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OpenAI Integration & Development
Products built on OpenAI's models, with the evaluation and guardrails that separate a working feature from a demo.
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What OpenAI is
OpenAI provides commercial large language model APIs (the GPT model family) widely used as the foundation for generative AI and conversational AI features. Building a genuinely useful product on top of them is mostly an integration, evaluation and constraint-design problem, not a model-training one.
The gap between a working prototype and a production feature is usually evaluation discipline — knowing when the model is wrong, handling that gracefully, and measuring quality with something more rigorous than "it looked right in the demo."
Suitability
And when it is not. Choosing the wrong tool costs more than any implementation detail.
Good fit for
- Conversational and assistant-style features where OpenAI's models offer strong general-purpose capability
- Content generation, summarisation and classification tasks suited to a general-purpose LLM
- Products wanting a well-documented, widely-supported API with broad tooling ecosystem support
Consider alternatives for
- Narrow, well-defined tasks where a smaller, specialised model would be cheaper and faster with comparable accuracy
- Workloads with strict data-residency or self-hosting requirements that a third-party API can't satisfy
In practice
01
AI-powered product features
Customer-facing assistants, content generation and classification features built on OpenAI's models, with evaluation built in from the start.
02
Retrieval-augmented systems
RAG pipelines pairing OpenAI's models with a client's own data, so responses are grounded rather than relying on the model's general knowledge alone.
Considerations
Practical notes from running this in production.
- 01
Model behaviour and pricing change over time as providers update their offerings — a production system needs an evaluation suite that can quickly confirm a model or prompt change hasn't degraded quality, not just a one-time test.
- 02
Guardrails (input validation, output filtering, rate limiting) are a design requirement from day one for anything customer-facing, not a later addition.
- 03
Cost scales with usage in a way that is easy to underestimate during prototyping — modelling expected production volume early avoids surprises.
FAQ
How do you know if an OpenAI-based feature is actually working well?
An evaluation set built before implementation — real examples with known-correct answers, scored automatically — turns quality into a trackable number rather than a subjective impression. We build this as a standard part of any generative AI engagement, not an optional extra.
Can OpenAI models be integrated with our existing data?
Yes, typically through retrieval-augmented generation — the model is given relevant context from your own data at query time rather than relying on its general training. This is usually the right approach for anything needing answers grounded in specific, current business information.
How long does a OpenAI development project take?
It depends on scope. A focused first release is usually weeks rather than months, while larger platforms are delivered in phases. We give a written estimate after a short discovery conversation and agree milestones before work starts.
How is OpenAI development work priced?
We work either to a fixed scope with agreed milestones or on a time-and-materials basis, whichever suits how well the requirements are known. You receive the estimate and its assumptions in writing before committing.
Who owns the code and the intellectual property?
Ownership terms are agreed in writing before work begins, so there is no ambiguity about what you receive and what you may reuse.
By industry
Where we apply OpenAI. Tiles marked "Guide" open a dedicated write-up; the rest open the industry page.
Let’s talk about your OpenAI project
Tell us what you are trying to build or fix. We will come back with scope, approach and an honest view on cost and timeline.