ARTIFICIAL INTELLIGENCE PROJECTS FOR BUSINESSES
Cétery designs and builds bespoke AI projects, integrated into your processes, your data and your teams. No hype: technology that stays up and running.
OFFICIAL PARTNER OF
WHY CÉTERY
Almost every company has tried AI by now. Almost none has put it to work.
Cétery doesn't bolt AI on top of your business: we integrate it into your processes, your data and your team, and we don't call it done until it works without us in the room.
Repetitive work stops tying up your best people.
We identify the tasks your team repeats every week and turn them into workflows that run on their own, with human oversight where it matters. The process doesn't change owner: it changes speed.
TEAMS WITH DEFINED PROCESSES THAT STILL DEPEND ON COPYING, PASTING AND CHECKING BY HAND.
An assistant that knows how you work, not the whole internet.
We build assistants connected to your documents, criteria and internal systems, so they answer the way the most experienced person on the team would. Every answer cites where it comes from.
SUPPORT, SALES, TECHNICAL OR BACK OFFICE TEAMS WITH A LOT OF SCATTERED KNOWLEDGE.
Your data already holds the answers; what's missing is a way to ask.
We organise and connect the information that already lives in your ERP, your CRM and your spreadsheets so you can query it just by asking. It stops being an archive and becomes a decision-making tool.
MANAGEMENT AND TEAMS WHO MAKE DECISIONS WITH REPORTS THAT ARRIVE LATE OR DON'T ADD UP.
A tool nobody uses is a cost, not an investment.
We train your teams on their own cases, not generic examples, and set clear criteria for what gets delegated to AI and what doesn't. The aim is for them to keep going without us.
COMPANIES WHERE AI IS ALREADY BEING USED ON THE QUIET, WITH NO SHARED CRITERIA OR CONTROL.
THE METHOD
The order isn't bureaucracy: each phase reduces the risk of the next one and ends in something you can see, test and use to decide. If a phase doesn't convince you, we don't move on to the next. Timings are a guide.
We sit down with the people who do the work and look at your processes and your data as they are today, not as the manual says they are. We come away with a short list of where AI genuinely adds value and where it doesn't belong.
We build the most promising case on your real data and put it in front of the people who would use it. It shows whether it works in your context before anything critical is touched.
We take the prototype into production: it gets connected to your systems, it's agreed who is accountable for what, and it's documented. We train the teams who will use it so they stop depending on us.
With one piece working and measured, the pattern is repeated on the next process. This is where we decide what gets extended, what gets fixed and what gets retired because it isn't worth maintaining.
USE CASES
Production reports are filled in by hand on the shop floor and nobody checks them against quality until the end of the month.
We automate the capture of those reports and connect them to the incident history so a deviation shows up the same day.
The plant manager spots the pattern while it can still be corrected, not in the month-end meeting.
Every quarter-end, the team spends days reading contracts and invoices to extract the same data every time.
An internal copilot that reads that paperwork, proposes the extraction and always shows the passage each figure came from.
The professional reviews and signs off instead of retyping, and every figure stays traceable to its source.
Delivery issues come in by phone, email and WhatsApp, and each person resolves them in their own way.
A single channel where AI classifies each issue, links it to the delivery note and proposes the reply according to your own criteria.
Customer service stops depending on who happens to pick up the phone that day.
Product knowledge lives in the heads of two veterans, and the rest of the team either asks or improvises.
We gather product sheets, catalogues and supplier emails into a copilot that answers with the real information from your stockroom, not from the internet.
Someone a month into the job answers with the judgement of someone fifteen years in.
Engineers write up the job report at the end of the day and the useful information stays in loose notes on their phones.
On-site dictation that produces the structured report and cross-checks it against that installation's history before the next visit.
The engineer arrives knowing what has already been tried there, and the report is closed during the visit itself.
These are illustrative scenarios, not clients of ours: they are there to kick off the Diagnosis, not to promise a result.
FREQUENTLY ASKED QUESTIONS
That is decided before a single line of code is written: what data goes in, where it's processed and what gets logged. We work with the minimum data necessary and, when the case calls for it, with models hosted wherever your company is comfortable. What can't leave your company doesn't leave.
That isn't what we design. We automate tasks, not roles: what gets freed up is the time your team loses to repetitive, low-judgement work. If the real aim is to cut headcount, we're not the right supplier and we'd rather say so before we start.
We start from the assumption that it will. That's why every use case has a defined tolerable margin of error, human review wherever a mistake isn't acceptable, and traceability so you know why it answered what it answered. For anything critical, the AI proposes and a person signs off.
No. We integrate on top of what you already have and how your people use it. If a system doesn't allow a reasonable integration, we say so in the Diagnosis and look for the least invasive route: changing tools would be your decision, never a requirement of ours.
It isn't zero, and be wary of anyone who tells you it is. There's model usage, maintenance when your processes or systems change, and regular review of what the AI is doing. That cost is estimated before Integration, not once there's no going back.
The metrics are set in the Diagnosis and measured before anything is touched: time per case, volume processed, errors, real use by the team. Without that baseline there's no honest way to show an improvement. If the prototype doesn't move those numbers, it doesn't go on to Integration.
We'll tell you. The Diagnosis exists to reach that point too: sometimes the problem is a poorly defined process or data that hasn't been put in order, and putting AI on top only makes it more expensive and harder to explain. Closing at that stage is better business for both of us.
We start by looking at your real processes and data. If AI isn't the answer, we tell you before billing you for a project.
Before anything is integrated, you'll see a prototype working with your own data. Decisions are made on what can be seen, not on a slide.
We document, we train your team and we hand you control. If tomorrow you want to carry on without Cétery, you can.
ABOUT US
Cétery is an artificial intelligence company based in Elche, Spain, founded by Manuel Antón. We work with SMEs and mid-sized companies across Spain on short, measurable projects, with our own team and without subcontracting the work that faces the client.
TECHNOLOGY PARTNERS
Early access to models and direct technical support for projects built on GPT.
European cloud infrastructure, with data hosted in the European Union when the project requires it.
Alternative AI models and services, so as not to depend on a single provider when the case calls for it.
Write to us about the process that eats up most of your time. We'll reply with an honest first read: if we see potential, we'll say so; if we don't, we'll say that too.
Email us at manton@cetery.com