---
title: "In production, and yours to keep."
description: "Production-grade AI agents and automation built with your team, wired into your stack and yours to keep. What the build engagement covers, how it runs, and what you own at the end."
lang: "en"
canonical: "https://www.casperlarsen.dk/services/ai-build"
---

# AI agent and automation development

The build engagement delivers production-grade AI systems: agents, automation and retrieval-augmented generation (RAG) wired into the company’s existing stack. It is built alongside the company’s own team with knowledge transferred as it goes, so the company owns and understands what ships. After launch the systems are monitored, tuned and extended under the same retainer.

- **Timeline:** First system in production typically within four to eight weeks.
- **Format:** Build with your team, in your repo, with weekly demos of working software.
- **Output:** Running systems, documentation, and a team that can operate them.
- **After launch:** Monitoring, tuning and the next initiative, on the retainer.

## What you get

- Systems in production: agents, automation, RAG, integrations, wired into your ERP, CRM or data platform.
- An architecture per system: technology, data flows, integration points, delivery plan.
- Human-in-the-loop by default: nothing irreversible happens without a person approving it.
- Documentation and handover, so your team can run and extend it.
- Monitoring and measurement against the baseline set in the audit.

## How it works

Every system starts with its own blueprint, then gets built in small, shippable steps. You see working software every week, not a status report. The first version goes into production early and gets better from there.

The build happens in your repository, with your team in the room. The goal is that when the engagement ends you are not dependent on anyone outside the company to keep it running.

## What gets built

Typical systems: an agent that reads incoming documents and drafts the response for a person to approve. Automation that reconciles data across systems and flags deviations. A RAG system that answers questions from your own documents, in Danish, and says so when the answer is not there. A pipeline that turns a public data source into a ranked list every morning.

The common thread is that each one replaces hours of repeatable work with a system a person supervises.

See it in production: Danish law firm: the gap analysis is done before the first conversation (https://www.casperlarsen.dk/use-cases/gap-analysis-outreach)

## Questions buyers ask

### Do we need our own developers?

It helps but is not required. If you have a team, the build happens with them. If you do not, the system is built to be operated by the people who use it, with documentation and a handover.

### Which models and tools are used?

Whatever fits the job and your constraints on data and cost. Systems are built so the model can be swapped when a better or cheaper one arrives.

### What about GDPR and the EU AI Act?

Both are designed in from the start: data handling, logging, human oversight and the documentation the AI Act asks for. It is much cheaper to build it in than to retrofit it.

### What happens when the engagement ends?

The systems keep running. They are in your stack, your repo and your team’s hands. Most clients continue on a lighter retainer for monitoring and the next initiative, but that is a choice, not a lock-in.


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Casper Larsen, Fractional Chief AI Officer, Denmark. Contact: https://www.casperlarsen.dk/contact · Free pre-flight check: https://www.casperlarsen.dk/audit