OpenAI doesn't know your business.
That's why the demo works and production doesn't.

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.

Chat Completions is stateless - you manage conversation history, file handling, and tool orchestration yourself. The Responses API gives you built-in tool use, hosted conversation state, and a simpler integration surface, and it is the path OpenAI is investing in going forward - the older Assistants API is being retired. For most operational workflows the Responses API reduces the infrastructure you need to maintain, but a stateless Chat Completions call is still the right choice for simple single-turn tasks where you do not need persistence. We pick the right approach per use case, and we handle the migration for clients who built on the Assistants API before the retirement.

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 tier to the flagship 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 the fast low-cost tier at a well-engineered prompt outperforms the flagship model at a lazy one, and costs a fraction of the price. We make that call explicitly in the architecture phase.

No, and we will say so in the scoping call. If your compliance posture requires a specific cloud boundary, Azure OpenAI is usually the better call; if you want a second model for redundancy or a different reasoning style, Anthropic Claude fits. We are also not the right fit if what you actually want is a simple FAQ chatbot with no CRM or document connection - that is a commodity wrapper you can buy off the shelf in an afternoon, and we will tell you to do that instead of billing you to build it.