Adding a chat box is the easy 10%. The other 90% is prompt design that holds up in production, cost control at scale, retrieval over your own data, and keeping the integration private and auditable. We build OpenAI/GPT integrations that survive real usage — not just a demo.
The OpenAI API is simple to call and easy to misuse at scale. A dedicated team catches the cost, privacy, and reliability issues before they hit production.
Prompts version-controlled, tested against edge cases, and structured for consistent output — not tuned once in a playground and shipped.
Model selection, caching, streaming, and token budgeting so a useful feature doesn't turn into a runaway API bill.
Retrieval architectures and API configurations that keep customer and company data out of training pipelines, with clear data-handling documentation.
RAG pipelines and vector search so answers are grounded in your documents and systems, not just the model's general knowledge.
Integration layer built so swapping or mixing OpenAI, Claude, or Gemini later is a config change, not a rewrite.
Documented prompts, eval sets, and clean integration code so your in-house team can own and extend it after launch.
Scoped to the AI features your product actually needs — not a generic chatbot bolted on for its own sake.
In-product chat, support assistants, and copilots built on the Chat Completions or Responses API.
Persistent assistants with tool calling, file search, and code execution for internal or customer-facing use.
Fine-tuned models on your data for consistent tone, format, or domain-specific tasks where prompting alone falls short.
Vector search over your documents, tickets, or product data so responses are grounded and current.
Adding AI features into your existing product — search, summarization, drafting — without a rebuild.
Eval sets, logging, and cost dashboards so you know when quality or spend drifts after launch.
It depends on scope — a single chat feature costs far less than a multi-step assistant with tool calling and retrieval over your data. We give a fixed estimate after a short scoping call rather than a generic range.
It depends on the task: reasoning-heavy workflows, long-context document work, and cost-sensitive high-volume calls each favor different models. We benchmark against your actual use case rather than picking based on general reputation, and we build the integration so switching later is cheap.
We configure API access so requests aren't used for training, scope what data is sent to the model in the first place, and keep sensitive data in your own retrieval layer rather than in prompts wherever possible. We document the data flow so your security team can review it.
Yes, when it's the right tool — usually for consistent formatting, tone, or narrow domain tasks. Often a well-designed prompt plus retrieval gets you there faster and cheaper, so we evaluate both before recommending fine-tuning.
Yes. Most engagements are exactly this — adding search, summarization, drafting, or a copilot into a product that already exists, without a rebuild or disruption to what's already working.
Tell us what you're trying to add — chat, search, an assistant, automation — and we'll map an approach, cost range, and a fixed estimate on a discovery call.