Technical Consulting

This is for you if

• You have built an AI product or system, but its execution costs are higher than expected.
• Your system has become overly complex and you are no longer sure which parts are actually necessary.
• Your system has developed dependencies across several models, services or tools and you want to review its architecture.
• The system works, but its output or performance does not meet your expectations.
• Your n8n workflows or agents have grown complex over time and need a structural and logical review.
• You want to know whether your current models and tools are the right choice for your tasks, or whether other options should be considered.
• Before investing more in development, you want to understand the core problem and what fixing it would cost and involve.

When not to hire me for this

If you do not have an existing system or a specific problem and simply want to build an AI system from scratch, this service is not the right starting point. It is designed for situations where an existing product, workflow or AI system is already in place and you want to understand why its cost, quality, complexity or performance does not match your expectations.

How it runs

1. 1

   Understand the product and the problem

   We first review the product, the purpose of the system and the problem that led you to ask for a technical review. We identify whether the main issue is cost, quality, architecture, complexity, dependency or performance.

2. 2

   Review the architecture and system components

   The overall structure is examined: models and agents, workflows, how the components talk to each other, the tools in use, and the way information travels between the parts.

3. 3

   Review workflows, logs and configuration

   Where access allows, n8n workflows, execution logs, configuration and the system's real behaviour are reviewed — so the finding is about what happens in execution, not only about what the design says on paper.

4. 4

   Review models and execution cost

   The models in use, how they are used and the cost of running each task are analysed. Where useful, alternatives are compared on cost, quality, speed and fit for the task.

5. 5

   Identify problems and risks

   Architectural problems, unnecessary dependencies, excess complexity, inefficient components and the risks that could cause trouble during further development are identified and documented.

6. 6

   Propose improvement paths

   Possible solutions are given for the problems found, from a targeted change to a partial redesign of the architecture. Where needed, the approximate cost and implementation path of each is examined too.

7. 7

   Deliver the report and roadmap

   At the end you receive a written report covering the findings, the problems, the risks, the proposed solutions and — where required — a cost estimate and an improvement roadmap, so it is clear what has to be done next.

What you are left with

A clear technical picture of your existing AI system: where the problem is, why it exists, what risks it creates and what the options are. The goal is not necessarily to rebuild the system, but to let you make the next technical decision from a more accurate understanding of it.