Can it answer from our own documents?

Yes, and this is the most common thing we build. Your content is indexed and the relevant passages are supplied to the model with each question, so answers come from your material rather than its training data, with sources attached for checking.

Is our data used to train their models?

Not through the API by default. Business API usage is excluded from training, which is a different arrangement from the consumer ChatGPT product. For regulated work we can also deploy through Azure OpenAI, keeping data inside your own cloud tenancy.

Can we switch AI providers later?

If it is built for that, yes. We keep prompts, retrieval, and application logic separate from the provider itself, so moving to a different model becomes a configuration change rather than a rebuild. It is worth insisting on from any vendor.

Do we need ChatGPT or the API?

Different products. ChatGPT is a subscription your staff use in a browser, which is worth having and requires no development. The API is what puts the same capability inside your own product for your customers. Most businesses eventually want both.

Custom ChatGPT
Integration Services

Stateside Digitals provides custom ChatGPT integration services, putting GPT models inside your product where they answer from your own content, follow your own rules, and hand off to a person when they should.

GPT integration means connecting OpenAI’s language models to your software through their API, so the capability people recognize from ChatGPT works inside your own product, on your own data, behind your own interface. No model is trained and nothing is built from scratch. The model already exists, and the work lies entirely in what you connect it to.

For a business, that changes the economics of adding intelligence to a product. What once needed a data science team and months of training now begins as an integration project measured in weeks. The difficulty moves from building a model to deciding what it may do, where its answers come from, and what happens on the occasions when it gets something wrong.

Live In Weeks

No Training Required

The model is already built, so a working feature can reach real users while a custom model would still be waiting on labelled data.

Works With Your Content

Answers From Your Own Documents

Responses are drawn from your policies, manuals, and records rather than the model’s general knowledge, which is what makes them useful.

Costs You Can Model

Priced Per Request, Not Up Front

You pay for what people actually use, so a pilot costs very little and spending scales only alongside genuine adoption.

Customer Support Assistants

Answering routine questions from your own help content around the clock, and passing anything unusual to your team with context.

Internal Knowledge Search

Letting staff ask questions of years of accumulated documents, policies, and records in plain language, instead of guessing which folder something was filed in six years ago.

Drafting Assistants

First drafts of proposals, reports, and replies in your house style, produced for a person to edit rather than sent unreviewed.

Language models produce fluent answers whether or not they know the answer, and this is the single thing most likely to turn a promising feature into a liability. A confident but incorrect reply about refunds, dosages, eligibility, or contract terms does more damage than having no feature at all, because people believe it.

So most of the engineering is constraint. We ground answers in your documents rather than the model’s memory, cite sources so a user can verify, detect when a question falls outside what the system should answer, and route those cases to a person. Where consequences are legal or clinical, we design for human review as the default rather than as a setting somebody can turn off.

We build against the OpenAI API using structured outputs and function calling so responses arrive in a form your software can act on rather than as loose text. Your content is embedded and stored in pgvector or a managed vector database for retrieval. Moderation, rate limiting, and per-user spend caps sit in front, and Azure OpenAI is available where data residency requires it.

OpenAI API
GPT Models
Structured Outputs
Function Calling
Embeddings
pgvector
Vector Database
Retrieval
Moderation
Rate Limiting
Spend Caps
Azure OpenAI

Provider Kept Swappable

Prompts Version Controlled

Outputs Evaluated, Not Assumed

Spend Capped Per User

Prompts, retrieval and application logic stay separate from the provider, so changing model becomes configuration rather than a rebuild.

Prompts live in the repository and change through review, because an untracked edit to one is a silent change to how the product behaves.

We build a test set of real questions with known good answers and measure against it, so a prompt change is verified rather than eyeballed.

Per-user and per-key limits sit in front of the API, so an unusual pattern of use cannot turn into an unusual invoice.

Provider Kept Swappable

Prompts Version Controlled

Outputs Evaluated, Not Assumed

Spend Capped Per User

Usually far less than expected. Costs are per request and measured in fractions of a cent for most interactions, so a pilot serving hundreds of users often runs to tens of dollars monthly. Heavy document processing is where it becomes worth modelling properly.

Custom ChatGPT
Integration Services