API and application integration
Implement request handling, structured responses where appropriate, authentication boundaries and recoverable failure behavior.
OpenAI
OpenAI integration with application-level control.
Integrate OpenAI APIs into defined product and business workflows. Design the surrounding retrieval, permissions, tool execution and evaluation so the model serves a task with clear acceptance criteria.
Platform architecture
The application controls what the model can do
A model response is an input to application logic, not permission to change a business system. This reference architecture keeps context selection, model access and action validation behind boundaries your team controls.
Application and identity
Authenticate the user and establish the task and permissions before requesting model assistance.
Boundary: User intent and allowed actions
Context and retrieval
Select approved records or documents and preserve their access constraints in the context passed to the model.
Boundary: Permitted data and source provenance
Model interface
Send the bounded request through the selected API with explicit output expectations, timeouts and error handling.
Boundary: Model configuration and usage limits
Validation and feedback
Validate output and proposed tool calls in application code; record relevant examples for evaluation and review.
Boundary: Authorized execution and acceptance evidence
Across the system
- Server-side credentials
- Context permissions
- Action validation
- Versioned evaluations
Before choosing the stack
Decisions worth making early.
- How do we choose a model?
- Compare candidates on representative tasks using quality, latency and cost criteria. Record failure cases and operational constraints rather than selecting solely from a model description.
- Does retrieval remove incorrect answers?
- No. Retrieval can provide relevant context, but source selection, model behavior and response validation still need evaluation. Define fallback and human review for unsupported or consequential outputs.
- Can a model call business tools directly?
- The application should validate arguments and authorization before executing a proposed action. Sensitive writes need confirmation or review appropriate to the workflow.
Vendor documentation informs platform selection; it does not imply a vendor partnership or certification.
Before you commit
Is this the right engagement?
Model and API capabilities integrated into applications with approved context, controlled actions and task-specific evaluation.
- What we need from you
- The target task, representative examples, approved data sources, application interfaces and requirements for data processing, review and latency.
- How you accept the work
- Evaluate representative successful and failed tasks, validate permission boundaries and tool execution, and document unresolved limitations before release.
- Scope & alternatives
- This page covers OpenAI-specific integration choices. AI Consulting compares wider approaches; AI Development covers the complete application delivery engagement.
Capabilities
What we can implement with OpenAI
Select the relevant work after reviewing your existing environment. The proposal records deliverables, dependencies and ownership.
Retrieval and business context
Connect permitted data with source tracking and refresh rules, and evaluate whether retrieved context supports the user’s task.
Controlled workflow actions
Define tool interfaces and application checks for authorization, validation, duplicate requests and human approval.
Evaluation and operation
Version representative test cases and track task quality, latency and usage as prompts, models and application behavior change.
Frequently asked questions
Questions about OpenAI
01Is an OpenAI API integration the same as a ChatGPT subscription?
Is an OpenAI API integration the same as a ChatGPT subscription?
No. An application integration has its own API access, configuration, usage and operating responsibilities. Consumer or workplace ChatGPT access should not be assumed to cover application API usage.
02Should we fine-tune or use retrieval?
Should we fine-tune or use retrieval?
Start with the problem. Retrieval supplies task-relevant information; fine-tuning changes learned behavior for a supported training approach. Evaluate simpler prompting and retrieval before deciding that training is necessary.
03How do you evaluate an OpenAI application?
How do you evaluate an OpenAI application?
Use representative inputs and explicit task criteria, including incorrect answers, invalid tool requests and escalation behavior. Compare versions and preserve the evaluation assumptions alongside the results.
04Can confidential data be used in the workflow?
Can confidential data be used in the workflow?
First establish permitted processing, retention, access and provider requirements with the responsible owners. Minimize context and logs, then verify the selected configuration against those requirements before sending data.
05What happens when a model or API changes?
What happens when a model or API changes?
Keep integration boundaries explicit and retain regression examples. Assess availability and compatibility for the selected model or interface, and evaluate changes before promoting them into a production workflow.
Related
Choose the next delivery step
Start a conversation
Make the next OpenAI decision with a clear scope.
Bring the current architecture, the constraint and the outcome you need. We will identify the next useful increment and the evidence required to accept it.