Most "AI apps" are a regular app with a chat window stapled on. We build the AI into the product itself — smart search, recommendations, and an assistant that actually understands your data — with the usage metrics to prove people use it.
Wrapping a generic chat widget around an existing app is fast to ship and forgettable to use.
Smart search, recommendations, and the assistant sit inside the features people already use, not in a separate chat tab nobody opens.
Per-feature usage breakdown shows whether users touch smart search, recommendations, or the chat assistant — so you invest in what works.
Track model spend per feature and per user from day one, instead of finding out at the end of the month.
Correlate AI feature adoption with retention, so you know whether the AI investment is actually moving the number that matters.
The app is architected to swap or combine LLM providers as pricing and capabilities change, instead of hard-coding a single API.
No-code AI app builders cap what you can customize and where your data lives. A custom build removes both limits.
The features that make an app feel genuinely intelligent, not just AI-branded.
Natural-language and semantic search across your app's content, not just keyword matching.
Personalized suggestions based on user behavior and content, tuned to your specific catalog or use case.
An in-app assistant that understands your data and can take action, not just answer generic questions.
Sidebar view of Overview, Users, AI Features, and Analytics showing exactly which AI capabilities get used.
Active users, retention, and session metrics segmented by AI feature usage.
Live visibility into model API spend, broken down by feature so you can control it before it scales out of budget.
A chatbot bolted onto an app is a separate feature users have to seek out. We build AI capabilities like search and recommendations directly into the flows people already use, so adoption doesn't depend on anyone discovering a chat icon.
We choose based on the feature — that might mean an LLM API for the chat assistant, a smaller embedding model for semantic search, and a lighter model for recommendations. The app isn't locked to a single vendor.
We track cost per feature and per user from launch, cache and batch requests where it makes sense, and route simpler queries to cheaper models — so cost scales predictably instead of surprising you.
A focused AI app with one or two core AI features — search, recommendations, or a chat assistant — typically launches in 8-12 weeks, depending on how much custom model work the features need.
Tell us which AI features matter most to your users — we'll scope an app built around real adoption, not a demo.