OpenAI is powerful in demos.
Production is a different problem.

A prototype that works in a notebook is not a production system. The gaps that matter - retrieval quality on messy real data, prompt stability across edge cases, cost governance at scale, integration with your CRM, and evals that catch regressions - are all engineering and architecture problems, not API access problems. Most internal teams hit these walls and stall. We have solved them across enough deployments to move faster and avoid the expensive detours.

OpenAI's API does not use your data to train models by default, which is the baseline most mid-market firms need. For more sensitive situations we work through data classification with you upfront - identifying what can go to the API directly, what needs to be anonymized or summarized before transmission, and whether Azure OpenAI Service is a better fit for your compliance posture. We do not skip this conversation.

The Chat Completions API is stateless - you manage conversation history, file handling, and tool orchestration yourself. The Assistants API gives you persistent threads, built-in file search with OpenAI's vector store, and a managed run lifecycle. For most operational workflows the Assistants API reduces the infrastructure you need to maintain, but it also adds latency and cost overhead that is not always worth it for simple single-turn tasks. We pick the right approach per use case.

A focused single-use-case build - say, a sales rep briefing agent pulling from your CRM and a product knowledge base - typically takes four to eight weeks from signed scope to production handoff. That timeline assumes clean API access to your data sources. Integrations that require custom ETL or involve a heavily customized CRM take longer. We will tell you the honest timeline in the scoping phase, not after we have started.

Fine-tuning and RAG solve different problems. Fine-tuning adjusts the model's style, tone, or format - it does not reliably inject factual knowledge from your documents. For most mid-market use cases, a well-designed RAG pipeline outperforms fine-tuning on accuracy and is far easier to update when your content changes. We do implement fine-tuning when the use case genuinely calls for it, but we will tell you when it is the wrong tool.

You need an evaluation framework, not just vibes. We build a set of test cases from your real data, define the accuracy bar for your specific task, and run evals before and after any prompt or model change. This gives you a repeatable measurement rather than spot-checking outputs manually. It also gives leadership something concrete to review before approving wider rollout.

We work with OpenAI's current production lineup, from the fast low-cost models to the top reasoning models where the depth justifies the cost and latency tradeoff, and we re-evaluate as new models ship. Model selection is a design decision, not a default. For most high-volume operational tasks GPT-4o-mini at a well-engineered prompt outperforms GPT-4o at a lazy one, and costs a fraction of the price. We make that call explicitly in the architecture phase.