Skip to content

Your cookie preferences

With your consent, we measure site traffic with Google Analytics to improve it. No advertising cookies. You can change your mind at any time. Cookies policy

AI

AI agent development agency in Paris

Agents that run one business workflow end to end, with tools, human approval at the right steps, evals, logs and a cost ceiling.

In short

ZR Digital is a Paris AI and product engineering studio that designs, builds and operates AI agents for business workflows such as support triage, back-office tasks, document processing and sales operations. Agents act through your APIs and the Model Context Protocol (MCP), ask a human to approve sensitive actions, and are measured with evals, logged end to end and kept under a cost budget.

An AI agent is worth building when it takes a task off someone’s desk and gives back a result they can check. ZR Digital is an AI agent development agency in Paris: we design, build and operate agents that run real business workflows, with a human in the loop where it matters, measurable quality, and a cost you can predict.

We start from the work, not the model. A support team drowning in tickets, an operations desk retyping PDFs, a sales team whose CRM is always out of date. Each is a workflow with inputs, rules, tools and an expected output. An agent reads the inputs, reasons over them with a large language model, calls tools through your APIs or Model Context Protocol (MCP) servers, and either acts or asks for approval.

What an agent actually does

Typical first agents we build:

  • Support triage. Classify incoming tickets, pull the customer’s history, draft a reply grounded in your documentation, route the hard ones to a person.
  • Back-office. Reconcile orders and invoices, update records across two systems that never talked to each other, flag anomalies instead of guessing.
  • Document processing. Extract fields from contracts, invoices or forms, check them against your rules, and send exceptions to review.
  • Sales operations. Enrich leads, keep the CRM clean, prepare account briefs before a call.

None of these need a general-purpose assistant. They need a narrow agent with the right tools and clear limits. Narrow agents are easier to test, cheaper to run and far less likely to surprise you.

Where agents fail, and what we do about it

Language models are good at reading messy input, following instructions and writing. They are also confidently wrong at times, sensitive to phrasing, and exposed to prompt injection when they read untrusted content such as emails or web pages. We design for that from day one.

  • Human approval. Irreversible actions — sending to a customer, paying, deleting — wait for a person. The agent prepares, a human decides, and the interface makes approving fast.
  • Least privilege. Each tool exposes only what the workflow needs. An agent that drafts replies cannot issue refunds.
  • Evals. Before writing the agent we build a test set from your real cases. Every change of prompt, model or tool is scored against it, so quality is a number, not a feeling.
  • Observability. Every step, tool call and decision is traced. When something goes wrong you can see why, replay it and fix it.
  • Cost control. Token budgets per run, the smallest model that passes the evals, caching where it is safe. A runaway loop hits a ceiling, not your invoice.

We follow the OWASP Top 10 for LLM applications and keep your data where your obligations require it, including EU-hosted or self-hosted models when GDPR or confidentiality calls for it.

Start with one workflow

The most reliable path is small. One workflow, a clear baseline, a few weeks to a working agent in shadow mode, where it proposes and your team decides. Once the logs show it is right often enough, autonomy widens step by step. Then the second workflow reuses the same foundations: connectors, evals, tracing, approval interface.

We would rather tell you a workflow is not ready for an agent than build one that cannot be trusted. Sometimes the right answer is a plain script, or fixing the process first.

Built by people who ship software

ZR Digital is led by a founder-CTO with more than fifteen years of product engineering. We also design, build and operate our own SaaS, BookCab.tech, for independent VTC drivers in France. The habits that keep a product running — typed code, infrastructure as code, monitoring, incident response — are the same ones that keep an agent trustworthy once it touches real customers.

How we run it

  1. 01

    Pick one workflow

    Frequent, rule-heavy, measurable. We write down today’s volume, time spent and error rate so the agent has a baseline to beat.

  2. 02

    Design the loop

    Tools, permissions, data access and the exact steps where a human approves. Architecture and threat model are written before code.

  3. 03

    Build against evals

    We collect real examples first, then build. Every prompt, model or tool change is scored against the same test set.

  4. 04

    Ship in shadow mode

    The agent proposes, your team decides. Autonomy widens only where the logs show it has earned it.

  5. 05

    Operate and extend

    We watch quality, latency and cost in production, then move to the next workflow with the same foundations.

Frequently asked questions

What is the difference between an AI agent and a chatbot?

A chatbot answers questions. An agent completes a task: it reads a ticket, looks up the customer, drafts the reply, updates the CRM and asks for approval before sending. It uses tools and takes actions, which is why it needs permissions, logs and evaluation that a chatbot does not.

Which workflow should we automate first?

One that happens many times a week, follows mostly stable rules, and has an output you can check. Support triage, invoice and document intake, and CRM hygiene are common first candidates. Avoid starting with a workflow where one mistake is expensive and hard to detect.

How do you stop an agent from making costly mistakes?

Least-privilege tool access, hard limits on what it can do without approval, validation of every tool call, and a human approval step for anything irreversible such as payments, emails to customers or deletions. Everything is logged, so each decision can be replayed and explained.

Can our data stay in the EU?

Yes. We can use EU-hosted endpoints, Mistral models, or open-weight models self-hosted in your own cloud account. The right choice depends on data sensitivity, quality needs and budget, and we document it with you before building.

An AI use case in mind? A product to launch?

Describe the situation in a few lines. Within two business days you get a straight answer: what we would do, in which order, and whether we are the right team for it.

Talk about your project Or write to us directly