AI automation · tested on ourselves first

AI automation for the work your team repeats

Niro Digital provides AI automation for companies across the EU: we map how work moves through your company, pick the steps AI can do reliably, and build supervised agents and document pipelines with verification and human approval.

Show us the workflow

A person replies within one working day.
First call: we map one workflow together, no pitch.
We'll say if AI isn't the answer.

What is it, exactly?

Supervised AI agents doing the repetitive digital work your team does by hand: answering customer-support email, drafting offers and proposals, issuing invoices, chasing follow-ups, moving data between your CRM, email and databases, even the first pass over a thesis. A person still approves what matters. It starts with an audit of one workflow, not with a tool. If the process still lives on paper or in someone's head, we digitalize it first: digitalization is the prerequisite of automation, and a lot of companies are still at that step.

What we build

//01Workflow and process audits that show where the hours go
//02AI agents for content, document processing and back-office work
//03Multi-step AI pipelines with checkpoints, budgets and cost ceilings
//04Verification gates and human approval where mistakes are expensive
//05Integration with your CRM, email, calendars and databases
//06Audit trails, so every automated action can be traced and priced

Is it for us?

Best for: Teams that spend hours a week writing, reviewing, processing documents, moving data between tools or reporting.
What we don't do: We don't bolt a chatbot onto a process and call it automation.

What does it cost?

How it's priced: Per workflow after an audit: pilot first, then rollout, priced by complexity and hours removed.

How does a project run?

//Stage 01

Workflow investigation

We sit inside your real process and map it end to end: where work enters, which steps are judgment and which are repetition, what tools and data each step touches, and what an error costs.

You get: One workflow mapped, with cost per error

//Stage 02

Automation design and pilot

We pick the workflows where AI automation pays back fastest, then build a pilot: the agent, its guardrails, verification checks, and the approval points where a human stays in charge.

You get: A pilot agent running on real work

//Stage 03

Deploy, verify, expand

We run the automation on real work, measure quality and cost per task, tighten the checks, and only then expand it to more workflows — with an audit trail for every action.

You get: Cost per task, audit trail, next workflows

What changes for us?

96%

less per published article once supervised agents ran our own content pipeline

$0.37

per sourced article in model spend, Agent Agency, 2026

*Figures from our own accounts and case studies. Ask on the first call and we'll open the accounts.

What changes

//01Hours of repetitive work removed every week
//02Cost per task in cents, not hours
//03Output quality enforced by checks, not hope
//04AI executes, your team approves what matters

Which workflows can actually be automated with AI?

Repetitive digital work with clear inputs and outputs automates best: document review, content production, data entry and enrichment, reporting, classification, and multi-step back-office processes. The audit tells us — we map your workflows and rank them by payback and risk before anything is built.

How do you stop AI from making costly mistakes?

We never trust model output blindly. Every automation ships with deterministic verification checks, budget ceilings, and human approval gates for irreversible actions. We build systems where AI output is treated as a claim that must pass checks before it counts as done.

Will this replace our team?

No — it removes the repetitive part of their work. Humans keep the judgment calls: our automations are designed around explicit approval points, so your team decides what gets published, sent, or paid.

Selected work

Work in AI automation

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