horoz.aiProcess automation
We automate repetitive work, documents and data flows so your team can spend its time on what matters. Not every step needs AI; where a simple automation is enough, that is what we recommend.
What it covers
- Mapping the process step by step and finding where time is lost
- Extracting information from documents and emails, classifying it and passing it to the right system
- Turning data that is copied between systems by hand into automated flows
- Adding approval and review points where a person should keep the decision
- Monitoring that the automation works correctly and making errors visible
When it is the right starting point
- Your team copies data from one system to another by hand
- Documents such as invoices, forms or contracts are read and processed manually
- Errors and delays grow as a process grows
How we work
Mapping the process
Together with the people who do the work today, we map the process step by step. We make visible where each piece of information comes from, where it is copied by hand, where it sits waiting and at which step errors appear most often.
Choosing the right tool
For each step we look for the simplest solution that works. For rule-based, repetitive steps a conventional automation is often enough; for steps such as reading documents or classifying free text, AI comes in. At this stage we also mark the points where a person should keep the decision.
Prototype and gradual rollout
We first try the flow with a small prototype on real documents and data. As the results pass your team’s review, we widen the scope of the automation step by step and roll it out while disrupting the current way of working as little as possible.
Documentation and handover
We document how the flow works, when it stops to wait for approval and what to do when something goes wrong. We go through this documentation with your team and share what they need to use the automation as a natural part of daily work and to step in when needed.
Monitoring and improvement
We monitor whether the automation works correctly and make sure that when a step fails, it gets noticed and it is clear who will step in. As the process changes, we update the flow together with you.
What it can look like in practice
Example scenario: Picture a business that receives invoices from its suppliers by email. A team member opens each invoice, reads the amount, the date and the supplier details, and types them into the accounting system by hand. As the volume grows, entries fall behind and small mistakes can slip through. In an automated flow, incoming invoices are recognized, the required fields are extracted from the document and the result is put to the team for approval before it reaches the accounting system; documents that cannot be read or look inconsistent are flagged separately. The team member can then spend their time checking records that await approval and resolving exceptions instead of typing in invoices. This is a hypothetical example and does not describe a specific client’s experience.
Frequently asked questions
Can every process be automated?
No, and we say so up front. Processes with clear rules, frequent repetition and data that is already digital are usually good candidates. In steps that need a fresh judgment every time, or where a mistake carries high risk, automation takes a supporting role and leaves the decision to a person. Which steps suit automation is something we assess together once the process is mapped.
When does process automation need AI?
AI makes a difference in steps that are hard to describe with rules, such as reading documents, extracting information from free text or classifying incoming requests. For rule-based work such as moving data from one system to another, a simple automation is usually more reliable and easier to maintain. The two can also work together in one process: AI reads the document and a rule-based automation passes the result to the right system.
Do we need to replace our existing systems?
Not necessarily. Our aim is to reduce the manual work between the systems you already use. In discovery we look at how your systems can be connected, and if a change is truly needed, we recommend it along with the reasons. If a required connection cannot be made, we say so plainly and weigh the alternatives with you.
What happens if the automation makes a mistake?
Making errors visible is part of the automation. We build the flow to flag cases it cannot be sure about and, where needed, to wait for approval from someone on your team. That way mistakes get noticed and corrected instead of passing through silently.
Which kinds of documents can you work with?
Invoices, forms, contracts and emails, the documents businesses handle every day, are the most common examples. Because the format, quality and content of your documents can vary, we always test the approach on your own real examples.
Can our own team run the automation?
Yes, that is the goal. We build systems your team can understand, take over and extend. We document how the flow works and go through the monitoring and intervention steps with your team; if you wish, we keep developing it together.
Other services to consider
LLM integration and software development
If you also need a feature that understands or generates text in your product, explore how a language model can be integrated into the application.
AI consulting
If you have not decided which process is suitable for automation, start by assessing use cases against data needs, benefits and risks.
Articles on this topic
- Common process automation mistakes and how to fix them
Common mistakes in process automation projects: how to avoid unmapped processes, missing data, misused AI and a lack of measurement.
Let us talk about what you need
Describe your situation in a few sentences and we will work out the right next step together.