AI provider
OpenAI integration
OpenAI's models can read, write, summarize, describe images, transcribe speech, and search by meaning. We connect them to real systems, with limits and checks, and we compare them with other providers so the choice fits the task.
- 1,200+ clients helped
- Building since 2010
- 15 services, one team
- 50+ technologies
At a glance
What it is
OpenAI provides language, image, speech, and search models that other software can call, so products and processes can read, write, summarize, and answer.
Problems it solves
Teams want to use these models in their own systems, but are unsure how to connect them safely, keep costs in check, and know whether the answers are good.
Who it suits
Product teams and operations leaders planning an AI feature, and technical leads comparing model providers.
How to choose
Pick the provider by the task, the data rules, and the cost, and design so that the choice can change. OpenAI is one strong option among several.
What it means
Models you reach through an API
OpenAI is an AI company whose models can be used through an API, a set of agreed requests that software can make. Your system sends text, files, or audio, and receives a result.
You do not host these models yourself. That makes starting easy, and it means decisions about data handling, cost, and reliability depend partly on the provider's terms.
Good integration is therefore less about calling the model and more about what surrounds the call: what is sent, what is checked, what is stored, and what happens when the answer is wrong.
What DECHY can do
Working with OpenAI
Design
- Choosing which tasks suit a model and which do not.
- Cost estimates before building.
Build
- Features inside your product or workflow.
- Search and question answering over your documents.
Control
- Test sets and sampling of real results.
- Limits on spend, data sent, and actions allowed.
Stay flexible
- A design that lets the provider be swapped.
- Comparisons with other providers on your own examples.
What it looks like
A model used safely inside a product
A language model is useful inside a product when its answers are sourced and a person can step in.
Cost and risk
What surrounds a model call
The call is the small part. The layers around it make it safe to use.
What surrounds a model call
Layers from top to bottom. Your rules; What is sent; The model call; Checks on the answer; Records.
- Your rulesWhat the feature may do, and what needs approval.
- What is sentOnly the data the task needs, with private parts removed.
- The model callA request to the provider, with limits on size and spend.
- Checks on the answerValidation, source checks, and confidence limits.
- RecordsLogs and samples, so quality can be reviewed.
Choosing the right approach
What no model removes
Language models can state wrong things with confidence. Careful design reduces this and testing measures it, but no setup removes it. Where a wrong answer is costly, a person should check it.
We will tell you which parts of your process can be automated safely and which should stay with people.
Where it fits
Matching tasks to the kind of model
| Task | Capability used | What to check |
|---|---|---|
| Sorting incoming requests | Text classification. | Accuracy on real examples, and what happens to doubtful ones. |
| Answering from your documents | Search by meaning plus text generation. | Sources shown, and the documents kept current. |
| Reading invoices or forms | Document and image understanding. | Field accuracy, and human review of low-confidence results. |
| Transcribing calls | Speech to text. | Names, terms, and privacy rules for recordings. |
| Drafting replies | Text generation. | Tone, correctness, and a person approving before sending. |
Technical depth
How we build with OpenAI
What we settle early on OpenAI work: integration, quality, and data handling, so nothing surprises your team later.
We call the provider's API from your server, never from a browser, keep keys in a secrets store, and add timeouts, retries, and spend limits.
- Structured outputs validated against a schema before anything uses them.
- A test set of real examples run on every prompt or model change.
- Embeddings and a vector store such as Pinecone for search by meaning.
We review the provider's data terms against your requirements, send the minimum data, and choose regions and retention settings where the provider offers them.
Common questions
Questions about OpenAI
Is our data used to train the models?
That depends on the product, plan, and settings, and the terms change. We read the current terms against your requirements before building, and tell you what we find.
Should we use OpenAI or another provider?
It depends on the task and the rules you work under. We compare options on your own examples, including Anthropic Claude and Google Gemini.
How do we control cost?
By limiting what is sent, choosing the smallest model that does the job, caching repeat requests, and setting spend alerts. We estimate monthly cost before you commit.
What if the provider changes a model?
Our test set shows the effect, and the design lets us adjust or switch. That is why we keep the provider behind a clear boundary.
Have a different question?
Tell us what you are weighing up and we will answer it plainly.
Ask a questionAlready a client? Email support@dechytech.com
Tell us about the task
Describe what you want the AI to do and what information it would use. We will say whether OpenAI suits it, what it would cost to run, and how we would test it.