AI integration services for your stack

AI integration services cover the full process of embedding AI capabilities into your existing software — from technical planning and API integration to middleware development, testing, deployment, and monitoring setup. At InterCode, this includes LLM integration (OpenAI, Claude, Gemini), RAG pipeline setup, vector database configuration, prompt management systems, and observability tooling.

We use an incremental integration approach — starting with the lowest-risk, highest-value touchpoint, deploying behind feature flags, and rolling out gradually. We avoid big-bang deployments. Every integration goes through staging, load testing, and a canary release before full production exposure. This means you keep shipping your existing product while we extend it with AI.

LLM integration specifically refers to connecting large language model APIs (like OpenAI or Anthropic) into your product. AI integration is a broader term covering any AI system — LLMs, vision models, recommendation engines, classification models, and automation workflows. In practice, most of our current engagements involve LLM integration as the primary component, often combined with vector databases and AI orchestration layers.

Yes — openai integration services are one of our most common engagements. We integrate GPT-4o, GPT-4 Turbo, and embedding models into existing SaaS platforms, handling authentication, rate limiting, streaming, error handling, and cost management. We also build the prompt management layer and observability tooling so you can monitor and optimise the integration over time.

A simple LLM feature integration (e.g., adding a chat interface or summarisation feature to an existing product) typically takes 2–4 weeks. A more complex ai system integration — such as a multi-provider RAG pipeline with vector search and monitoring — takes 6–10 weeks. We provide a detailed timeline after a technical assessment of your current stack.