model limitations, integration permissions, expected consumption, and support responsibilities. Source ownership, deployment environments, and the boundaries of ongoing support are established with the delivery scope.
Talk through your project
Tell us what you use today, what needs to change, and who will use and maintain the result. We can help you work out the next step.
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Discuss your project