AI implementation · Athens & Thessaloniki, Greece
AI implementation that works on Monday morning — not only in the demo
AI implementation is setting up one specific process so that it runs with an artificial intelligence model: with rules written in code, checks before anything goes out, and a human who approves where a mistake costs. The UpCommerce team builds automations, content with rules, AI customer service and reporting for businesses of every sector in Greece — and delivers them into your own accounts, with documentation, so you do not depend on us.
What we build
Six kinds of implementation most businesses ask for
Each one starts from a process that already exists and costs hours. We do not invent needs to make AI fit.
Workflow automation
Forms that sort and answer themselves, orders that update the customer, emails that become tasks in the CRM, Slack alerts when something needs attention. Connecting the systems you already have, with a model in the middle that reads, decides and writes within limits.
Product content with rules
Descriptions, category copy, meta titles, newsletter variations — for hundreds or thousands of SKUs. The model writes only from the data you give it, with a rules file for lengths, tone and forbidden words, and a script that checks every text before it goes up. For e-shops on Skroutz, correct product data is also a sales matter.
AI customer service
An assistant that answers by email, chat or social based on your own documents — return policies, delivery times, product instructions — and hands over to a human when the question goes outside them. With every answer logged, so you can see what it said and correct the rules.
Reporting and data
The morning or weekly brief: sales, advertising, stock, appointments, measurements — from several systems into one message, written in plain language, with the numbers that changed highlighted. The same model also answers questions about your data, when you ask them.
Image and creative
Image and copy variations for ads and social, within brand limits defined once. Not to replace the photo shoot — so that ten versions get tested where one was tested before. Works together with the social media and ads team.
Technical GEO on the site
Structured data per page, FAQ that matches the schema word for word, llms.txt, a structure that answers questions. It is the implementation behind the GEO / SEO for LLMs service — and, for new sites, part of website development.
How we work an implementation
We pick one process together — not five — and measure how it is done today: how many hours, how many errors, what each error costs. Without this “before”, nobody can say whether it was worth it.
The flow is built with real data and runs alongside the current one, not in its place. You see what it produces, note what is wrong, and from the errors the first rules are written.
Whatever must not be left to the model’s judgement becomes a check in code: lengths, numbers, forbidden words, which fields are never filled without a source. And we define where human approval goes — almost everywhere at first, later only where needed.
The flow moves into your own accounts, with instructions in plain language and a lesson for those who will use it. If you want monthly support, it exists. If not, it is yours and it works.
Rules, not prompts
The principle behind every implementation: what you know, you enforce
A prompt that tells the model “do not invent features” is no guarantee — it is a request. The difference between an implementation that lasts and one abandoned after three weeks is how much of what you know about your work has become checks that run every time.
Reads the data, writes, sorts, summarises. That is where it is good and fast.
Lengths, numbers, units, repetition, words that are not allowed, fields without a source. Deterministically, every time, with no “probably”.
Where a mistake reaches the customer, changes a price or goes public. Often at first, then less and less — based on the numbers, not on trust.
Three examples
From our own workshop — with their mistakes
Product descriptions: 50 SKUs, first test
We gave the model fifty products with deliberate traps — empty material field, same series with different ml, six near-identical lamps — and no rules. It invented nothing: zero materials, zero warranties without a source. But all fifty descriptions came out under the minimum length, with four identical openings per series and the words everyone uses. The rules file and the check script written afterwards catch exactly these — and are the same material taught in ELHub’s “Claude for eShops” masterclass.
The team’s morning brief
A scheduled task runs every day at 9:00 and sends a Slack message with the day’s appointments, yesterday’s client sales from reporting, new articles from three industry blogs, the Epoptes measurements, the weather and the name days. Six sources, zero manual work. The lesson we learned: the task file initially held a database key in plain text — keys now live elsewhere, and that is a rule in every handover.
Epoptes: an implementation that became a product
Three models, eight questions per business written in the language of its market, comparison with competitors, Google AI Overviews, a PDF with recommendations, billing and delivery on the right day — all automatic, every month. The recommendations are checked by code before they reach the customer, because the model once proposed “create a sitemap” to a site that had one since 2006. epoptes.gr →
Data and security: the four we do not negotiate
Agreed in writing before anything is built. Customers’ personal data only when needed and only as much as needed; financials and keys never.
Providers are used with accounts where data is not used to train models. The accounts are yours.
No key in a shared file, in a prompt or in a screenshot. One learns this once — we would rather you did not learn it through damage.
Every automatic action is logged and can be undone. A system you cannot stop within a minute is not delivered.
AI Transformation
When the question is not “build me this” but “where do we start”: a programme with mapping, pilot, training and measurement, from four arms of the group.
The programme →AI Consulting
Before implementation: what is worth it, what is not, in what order — from John Kiskipelis, as a separate engagement.
AI consulting →Website development
A new site or e-shop with the automations and GEO built in from the start.
Development →Email Automations
The email flows we already build for e-shops — now with AI in the content.
Additional services →Athens & Thessaloniki
Bring one process that eats your hours
In the 30-minute intro call you describe one job that repeats — with the systems involved — and leave with an answer: it can be built, what it will need, and how long it takes. Or that it is not worth it, if it is not.
Frequently asked questions about AI implementation
What does AI implementation mean?
Taking one specific process of the business — product descriptions, replies to customers, a weekly report — and setting it up to run with an artificial intelligence model, with rules, checks and a human approval point. The result is something that works every day without you in front of the screen, not a demo.
How long does an implementation take?
The first prototype within two weeks from the moment we have access to the data and systems. Then two to four weeks of testing with real data, where errors are measured and the rules that catch them are written. Handover with documentation in the second to third month, depending on how many systems are involved.
How much does AI implementation cost?
Per project, based on the process and the systems being connected — a flow that sorts emails costs differently from a customer-service assistant over hundreds of documents. You get a written proposal after the intro call. The usage cost of the models (API) is paid by you directly to the provider; it is transparent and usually a few euros a month per process.
Which tools and models do you use?
Claude, ChatGPT or Gemini depending on the job — often more than one, because they differ in cost and quality per task. For connections, automation tools such as Make or direct code when control is needed. For the rules, files the model reads and scripts that check the output. We do not sell our own software: everything built lives in your own accounts.
What about data and GDPR?
Before anything is built, we agree what may be sent to a model and what may not. We use the providers’ business accounts, where data is not used to train the models. Access keys are kept out of code and out of shared files — a mistake we often see in implementations done in a hurry.
Will my team be able to manage it afterwards?
That is the goal. Every implementation is delivered with documentation in plain language: what it does, where you change the rules, what to look at when something goes wrong. And with a short lesson for the people who will use it. If you want monthly support, it exists — but it is not a condition.
Implementation or an AI transformation programme?
Implementation when you know which process you want to change and want it built now. A programme when the question is “where do we start” and the change concerns many processes and roles. The implementation is often the pilot that decides whether the programme is worth it.
