OpenAI API Integration Services for Existing Apps

What adding OpenAI to an existing app actually involves

Most teams asking about OpenAI integration already have a working product with users, data and a release process. They want one or two specific capabilities added without destabilizing what works. A property management platform wants to draft replies to tenant maintenance requests. A B2B ordering app wants a buyer to paste an email from a customer and get a ready cart. A field inspection app wants a technician’s voice note turned into a filled checklist.

In each case Tepia adds three pieces to your system. A gateway service on your backend holds the OpenAI key, builds prompts from your data and enforces limits. New endpoints expose each AI feature to your web and mobile clients. A small admin view lets your team see requests, outputs and feedback. Your database, authentication and business rules stay where they are; the model works through them rather than around them.

Tepia scopes the first release to the feature where a wrong answer is easy to catch, such as a draft a person approves, and expands toward automated actions once the logs prove the outputs are reliable.

OpenAI capabilities mapped to real features

Tepia starts from the feature you want and works back to the OpenAI capability, not the other way round. This mapping covers most requests we see:

Structured outputs deserve special mention. When the model must return data in a defined schema, your code can reject anything malformed instead of parsing free text, which is what makes extraction features dependable enough to write to a production database.

A gateway that keeps the integration maintainable

Calling OpenAI directly from scattered places in a codebase works until the first outage, model change or abuse incident. Tepia builds a single gateway service that every AI feature goes through:

• Secrets: keys stored in AWS Secrets Manager, Azure Key Vault or Google Secret Manager and rotated on a schedule.
• Limits: per user and per account request limits so one heavy customer cannot exhaust your quota for everyone.
• Resilience: timeouts, retries with backoff on rate limit responses, and a graceful message when the service is unavailable.
• Logging: prompts, responses, latency and token usage stored with personal data redacted, so issues can be traced to a request.
• Versioning: prompts and model names in configuration, tagged per release and covered by regression tests.

On the client side, Tepia streams responses into React Native, SwiftUI, Kotlin or React interfaces so users see text appear within a second or two rather than staring at a spinner. Read how to add AI features to a mobile app without a rebuild for the client side detail.

Data handling with the OpenAI API

Security reviews usually ask the same questions, and Tepia answers them in writing during Discovery. OpenAI states that data sent through its API is not used to train its models by default, and it offers shorter retention options for eligible customers. That is a starting point, not the whole answer.

Tepia minimizes what leaves your system in the first place. Prompts are built from the fields the feature needs, not whole records. Names, account numbers and other identifiers are masked where the task allows it. Outputs are stored in your database under your existing retention rules, and admin access to logs is limited by role.

Regulated data needs an extra decision. If your app handles protected health information, Tepia confirms whether the required business associate agreement is in place with the provider you choose, or routes the workload through a cloud agreement your company already holds, such as Azure. We also update your privacy policy language and App Store disclosures so users know when they are reading AI generated content.

How Tepia approaches OpenAI integration

Tepia applies its six phase process to OpenAI work, with a US based project manager running the engagement.

1. Discovery: a review of your codebase, APIs, data flows and security requirements. Deliverables are an Investigation Summary, an Interview Summary and User Stories such as a dispatcher turning a customer email into a scheduled job.
2. Design: wireframes for streaming output, edit and approve states, error states and feedback controls, built on your existing style guide.
3. Development and Testing: the gateway first, then each feature behind a flag. Test plans cover functional behavior, user acceptance and non functional checks for latency, rate limits and schema validation, run against a set of real sample inputs.
4. Training: hands on sessions for staff who review AI output and admins who read the logs.
5. Launch: staged rollout by account or percentage of users, with support reps monitoring the first weeks.
6. Support: model upgrades tested against your sample set, prompt tuning and new features as requests come in.

A single feature added to an existing app usually fits the short end of Tepia’s typical ranges: Discovery of 1 to 3 months and development and QA of 2 to 6 months. See our process for detail.

OpenAI direct, Azure OpenAI or another model

Calling the OpenAI API directly gives you the fastest access to new models and features, and it is the right default for most product teams. Tepia recommends other routes in specific cases:

• Your company runs on Microsoft and security requires private networking and Entra ID sign in for the model endpoint. Consider Azure OpenAI integration.
• Your app is hosted on AWS and you want model access under existing AWS agreements and IAM policies, which points to Bedrock.
• Your workload involves very long documents or a team preference for Anthropic’s models; see OpenAI vs Claude for business apps.

Because Tepia puts every call behind one gateway, the provider becomes a configuration choice. Several clients run one provider for chat and another for embeddings or extraction. Browse our work, then talk to Tepia about the feature you want to add first.

Frequently asked questions