AI features that hold up with real users
Most AI work stalls after the demo. We start from a real user journey and build something that keeps its quality, and its costs, once people start using it.
How we approach ai
Selcouth.DIGITAL builds AI features that work for real users, not just in a demo. We start from an actual user journey, choose the model and approach that fit it, and design for the cases that decide whether people trust a feature: slow answers, wrong answers, and the moments when the model is unavailable. That means streaming interfaces, clear fallback states and a human decision point wherever an AI action matters.
Quality and cost are part of the build, not an afterthought. We write evaluations so you can tell whether a change to a prompt, model or retrieval step made things better, add guardrails around inputs and outputs, and track cost per completed task so the running cost of the feature is known before launch. We work with Anthropic and OpenAI models, the Vercel AI SDK, pgvector and LangGraph, and our experience includes production generation for AMiSi.ai and conversational AI for Francis in regulated finance. If you have an idea, a stalled prototype or a feature that behaves unpredictably, we can scope what is realistic and build it to hold up.
What you get
- Feature scoping and model selection
- Chat, generation and automation flows
- Evals, guardrails and cost controls
- Streaming UI and fallback states
Stack
Typical outcome
A feature that holds its quality bar under load, with a known cost per request.
Proof
AMiSi.ai production generation; Francis conversational AI in regulated finance.
AI questions
AI case studies
- Francis
An embedded financial AI experience with secure onboarding, streaming conversations and dependable account journeys across a production Expo app.
Other services
Tell us what you're building.
Send a few lines on the problem. We reply within one working day with how we'd approach it and what happens next.