AI App Development: LLM-Powered Products and Features That Ship

There's no fixed price until the use case is clear, so I send a written fixed-scope quote after a short call. What moves the price in AI app development is whether it is a feature in an existing app or a new product, whether it needs RAG over your data and how messy that data is, how many tools an agent must use, and the quality bar. Model API usage is a separate running cost paid to the provider, and the plan explains how it scales.

Almost certainly not. Hosted models from Google, Anthropic and OpenAI handle most text and reasoning tasks well with good prompts and structured outputs. When answers depend on your own content, RAG adds that knowledge at query time. Fine-tuning is worth considering only when you need a consistent format or tone on a narrow task and have good labelled examples. Training from scratch is a research project, not a first product step.

Use the one that passes your test cases at the lowest cost and latency, which is why I test before committing. All three are strong, and the differences show up per task: long documents, tool use, structured output, speed, price and data terms. Krillo runs on Gemini, and I also build with the Anthropic API. I keep model calls behind a thin layer in your code, so switching providers later means re-running the tests, not rewriting the app.

RAG, or retrieval-augmented generation, means finding the relevant pieces of your own content and giving them to the model with the question, so answers come from your data instead of the model's memory. You need it if users ask about your documents, products, policies or records, or if that content changes often. You don't need it for generic tasks like rewriting text or classifying tickets. Good RAG is mostly about chunking, retrieval quality, permissions and showing sources.

That depends mostly on your data and your quality bar, and the written plan you get before work starts includes a timeline with milestones. The biggest variables are how ready your data is, how many systems the AI has to connect to, and how many rounds of testing it takes to reach acceptable answers. Because I prototype the core AI flow on real examples first, you see actual outputs early instead of waiting for the full build to judge the idea.

Yes, and it is often the quickest way to get value from AI. I add LLM calls on the server side of your current stack, whether that is Next.js, React with a Node.js or Express backend, or a Python service. API keys never reach the browser, responses stream into your interface, and usage is logged. If your app runs on a stack I'm less suited to, I'll read the codebase first and tell you plainly.

I design for cost from the start instead of reacting to the first large invoice. That means using the smallest model that passes your tests, sending only hard requests to larger models, retrieving the relevant passages rather than whole documents, caching repeated answers, capping output length and rate-limiting each user. Token usage is logged per feature, and I set spend limits or budget alerts wherever the provider supports them, so a spike shows up before the invoice does.

It can be, if the system is designed for it. Paid API plans from the major providers generally state that they do not train on your requests by default, but terms vary by plan and free tiers can differ, so I check the exact plan you will use. I send only the fields a task needs, mask personal data where possible, keep calls server-side, store embeddings in your own database and enforce permissions at retrieval. If data must never leave your servers, we can discuss self-hosted open-weight models.

You do, including the parts specific to AI. Model API keys, the vector database and hosting sit under your own accounts, so nothing depends on me to keep running. Prompts, evaluation test sets and notes on why each prompt is written the way it is are part of the handover, so another developer can change them safely. If you'd like me to keep improving the product after launch, post-launch support can be arranged.