Business process automation with RPA and AI
At an insurance client, about 70% of routine claims flow through without human intervention: RPA carries the rule-based steps, AI carries the ones that take judgment. We build these hybrid systems for repetitive back-office processes, for SMEs and enterprises. Built by a Budapest-based team.
The next step is the AI opportunity assessment: from HUF 500,000 + VAT.
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Most repetitive processes — moving data between systems, assembling reports, handling claims and orders — break down into steps, and the right tool is decided step by step. Our rule of thumb: if you can draw an exact flowchart for a step, it belongs to RPA or a plain script; where the target system offers an API, that is even better. Where the decision depends on the input — unstructured text, varying formats, judgment calls — that is where AI works. Most real-world processes are a mix of the two.
Across our 46+ completed projects, automated processes reach a 40-45% efficiency gain; the typical ROI timeframe is 6-12 months. The order is driven by cost: a rule-based RPA bot is usually significantly cheaper to build and maintain than a bespoke AI system, so where a rule suffices we use a bot or a script, and we add AI only where it creates measurable value. We have talked clients out of AI projects and built them RPA solutions instead — it cost less, shipped faster, worked better.
Document-driven flows have pages of their own — our AI document processing page covers the full scope, and our AI invoice processing page shows how roughly 2,000 monthly invoices went from 3 days of processing to 4 hours at a freight forwarder. Which steps of a process belong to RPA and which to AI is covered in our RPA vs. AI article. This page is about the wider picture: the repetitive processes where manual work consumes the time — the assessment shows, step by step, what can be automated and with what.
How we split the steps
Rule-based steps with RPA or scripts
Intake, moving data between systems, posting, notifications — steps where the correct result is the same on every run. We build these with an RPA platform (Blue Prism, UiPath, Power Automate) or a script; where the target system offers an API, with a plain API integration.
Judgment steps with AI
Reading unstructured text, classification, extracting data from varying formats. At our insurance client, AI determines the claim category, extracts amounts from free-text descriptions, and adds a risk score — RPA carries the mechanical steps before and after.
Exception handling
Discrepancies are flagged and routed to a colleague with the problematic parts pre-highlighted — the decision stays with them. Deterministic checks — arithmetic validation, duplicate detection — run as rules, without a model.
Measurement from pilot to production
During the pilot we measure results on your own data; in production a monitoring dashboard watches the system, and an alert goes out if accuracy drops below a threshold. The return shows up in numbers, and degradation surfaces in time.
How does the implementation work?
- 1
AI opportunity assessment — from HUF 500,000 + VAT
We walk through the process step by step: which steps are rule-based, where a model is needed, what the volumes are, which systems to connect. The output is a written plan with cost and payback estimates you can use even if you continue without us. If there is no viable use case, we say so.
- 2
Pilot — 2-3 weeks
We test the best-return segment of the process live, on your own data, and measure the results. A small proof of concept is ready in 2-3 weeks.
- 3
Implementation and operations — 6-12 weeks
Most projects go from kickoff to production in 6-12 weeks. We connect to your existing systems — without ripping and replacing, with zero downtime. Source code, bots, and models remain yours; monitoring and support are part of how we work.
Why Leventech
A complete team from one source
A project manager, senior developers, and an AI DevOps engineer take the project from assessment to production. 46+ completed projects, 96% client satisfaction.
Automation experience at enterprise scale
Our founder led a multinational corporation's global automation and AI centre of excellence for five years, with $6M+ in annual savings. We bring the same engineering discipline to SME projects.
EU AI Act and GDPR built into development
We run the risk classification at project start and prepare the required documentation. We have experience implementing high-risk AI systems as well.
Your data stays with you
On-premise or private cloud deployment when needed, with open-source models. Your data never leaves your environment.
Common questions
The entry point is the AI opportunity assessment: from HUF 500,000 + VAT. An AI assistant or integration project is usually €15-40K; a document processing system typically runs €30-80K. A rule-based RPA bot is usually significantly cheaper to build and maintain than a bespoke AI system — if most of your process is rule-based, the project gets cheaper too. After the assessment you get an exact quote — no surprises.
If you can draw an exact flowchart for a step, that step belongs to RPA or a script; where a step takes judgment — unstructured text, varying formats — it belongs to AI. Most real-world processes are a mix of the two. If RPA solves 80% of the problem, we start there, and we add AI only where it creates measurable value.
Blue Prism, UiPath, and Power Automate. At our freight forwarding client, Blue Prism handles the ERP integration. Where the target system offers an API, we build a plain API integration instead of a bot.
The system flags discrepancies and routes them to a colleague with the problematic parts pre-highlighted — the decision stays with them. Deterministic checks — arithmetic validation, duplicate detection — run as rules, without a model; the model proposes, and the write into the target system is a controlled, auditable step.
By measurement. The assessment runs the numbers on your data, during the pilot we measure results on your own processes, and in production a monitoring dashboard watches the system — an alert goes out if accuracy drops below a threshold. Across our 46+ completed projects, automated processes reach a 40-45% efficiency gain; the typical ROI timeframe is 6-12 months.
A small proof of concept is ready in 2-3 weeks. Most projects go from kickoff to production in 6-12 weeks.
No. We connect to your existing ERP, CRM, and enterprise systems with RPA or via API — without ripping and replacing, with zero downtime. Source code, bots, and models remain yours.
Most back-office automation falls into the limited or minimal risk category. We run the classification at project start and prepare the required documentation. We have experience implementing high-risk AI systems as well.
Still have questions?
Start with a conversation
Tell us which process consumes the most manual work — we tell you which steps to hand to a machine, and which machine fits which step.
What would you automate in your business?
Tell us briefly about the task. We will reply by email to arrange an initial conversation.
The next step is the AI opportunity assessment: from HUF 500,000 + VAT.
What happens after you send?
In the first conversation we review the task, your existing systems and the outcome you need. Then we discuss whether a detailed assessment would help. We provide a proposal before any paid work.
Or reach us directly:
[email protected]
Monday-Friday, 9:00-17:00 CET