Patrick PetcuAI agents & automation
SVBook AI audit

From AI idea to real business value.

I help companies choose the right AI case, build the solution and make it work inside real operations.

Book a free AI auditSee the method
Patrick Petcu - AI-rådgivning, AI-implementation och automation
Patrick PetcuStrategy · implementation · governance

Practical AI implementation — from prioritisation to systems people use.

Many people can talk about AI. I combine business understanding, agent building, automation and measurement so a selected use case reaches real operations.

Read the AI guide for companies

  1. Choose well

    Prioritise by business value, risk and feasibility — not tool hype.

  2. Build for real

    Connect AI agents to data, websites, CRM, documents and existing workflows.

  3. Keep control

    Measure quality and impact. Let people own decisions where judgement matters.

An AI project should be understandable before it is automated.

This summary keeps the homepage offer clear without JavaScript. Patrick works on bounded B2B engagements where the process, data, exceptions and human accountability can be described. The goal is not to add another AI tool, but to create a usable system tied to a measurable operational outcome.

  1. Start with the business problem

    A strong first case has a clear owner, recurring volume and an outcome that can be measured. That may mean shorter lead time, fewer manual hand-offs, more consistent data quality or better access to internal knowledge. If the value cannot be stated before the build, the technology is probably not the right starting point.

  2. Map data and systems

    The agent needs explicit allowed sources, freshness requirements and behaviour for missing or contradictory data. Patrick maps integrations across CRM, forms, documents, websites and reporting, separating the connections required for a useful pilot from those that can safely wait for a later phase.

  3. Design human control

    Stable and reversible steps can be automated further. Uncertain answers, sensitive data, exceptions and high-consequence decisions should route to a named person with the relevant context. Review points belong inside the process rather than in a generic disclaimer after the system has already acted.

  4. Build a measurable pilot

    A pilot should test a bounded hypothesis with known quality criteria. Before launch, define the baseline, acceptable error margin, review method and the result that would justify further investment. Documentation, logging and ownership are included so the company can evaluate the system beyond a polished demonstration.

  5. Operate what reaches production

    Models, vendors, data and processes change. A production workflow therefore needs an owner, recurring quality review, safe change procedures and a fallback when behaviour drifts. Patrick helps design that operational frame but does not replace legal, privacy or security specialists where their assessment is required.

  6. Define the agent interface

    An agent needs explicit guidance about when it should be used, which inputs it may receive and which response or action is expected. Tools, permissions and failure paths are documented so other systems can call the solution without guessing. Public interfaces should expose only intended information, while company data and write actions require authentication, least privilege and explicit approval.

  7. Make quality observable

    A system is not finished because one successful example can be demonstrated. Production requires traceable logs, clear error categories, answer-quality checks and signals when cost, latency or behaviour changes. Monitoring should connect to the business goal and show when a person needs to intervene, when prompts or data need adjustment and when automation should be paused entirely.

Many signals in. One clear decision out.

Good AI work is less about the model and more about how data, rules, judgement and follow-up become one intelligible system.

Taktil visualisering av signaler, AI-bearbetning, mänsklig kontroll och affärsutfall
Method visualisation · created for Patrick Petcu

See how an AI system moves from signal to decision.

Choose a common business problem. The map shows Patrick's principle for bounding the agent, preserving human control and creating a measurable next step.

  1. New enquiry
  2. Qualify and enrich
  3. Approve next step
  4. The right owner gets context
Principle

The agent reads the signal, gathers relevant context and proposes the next action. A person keeps the decision when business risk or relationships require judgement.

Interactive principle model — not a claim about a specific client result.

Three ways to start.

Start with a decision, a pilot build or a clearly bounded implementation. Each path leads to a focused page with the depth the homepage does not need to carry.

View all AI services

AI & Automation Audit

A prioritised roadmap and recommended first AI case.

AI Agent Pilot

A first agent connected to your data, systems and quality controls.

Automation Implementation

Make, n8n, CRM, forms and reporting as one reliable workflow.

Ett varmt nordiskt arbetsbord med papper, processkisser och materialprover
I start at the table — with the problem, decisions and exceptions. Only then do I choose the model and tools.
The Advisor’s Table · visualised working environment

Experience before big claims.

Patrick has worked with web, growth, AI systems and agent-related workflows for several Swedish organisations. Full cases are published when the evidence can be shown properly — not before.

View published cases Larger delivery through Haien
Boomr Group logotypHajen logotypSkärgårdens Måleri logotypSnackie logotypThunman logotypBärgningSportalen logotypContactmedia Sverige logotypCSS Klar logotyp

Certifications

Google Ads Search certification badge

Google Ads Search

Google Skillshop · Valid until 2027-01-08

Tools follow the problem — not the other way around.

A compact view of the stack I work with.

Useful to know before we start.

Short answers to the most common decision questions.

What does an AI implementation cost?

A basic automation normally starts from SEK 15,000. More advanced AI integrations and agent systems depend on data sources, risk, system connections and scope. The first audit call is free.

How long does it take?

A bounded automation often takes 2–4 weeks. A larger AI system with several integrations usually takes 4–8 weeks and is divided into testable stages.

Which systems can be connected?

I work with Make, n8n, CRM, Google Workspace, websites, forms, analytics tools and AI models. If a system has an API, there is often a reliable integration path.

How do we keep control of the AI agent?

We bound the agent's task, data sources and permissions, log important steps and define when a person must review or make the decision.

Which AI case is worth building first?

Bring a lead flow, manual process or recurring decision. In 30 minutes we prioritise the next step.

[email protected]
I would rather write

Book a free AI audit or send a short description of your lead flow, manual processes or the growth system you want to improve.