In depthLLM integration that is accurate, private and affordable
Adding a large language model to a product is easy to demo and hard to run well. Our GPT integration services cover the parts that decide success in production: grounding answers in your data, protecting personal information, keeping latency low and controlling token costs, across OpenAI, Anthropic Claude, Google Gemini and open-source models.
ChatGPT integration for business, grounded in your data
A general model knows the internet, not your business. We use retrieval-augmented generation (RAG) to give it access to your documents, help centre, contracts or product data without retraining. Your content is split into passages, converted to vector embeddings and stored in a vector database. When a user asks a question, the most relevant passages are retrieved and the model is instructed to answer from those facts alone, citing its sources. If the information is not there, it says so rather than guessing. This is the approach behind the legal knowledge system we built for Chen & Associates, which cut research time by 90%.
OpenAI API integration and Claude API integration
We are model-agnostic and choose per task. OpenAI and Anthropic Claude models lead on reasoning and writing quality; Google Gemini is strong with long documents and images; open-source models such as Llama can run inside your own cloud when data must not leave it. Many integrations route requests between models, sending simple classification to a small, fast model and complex reasoning to a larger one. Abstracting the provider also means you can switch models as prices and capabilities change, without rewriting your product.
Guardrails, privacy and compliance
Every request passes through a guard layer before it reaches a model. Personal data such as names, card numbers and addresses is detected and masked, prompts are checked for injection attempts and outputs are validated against your rules on tone, topics and format. Access controls ensure users only retrieve documents they are allowed to see. We follow SOC2-ready and GDPR-compliant engineering practices, log every interaction for audit and agree data retention settings with each model provider so that your data is not used for training.
Performance and token cost optimisation
Users expect answers to start appearing within a second. We stream responses token by token, cache frequent answers and retrieved context, and keep prompts lean through compression and careful context selection. Usage is tracked per feature and per customer, so you can see exactly what each capability costs to run and set budgets or limits. These optimisations often matter as much as model choice: a well-engineered integration on a mid-sized model frequently outperforms a naive one on the largest model, at a fraction of the running cost.
