The hard part of an AI feature was never the demo — it’s the production version: the one that handles edge cases, stays within budget at scale, fails gracefully, and doesn’t embarrass you in front of a customer. That’s the part we do.
OpenAI gives us the broadest production-ready model range available — GPT-5 for general intelligence, the o-series for deep reasoning, GPT-4o and GPT-4.1 for high-volume and multimodal work, and the Nano tiers when cost and speed matter most. It’s our default for production LLM features, and it’s backed by the most mature API ecosystem: function calling, structured outputs, embeddings, the Assistants API, batch processing, and aggressive prompt caching.
We use OpenAI to build chatbots and virtual assistants that actually resolve things, content and extraction pipelines, document analysis and summarization, semantic search and RAG, and tool-use agents that take real actions. Every build ships with the production concerns handled — prompt engineering, token and cost optimization, rate-limit handling, guardrails, monitoring, and fallback logic — so the feature is powerful and economical, not just impressive in a demo.
And because we’re an AI-first agency that isn’t married to a single vendor, we treat model choice as an engineering decision, not a loyalty test. OpenAI is our default — but we’ll route you to Claude for long-context reasoning and code, or Gemini for cheap multimodal, the moment the use case calls for it. The next section is exactly that honest breakdown.
