Custom AI Application Development

In Depth

Building Products With AI at the Core

Adding a chat window to an existing product is easy. Building a product where language models do real work reliably is a different engineering problem, and it shapes decisions from the first sprint: how requests are queued when a model is slow, what the interface shows while an answer is being generated, how outputs are checked before a user acts on them, and what happens when a provider has an outage.

We design for those cases up front. Model calls sit behind a service layer, so the product can switch between providers such as OpenAI and Anthropic, or move to a self-hosted model, without a rewrite. Prompts and evaluation sets are versioned alongside the code. Costs are tracked per feature, so you can see what each capability costs to run before it reaches every user.

The rest is the engineering any production application needs: authentication and role-based access, audit logs, automated tests, and deployment pipelines that let you ship small changes safely. For mobile we usually recommend a cross-platform framework such as React Native or Flutter, with native code where a feature genuinely needs it.

Work runs in short cycles with a working build in front of your users early, and it typically draws on LLM integration and RAG development for the language features themselves. After launch we can stay on for maintenance, or hand the codebase to your own team with documentation to match.