AI Application Development

AI application development means building a web or mobile application where AI is a core part of how the product works, not a bolted-on feature. In practice that means integrating a large language model (OpenAI, Anthropic, or an open-source model) for tasks like chat, summarization, or content generation, connecting custom ML models for prediction or classification, and building the surrounding product: authentication, data storage, APIs, and UI, on the same React, Next.js, and Node.js stack we use for standard applications.

Traditional app development is mostly deterministic: the same input produces the same output every time. AI app development adds a probabilistic layer on top: prompt design and testing, retrieval-augmented generation (RAG) so the model answers from your actual data, evaluation of model output quality, and management of per-request inference cost. The application layer (frontend, database, auth, APIs) is built the same way either way. We treat AI features as an addition to solid engineering fundamentals, not a replacement for them.

Most AI-powered apps take 4-6 weeks to build. Adding one or two well-scoped AI features (a chat interface, a summarization tool, a recommendation engine) to a straightforward product can be ready in 3-4 weeks. Applications that need custom model training, multiple LLM integrations, or complex data pipelines typically take 8-12 weeks. We give you a specific timeline after a scoping call, not before.

We quote AI application development on a fixed-scope basis in USD after understanding your requirements, so you know the total cost before development starts. The main cost drivers are the number of AI integrations, whether you need custom model training versus calling an existing LLM API, and how much of your data needs a retrieval pipeline built around it. We work with funded startups and enterprise teams in the US, UK, Canada, Australia, and the EU, and price accordingly rather than quoting hourly rates.

For the AI layer: OpenAI and Anthropic APIs, open-source LLMs, retrieval-augmented generation (RAG) pipelines, vector databases, and custom ML models for prediction or classification tasks. For the application layer: React, Next.js, and React Native on the frontend, Node.js on the backend, REST and GraphQL APIs, and AWS, GCP, or Azure for cloud deployment, sized for real inference load rather than a demo.

Yes. We build AI-powered web applications with React and Next.js, cross-platform mobile apps with React Native for iOS and Android, and Progressive Web Apps (PWAs). Native iOS and Android development is available when a project specifically needs it.

Yes. We regularly integrate LLMs and custom ML models into existing systems, databases, and APIs rather than starting from scratch. We audit your current stack first, then scope the specific AI features (chat, search, summarization, recommendations) so they connect cleanly to your existing data and infrastructure.

Yes. You get full ownership of all source code, prompts, custom model work, and documentation we produce. There is no vendor lock-in to us: you can take the codebase and AI integration work to any team after the engagement.

We include 60 days of post-launch support for bug fixes and minor enhancements. After that, we offer ongoing maintenance packages that cover model updates, prompt tuning as your data changes, new feature builds, and general technical support.