Why this distinction gets lost
"We want to build an AI feature" gets used for two very different projects: embedding an existing foundation model (OpenAI, Anthropic, etc.) into your product via an API, or building and training custom model infrastructure. They get priced, staffed, and timelined completely differently, and conflating them is one of the most common sources of blown budgets and missed expectations in AI projects.
AI integration — what it actually is
Connecting an existing LLM to your product: RAG pipelines over your data, custom assistants and chatbots, API-layer integration into your existing systems, prompt engineering and evaluation. You're not training a model — you're engineering the system around one that already exists. This is what the large majority of real business "AI projects" actually are.
Custom AI development — what it actually is
Training or fine-tuning models specifically for your use case, building custom ML infrastructure, or developing proprietary model architecture. This is rarer, more expensive, and only justified when off-the-shelf models genuinely can't do the job — a narrow, specialized task at scale where a smaller fine-tuned model beats prompting a general one, for example.
How to tell which one you need
Ask: "does an existing model already have the general capability we need, and we just need to connect it to our data and workflow?" If yes, that's integration — see What is RAG (Retrieval-Augmented Generation)? and RAG vs. Fine-Tuning for the two most common integration architectures. If the answer is genuinely no — the capability itself doesn't exist yet in any general model — that's a custom development conversation, and a much bigger one.
Cost and timeline implications
Integration projects are measurably faster and cheaper — see AI Chatbot Development Cost for realistic ranges. Custom development is a different order of magnitude in both cost and time, and should only be pursued after confirming integration genuinely can't solve the problem.
Where Nemnidhi fits
Our AI Integration work covers the large majority of real business AI needs — LLM pipelines, RAG, custom assistants, document intelligence, embedded via API into your existing product. Tell us what you're trying to build and we'll tell you honestly which category it falls into before we talk pricing.