Cette page est publiée en anglais. Les contrats et politiques sont rédigés en anglais pour tous les marchés.
Custom LLM features, shipped into your product
Retrieval, fine-tuning, and agents built against your data, with evals so you know it works before users do.
Why a custom layer
General models do not know your product, your policies, or your customers. A retrieval layer over your own documents gives accurate, sourced answers; fine-tuning shapes tone and format; both cut inference cost by sending smaller prompts.
We start with an audit of what data you have and what the model must never say, then pick the smallest approach that meets the bar.
Interfaces that stay out of the way
Most AI features fail on the interface, not the model. We build streaming, keyboard-first surfaces that show sources, allow correction, and degrade gracefully when the model is slow or unavailable.
The example is the whole chat surface: one hook, one form, streaming tokens.
'use client'
import { useChat } from 'ai/react'
export default function Chat() {
const { messages, input, handleInputChange, handleSubmit } = useChat()
return (
<section>
<ol aria-live="polite">
{messages.map((m) => (
<li key={m.id} data-role={m.role}>{m.content}</li>
))}
</ol>
<form onSubmit={handleSubmit}>
<label htmlFor="q">Ask about your account</label>
<input id="q" value={input} onChange={handleInputChange} />
</form>
</section>
)
}À partir de $1,150 USD · 4-8 weeks · env. 1 060 €