Generative AI Application
Development Services

An AI wrapper is a basic frontend connected to a single AI API — it works for a demo but breaks under real usage. An AI-native application includes a custom backend, dedicated databases for user memory, payment infrastructure, and business logic that orchestrates multiple AI models — built to handle concurrent enterprise-scale traffic without falling over.

RAG connects an AI model to your own data — documents, user history, proprietary knowledge — so it retrieves accurate, specific information instead of relying only on its general training. If your app needs to reference user-specific or company-specific data rather than just general knowledge, you need RAG; our custom LLM development work covers exactly this kind of architecture.

"AI app development" is the broader engineering discipline — building the product around any kind of AI, including predictive or recommendation models. "Generative AI development" specifically means the app creates new content — text, images, audio — using models like GPT or Claude. Most modern products blend both: predictive logic for decisions, generative models for content.

Cost depends mainly on your "cost per user" — driven by API token usage, model choice, and how many AI capabilities you need — plus standard engineering costs for the backend, database, and infrastructure. As part of scoping, a serious partner maps out your expected inference costs upfront so you know your margins before you launch, not after.

A focused MVP with a single core AI feature typically takes 8–12 weeks to build and launch. A full multimodal ecosystem — with vector database memory, billing infrastructure, and mobile apps — usually runs 4–6 months, delivered through iterative sprints so you can start testing with real users early.

Slow, blocking AI responses feel broken to users. The fix is streaming the response token-by-token via WebSockets or Server-Sent Events — the same technique ChatGPT uses — so the app feels instantly responsive even while the full answer is still generating.

Yes — a multimodal AI app can process and generate multiple data types in one workflow: a user uploads an image, the AI analyzes it and writes a script, then generates a voiceover, all orchestrated through a single backend. This requires careful backend architecture to keep the different AI calls in sync, not just stitching together separate tools.

Yes — you retain 100% intellectual property rights to the frontend, backend, custom databases, and business logic engineered for your product. This is standard for any serious Generative AI development engagement — the code and architecture belong to you, not the agency.

Building a generative AI app means creating an entire new product where AI is the core feature — with its own backend, database, and UI. Adding an AI agent, by contrast, means integrating autonomous task-execution into software you already have, without rebuilding it. If you already have a working platform and just need it to take action, Agentic AI Solutions is the right service, not a new app build.