AI-native features

"We added a chatbot. It answers wrong, nobody trusts it, and now it's a liability with our customers."

LLM appsRAGagents

The difference between an AI feature people trust and one they turn off is engineering, not prompts: what the model is allowed to do, what it must never do on its own, and what happens when it's unsure. We build assistants and agents with hard walls — actions gated by your permission system, sensitive steps handed to a human, every decision traceable.

This is the layer we build for a living: Miira, the agent inside Niiko, answers and qualifies real leads over WhatsApp every day — consent-first, never moving a pipeline on its own. We apply the same architecture (retrieval over your data, tool use over your APIs, cost controls per tenant) to your product.

What you get

An assistant or agent scoped to what it may and may not do
Retrieval over your own data — answers grounded, not invented
Guardrails as architecture: permissions, handoffs, audit trail
Cost visibility per user or tenant, from day one

How an engagement runs — one call, a one-week scope, a plan with a price — is the same for every service: see the process.

// get started

Tell us what has to work.

One call. A one-week scope. A plan with a price.

Talk to us →