ChatGPT Application Development Services

We build custom applications that use OpenAI's GPT models for specific business use cases: AI assistants for customer support or internal knowledge search, document processing tools that summarise, extract, or classify content, AI copilots embedded in existing software, automated content generation tools with approval workflows, and conversational interfaces for complex data queries. The use case determines the architecture, not all ChatGPT applications are chatbots.

Out-of-the-box ChatGPT doesn't know your business. We make it accurate through three approaches: (1) RAG (retrieval-augmented generation), the model retrieves relevant documents from your knowledge base before generating a response, so answers are grounded in your actual data. (2) System prompts and fine-tuning, we engineer prompts that constrain the model's behaviour and, where appropriate, fine-tune a model on your domain-specific examples. (3) Guardrails, we build validation layers that catch and handle responses that fall outside expected parameters.

Yes. We build RAG pipelines that index your internal documents, PDFs, Word files, SharePoint content, database records, website content, into a vector store that the model can search before generating responses. The model doesn't guess based on its training data, it retrieves the right information from your sources and uses that to generate the response. Answers include source citations so users can verify them.

Hallucination is a real risk with any language model. We reduce it through RAG (answers are grounded in your actual documents, not model memory), confidence scoring (responses that don't find relevant sources are flagged or escalated), human review workflows for high-stakes decisions, and response logging so you can identify and fix systematic errors. We don't promise zero errors, but we design systems with the failure modes in mind.

A focused first application, for example, an internal knowledge search assistant or a document summarisation tool, typically runs $20,000--$50,000. A full AI platform with multiple use cases, custom integrations, and a user interface typically runs $60,000--$150,000. The cost depends on the number of use cases, the complexity of the data pipeline, and the user interface requirements. We scope every project before pricing it.

Yes, you can. The API is well documented and the basic integration is straightforward. The hard part is: designing prompts that produce consistent, accurate results for your use case; building the RAG pipeline that connects the model to your data; handling failure modes (timeouts, rate limits, wrong answers); building the user interface and approval workflows around the AI capability; and testing and monitoring the system in production. These are engineering and product problems, not just API calls. We've solved them 20+ times.