OpenAI

OpenAI’s offering changes quickly. In practice, you usually choose between a small set of options.

High-capability models. Best quality for harder reasoning, more complex instructions, and nuanced outputs.

Cost-optimised models. Faster and cheaper for simpler steps, classification, extraction, and high-volume tasks.

Long-context options. Useful when you need to work with large documents or keep more context in a single request.

Coding-focused options. Useful when you are building developer tools or agentic coding workflows.

Embeddings. Used for semantic search and retrieval over your own content.

We recommend based on evaluation, not branding: what meets the success criteria at the lowest cost and risk.

These models power sophisticated applications:

Agentic workflows: Systems that plan and execute multi-step tasks by calling tools and APIs, with clear guardrails and human approval where needed.

Knowledge work support: Drafting, summarising, analysing, and structuring information when the task is well-specified and you can validate the output.

Customer service: Chatbots that understand nuanced queries and provide contextual, accurate responses with dramatically improved consistency.

Content and document generation: Drafting reports, emails, and structured documents with human review for tone and accuracy.

Code development: Debugging assistance, code explanations, and building features when paired with your repo and your engineering standards.

These models are powerful but require thoughtful deployment:

Accuracy is not guaranteed: Even strong models can produce plausible errors. For anything consequential, you need grounding, validation, and clear fallbacks.

Quality versus latency: Higher-quality answers can cost more and take longer. Design should balance user experience, cost, and the true quality bar.

Cost management: Architecture choices (context size, retrieval approach, caching, batching) usually matter more than the headline model spend.

Data handling: Direct API versus Azure OpenAI or Bedrock can change your security posture and governance options. Choose based on your constraints, not convenience.

We build production applications using OpenAI's models:

Prompt engineering: Designing instructions that produce reliable outputs. The gap between demo and production remains significant.

Agent development: Building systems that leverage GPT-5's extended task capability with appropriate guardrails and human oversight.

System architecture: Handling rate limits, implementing fallbacks, managing context across extended interactions.

Integration: Connecting to business systems so AI can access relevant data and take meaningful action.

Cost optimisation: Choosing appropriate models for different tasks, implementing caching, managing token usage.

OpenAI offers multiple options:

High-capability models. For complex reasoning and high-quality output where nuance matters.

Cheaper models. For simpler steps at higher volume, with the right validation around them.

Long-context models. For working with large documents when retrieval alone is not enough.

Embeddings. For semantic search, similarity, and retrieval over your own knowledge base.

Specialised models. For audio, image generation, or domain-specific needs.

We help you select appropriately and can design systems using multiple models for different purposes.

Whether you are exploring what GPT-5 can do for your business or ready to build a production application, we can help you evaluate feasibility, design the solution, and deploy something that works properly.

Use these prompts to explore fit and trade-offs before you commit to an architecture.

“Which OpenAI model(s) should we use for our workflow, and where should we split steps across different models?”

“What guardrails do we need to keep outputs safe and reliable in production for this use case?”

“What evaluation plan will prove this works: datasets, metrics, human review, and failure modes?”