OpenAI integration for practical, well-tested AI features
OpenAI provides GPT language models and related models for images, speech and embeddings through a hosted API. It is a common starting point for adding assistants, summarisation, extraction and semantic search to existing products.
The OpenAI API offers capable general-purpose models, function calling and structured outputs that make results easier to use in software, plus embeddings for retrieval. Getting value from it depends on good prompts, grounding in your own data and systematic testing. Costs scale with usage, and data leaves your infrastructure, so we review terms and design for both. We often compare OpenAI with Anthropic Claude and Google Gemini on your real tasks before choosing, and keep the integration portable between providers.
What we build with OpenAI
- Customer and staff assistants
- Document summarisation and data extraction
- Semantic search over company content
- Classification and routing of messages
- Drafting and content workflows with human review
OpenAI questions
Does OpenAI use our API data to train its models?
Under its business API terms, data sent through the API is not used for training by default. We confirm the current terms and configure retention settings for your use case.
How do you control costs?
We choose the smallest model that meets the quality bar, cache repeated results, limit context size and add usage monitoring and limits.
How do you stop the model from making things up?
We ground answers in your own data through retrieval, instruct the model to say when it does not know, and test responses against a set of real examples before and after each change.
How do you measure whether an OpenAI feature is good enough?
We build an evaluation set from real cases with expected outcomes and score each model and prompt change against it, so quality decisions rest on evidence rather than impressions.
Should we fine-tune an OpenAI model or use retrieval?
Retrieval is usually the first step when the model needs your facts, because it stays current and can cite sources. Fine-tuning helps with consistent format, tone or narrow tasks where prompting alone falls short.
Can we get reliable structured data out of the model?
Yes. Structured outputs and function calling let us require responses that match a defined schema, which we then validate before saving or acting on them.
How do you keep responses fast?
We stream replies so users see text straight away, pick smaller models for simple steps, trim prompts and cache results that do not need to be regenerated.
Can OpenAI models be used through Azure?
Yes. Azure OpenAI offers the models within your Azure subscription, which can help with regional hosting, private networking and existing enterprise agreements.
How do you monitor an OpenAI feature in production?
We log prompts, responses, token usage and user feedback with sensitive data handled appropriately, and alert on errors, cost spikes and drops in quality.
What happens when OpenAI retires a model we use?
Providers retire older models over time, so we keep model names in configuration and rerun the evaluation set against replacements before switching.
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Tell us what you are building. We reply within one business day with next steps — and we are happy to sign an NDA first.