Only 7%1 of surveyed firms have AI at scale. We’ll put you among them.
We help companies automate selected processes, connect the systems they already use to AI, and bring AI agents into their development teams. AI agents are software that carries out the steps of a task itself, for example writing and fixing code. From checking viability, through connecting the solution to your systems and testing it, to handing it over to your team. We describe how a whole AI implementation in a company runs on a separate page.
1 Source: Grant Thornton and EDISONDA, “Adaptacja AI: Polska 2026” (fieldwork in July 2026, 314 managers at companies with at least 50 employees): 7% report AI deployed at full scale with a measurable financial result.
Three ways to work with us
Every implementation starts with one limited scope of work and ends with sign-off. Pick the one that matches what you want to improve.
Automating one process
We automate one repetitive task for your team. We hand over a working, tested solution with acceptance criteria agreed at the start — not an endless transformation plan. Supplier data, documents and e-mail, invoice flow, sales enquiries.
Start with a pilot on your own data →Connecting an AI assistant to your system (MCP)
We connect a chosen AI assistant to your system, including through MCP, an open standard that lets the assistant use your system’s functions. The assistant can use them safely: you can trace which permissions it was given and test how it behaves. For companies that own an API (the interface other software uses to work with a system and its data) and decide for themselves whether to launch the connection.
AI agents in your development team
We turn your development team’s work with AI agents (software that carries out the steps of a task itself — here, writing and fixing code) into a repeatable process: under agreed rules, with human review of code changes, on your own codebase. We measure the results on your team’s real work, not on lines of code generated.
Legal requirements and where to start
- AI Act technical compliance sprint →
Your lawyer sets the scope of work under the AI Act (the EU regulation on artificial intelligence), and we implement it: a register of the AI systems you use, content labelling, documentation and an AI literacy workshop as part of preparing for the Article 4 obligations.
- AI readiness and opportunity audit →
An optional first step: which processes are worth improving, what data they need and when simple rule-based automation, without AI, is enough.
See what AI implementation looks like step by step — from checking the process to handing over to your team.
Where this comes from
Facts you can check.
We base our AI implementations on more than 20 years of experience with software systems that businesses use day to day, integrations between systems and process automation — for companies ranging from a few dozen employees to corporations with more than 20,000 staff. Proven in practice, not on courses.
How we work
Four stages — each ends with a concrete result and your decision on the next step, and we measure the effect in the pilot before you invest in a full implementation.
Viability check
A conversation about one process: what repeats, what it costs and who knows it. We also check what can be built on the tools you already have. So before we start, you know where AI will have the biggest effect and what it costs.
Scope and acceptance criteria
We agree on the scope of the pilot, a short check of the solution on samples of your data: which data we use, who is responsible for the process and how we will know it works. The scope and the criteria are written down before anything is built.
Pilot and trial run
We build the solution and check it first on samples of your data, then for a few weeks on your real data — with a person approving the results. We connect it to your systems for good only once the results are good.
Handover
You keep a working system, documentation and tests, and your team practises with it on real tasks. Preparing your team is part of acceptance, so the business runs the system on its own from day one.
The cost depends on how many processes the project covers, how many systems it has to connect to and the state of your data. We give a price after a free first call and a look at samples of your data. What goes into a quote is explained in our note on what an AI implementation costs.
Why ploch.ai?
You work with a company that designs and builds the solution — from the first conversation, through the process audit, to going live and handing over. There is no account manager passing the work on. When a project needs additional specialist skills, we bring them in to match its scope.
In an operating business, automation is mostly about connecting the solution to the systems you already use and keeping the whole thing running, rather than about the AI model itself (the engine behind tools such as ChatGPT). So we start from your systems, your data and the people who know the process — not from choosing a tool.
We start with one process and check whether an existing tool can meet the need. Before building anything, we agree the scope and the acceptance criteria. If a simpler solution meets the goal, we build the simpler one — it works sooner and costs less to run.
- One process to start, with acceptance criteria — not a transformation plan
- Proven on systems businesses run every day, not in slide decks
- Your team learns to own it, not to depend on us
Who is behind ploch.ai and the experience our implementations rest on — on the about page. The principles we follow on every project are collected in our standards.
What stays in your company after the implementation
- A working system in your environment, with the code and its change history, or a configuration export, handed over. Using it does not depend on our access.
- Operating instructions, a description of what the system covers and the procedure for undoing a deployed change — written for the person who will run the system.
- Tests that check whether changes have broken existing behaviour, plus acceptance criteria, so you can check that a change to the AI model or to an API (the interface other software uses to work with a system and its data) has not broken anything.
- Your team, having practised with the system on real tasks rather than on a course — and a named person in the company who takes the topic further.
Ethics
Bringing AI into the work does not have to mean redundancies. We run implementations so that the company goes through the change without cutting jobs, or with as little strain on its people as possible — for ethical reasons we do not want this industry to be associated with job cuts.
For people whose work will mostly be done by AI, we plan alternative tasks together with the company — including those related to maintaining and developing the solutions that have been put in place.
Your employees know the company and its processes. AI does not know the nuances — they do. In our experience it is better to use AI so the company can do more, faster, than to cut jobs.
Describe one process
Describe one repetitive task in a few sentences: what repeats, in which tools and who does it today. We will give you a concrete answer: what can be automated and where to start. The first call is free and takes 20–30 minutes: write or call, and we’ll reply with two time slots.
Write to us through the form
The message goes straight to our mailbox. If you would rather e-mail or call, both are above.
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The address, registration details and answers to common questions are on the contact page.