Custom GPT Development UK

A custom GPT is an AI assistant built specifically for your business using your data, brand voice, and defined workflows — unlike the generic public ChatGPT. It can be grounded in your proprietary documents, integrated with your internal systems, restricted to specific tasks, and deployed within your own secure environment.

We build on GPT-4o and GPT-4 Turbo via the OpenAI API, Anthropic Claude 3 and 3.5, Google Gemini, and open-source models such as Llama 3 and Mistral. Model selection depends on your performance requirements, data sensitivity, cost constraints, and whether you need an on-premises or cloud-hosted deployment.

We use retrieval-augmented generation (RAG) rather than fine-tuning in most cases, which means the assistant can access and reference your documents, knowledge bases, and databases at query time without exposing your data to the base model training process. Fine-tuning is available for specific scenarios where consistent tone or domain terminology is required.

Your data is never used to train third-party models. We implement access controls, encryption at rest and in transit, and detailed audit logging. For the most sensitive deployments we can build on Azure OpenAI Service with customer-managed keys, or deploy open-source models entirely within your own infrastructure.

Custom GPT assistants can integrate with Microsoft 365, SharePoint, Teams, Salesforce, HubSpot, Slack, bespoke internal databases, document management platforms, and virtually any system with an API. We also build tool-calling capabilities that allow the AI to take actions — not just answer questions — within connected systems.

A focused single-purpose assistant (for example a policy Q&A bot or internal knowledge search) typically takes four to eight weeks from kickoff to production deployment. More complex multi-tool agentic assistants with deep system integrations run from eight to twenty weeks.

A custom GPT uses retrieval-augmented generation to access your documents and data at query time — it is easier to update, more explainable, and does not require large labelled datasets. Fine-tuning adjusts the model weights themselves using your training data, which is appropriate for specific style or domain tasks but more complex to maintain.

We implement RAG with source citation, confidence thresholds, and output validation layers. Where the model is uncertain or cannot find relevant source material, it is instructed to say so rather than fabricate an answer. We also conduct red-team testing before launch to identify and mitigate hallucination risks specific to your use case.

Yes. We build all assistants as fully branded products under your identity. The assistant interface, name, and tone are tailored to your brand, and there is no visible reference to the underlying model or to iCentric unless you choose otherwise.

AI models are updated frequently by their providers. Our support packages include model version testing, prompt adaptation when underlying model behaviour changes, knowledge base refresh workflows, and performance monitoring. We proactively notify you of any changes that could affect your assistant before they are applied.