In short
An AI automation agency automates a company’s repetitive work, such as sorting email, reading documents, entering CRM records and preparing reports, using language models and workflow tools. It is for SMEs and corporate departments that want to stop losing hours to manual tasks. CybUP maps the processes, builds a measurable pilot and takes it live with human approval and data protection rules in place.
What does an AI automation agency do?
An AI automation agency studies a company’s processes, works out which steps a language model and an automation workflow can take over, then builds and runs those workflows. Our work is less about selling chat windows and more about building machinery that runs quietly in the background: workflows that read incoming email and route it by type, pull the fields out of a PDF quote and write them to the CRM, or compile the weekly report and add commentary.
We combine two kinds of tool. The first is classic workflow automation for anything with clear rules, usually n8n installed on your own server. The second is language models, for steps that involve understanding, classifying or condensing text. We don’t hand everything to AI: giving a model a step that a simple rule could handle raises both the cost and the error rate.
Which business processes can be automated with AI?
The best candidates are tasks that repeat often, take text or documents as input and produce a result someone can check. How many times a day the task happens, how much of a person’s time it takes and what a mistake costs are the three questions that set the priorities.
- Email: classifying requests in a shared inbox, routing them to the right person, drafting replies
- CRM: turning enquiries from forms, email and WhatsApp into records and filling in missing fields
- Documents: extracting fields from PDFs such as quotes, orders and delivery notes, and checking them against a checklist
- Reporting: pulling data from several sources into a short, readable weekly summary
- Internal knowledge: answering questions from procedures and documentation
How does AI process discovery work?
Process discovery starts by sitting down with the people who do the work and writing out, step by step, how a task is actually done today. The process a manager describes and the one that happens at the desk are often different. Which inbox gets checked, what gets copied into which spreadsheet, who approves what: we write all of it down.
Say a 40-person import-export company has an operations team that reads hundreds of supplier emails every day and types the shipment dates into an Excel file. During discovery we measure how many hours a day this takes, what share of the emails follow a predictable pattern and what a wrong date leads to. The resulting picture shows where automation should start.
At the end of discovery you have a short list of processes ranked by expected time saved, technical difficulty and data sensitivity. The first pilot is chosen from that list.
Why start AI automation with a measurable pilot?
A pilot automates a single process within a limited scope and measures the result in numbers. Before we begin, we write down what will be measured: time spent per transaction, the share of records that need manual correction, response time and so on. When the pilot ends, we look at the same metrics and you decide whether to carry on.
In the import-export example above, the pilot might cover only the emails from the few suppliers the team corresponds with most. The model extracts the shipment date and order number, the workflow writes them to the spreadsheet, and anything the model isn’t sure about is flagged for the operations team. After a few weeks it is clear how many records needed correcting. This sidesteps the trap many AI projects fall into: trying to change everything at once.
How do you build human-in-the-loop approval into AI automation?
Human-in-the-loop means that wherever an automation error would be costly, an employee makes the final call. Language models can produce output that is convincing and wrong. So a reply to a customer, a payment record or a contract clause is never sent automatically; it is prepared as a draft, and the responsible person approves or corrects it with one click.
Over time the approval rate becomes a metric in its own right. If a certain type of transaction almost never needs correcting, moving that step to full automation is worth discussing. If it does need correcting, human approval stays for good; that is not a failure, it is sound design. The OWASP Top 10 for LLM Applications also lists giving a model too much autonomy (excessive agency) as a risk of its own.
“Utilise human-in-the-loop control to require a human to approve high-impact actions before they are taken.”
How is data protected in AI automation?
Data protection starts with knowing which data goes to which model, on servers in which country. With a cloud-based language model, personal data may be transferred abroad, which needs its own assessment under KVKK, Türkiye’s Personal Data Protection Law (Law No. 6698). The Personal Data Protection Authority’s guide to generative AI and personal data protection (in Turkish) sets out the data processing risks in these systems.
In practice we take three routes. Personal data the model doesn’t need is stripped out or masked before anything is sent. We prefer services with an enterprise agreement that commit to not using your data for model training. And when data must not leave the building at all, we run the model in-house; the details are on our local LLM deployment page. The legal compliance assessment belongs with your KVKK adviser; we prepare the technical side and the documentation.
“To reduce this risk, LLM applications should perform adequate data sanitization to prevent user data from entering the training model.”
AI automation, chatbot or internal assistant: what is the difference?
Automation runs on its own in the background, a chatbot talks to customers, and an internal assistant answers employees’ questions. All three use the same technology, but they are designed differently. For a customer-facing channel see WhatsApp AI chatbot; for a tool that lets staff ask questions of your procedures and documents, see internal AI assistant.
For most companies the right place to start is an automation nobody sees but everyone gains time from. A chatbot or an assistant stands on firmer ground once the processes behind it have settled.
What you receive
- Process discovery report and a prioritised list of automation candidates
- A pilot automation with success criteria agreed in writing beforehand
- Workflows built with human approval steps and error alerts
- A data flow document showing which data goes to which service
- Pilot results report and a rollout recommendation
- Short working instructions for the team that uses it
How we work
- 1
Free initial call
We discuss the tasks that eat time and the systems you use, and agree the scope of discovery.
- 2
Process discovery
We map processes step by step with the people who do the work, measure them and rank them.
- 3
Pilot design
One process is chosen, and its success criteria, data rules and approval steps are written down.
- 4
Build and measure
The pilot runs on real work for a few weeks and the results are reported against the criteria.
- 5
Rollout and maintenance
With your approval we move on to other processes; workflows are monitored and kept up to date.
Frequently asked questions
Will AI automation replace our staff?
Our aim is to cut repetitive tasks, not people. When copying, pasting and sorting shrink, the same team has more time for customers and for work that needs judgement. On risky steps the decision still rests with an employee.
Which AI models do you use?
We aren’t tied to a single provider. Depending on how sensitive the data is and the kind of work, we choose enterprise cloud models or open-weight models running in-house. We set out the choice and our reasoning in writing as part of the pilot design.
How long does an AI automation pilot take?
A pilot limited to a single process is usually built and measured within a few weeks. The timeline depends on how easily the systems involved can be accessed and how tidy the data is. We confirm the schedule after discovery.
How much does AI automation cost?
We send a written quote once we have reviewed the scope; the review is free. If a cloud model is used, the provider’s usage charges come on top, and we include an estimate of those in our report.
Will our data be sent to AI companies?
It depends on the design. Workflows that use a cloud model send only the data that is needed, and what goes where is documented. If data must never leave your premises, the model runs in-house.
Will it work with our existing software?
We connect directly to systems that have an API, and to those that don’t through email, files or the database. During discovery we check how each system can be reached. Marketplace and e-invoicing integrations are outside our scope.
What happens if the model gets something wrong?
Workflows are designed to flag uncertain cases and hand them to a person. Critical outputs aren’t sent without approval, and errors are logged. That way a mistake is caught before it turns into a consequence.