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AI-Driven Process Development
Assessment, pilot, scaling — with business results you can actually measure.

AI that pays for itself

AI implementation for business is not a technology question — it is a process question. We find where your company gains the most, and then we build it.

In most companies the problem is not a shortage of AI tools. The problem is that nobody has asked the three questions that matter: which process eats the most working hours, where do the most errors appear, and which step could simply be removed from tomorrow onwards. AI-driven process development starts exactly there — not with picking a tool, but with mapping how your own operation actually works today.
Develops Hungary has been building custom software from Budapest since 2018. Over the past years the centre of gravity of our work has shifted towards enterprise AI: extracting data from documents, drafting customer service replies, internal knowledge bases, automated reporting and decision support. What they have in common is that each one is attached to a concrete, measurable process — not to a demo.

What AI-driven process development is — and why it is more than launching a chatbot

Launching a chatbot is, on its own, a purchase. AI-driven process development looks at an entire workflow instead: what data arrives, who touches it, into how many systems it has to be retyped, where the process stalls waiting for approval, and what happens when somebody gets it wrong.
When you measure that end to end, it usually turns out that AI can genuinely take over 10–20% of the process — but exactly the 10–20% that consumes half of the working time. The rest often does not need AI at all: it needs an integration, a better form, or a manual step removed altogether. That is why a good AI project is almost always a software development project as well.
The difference shows up in practice, too. A tool bought in isolation quietly falls out of use within six months. A properly designed process becomes part of daily operations, because people genuinely finish faster with it — and that is the only adoption metric that survives the first quarter.

Who it is for: SMEs and large companies

It is not a question of size, it is a question of repetition. If your company regularly does the same work by hand — with ten people or with five hundred — there is a return waiting. These are the areas where we usually find it first:

50–250 employee SMEs

The decision path is shortest here: with one well-chosen process you reach your first measurable result in 6–8 weeks, without hiring an in-house AI team.

Departments inside large companies

You don't have to transform the whole organisation. One department, one process, one tightly scoped pilot — that is the shape that survives internal approval.

Customer service and back office

Wherever a lot of incoming text arrives (emails, tickets, forms), the immediate saving in working hours is the highest.

Document-heavy operations

Contracts, invoices, quotes, minutes: unstructured input turned into structured data, without anyone retyping it.

Sales and reporting

Quote generation, keeping CRM data clean, assembling management reports automatically instead of every Monday morning.

Measurable AI

Our process: from assessment to operation

We work in 4–8 week cycles. At the end of every cycle there is something you can look at and decide whether to continue. There is no six-month strategy phase ending in a slide deck.

1. Assessment (1–2 weeks)

Interviews with the people who own the process, a review of your current systems and data sources, and an hour-level estimate of the manual steps. You end up with a list: where time and money are actually sitting in your operation.

2. Prioritisation (a few days)

We score every opportunity on two axes: expected return and implementation risk. We start with what is high-value and low-risk — not with what is most spectacular in a demo.

3. Pilot (3–6 weeks)

One process, real data, real users. We agree up front on what counts as success (for example: what share of cases is handled without human intervention), and at the end of the pilot we measure exactly that.

4. Scaling (in 4–8 week cycles)

We integrate the proven solution into your existing systems, extend it to more teams, and build the permissions, logging and measurement around it that a production system needs.

5. Operation

An AI solution is not a finished product: models change, your data changes, and so does regulation. We provide continuous monitoring, version updates and support so it still works the same way six months later.

How we differ from a strategy consultancy

The output of classic consulting is a document. Ours is a working system. We started as a software engineering team and we stayed one, which has two very concrete consequences. First, we do not promise anything we cannot build — because we are the ones who have to build it. Second, nothing gets lost in translation between the people who designed the solution and the people who deliver it.
Integration is home ground for us: we work from your existing ERP, CRM, ticketing system or database, through APIs where they exist and through other routes where they do not. The goal is never to make you replace systems that work — it is to make them talk to each other and to the AI layer.

One team from assessment to support

The person who sits in the assessment workshop is the person who writes the code later. There is no handover gap between strategy and delivery — the gap that kills most AI projects.

Custom development, not reselling a box

We are not tied to one vendor's platform. We choose the model, the tooling and the architecture that fits your process, your data volume and your budget.

Hungarian-speaking team, EU data handling

Budapest-based since 2018, working in Hungarian and English, with EU-hosted infrastructure and GDPR-compliant data processing agreements as the default, not the upsell.

Typical business results

We do not promise a specific number before we have seen your processes. What we can share is the typical order of magnitude from projects where the starting process was chosen well:

Hours saved

On a well-chosen, repetitive administrative process, typically 30–60% of the working time spent on it can be freed up. This is rarely about headcount: the freed capacity usually moves to the work where there were never enough people.

Error rate

In manual data entry and retyping, a large share of errors disappears structurally, because the data no longer passes through human hands. The errors that remain become visible: every automated decision is logged and reviewable.

Lead time

Where a case or a quote used to be measured in days, it often drops to hours or minutes — not because someone works faster, but because the waiting time between steps disappears from the process.

Return on investment

A significant share of pilots pays for itself within 6–12 months. If our assessment says a given idea will not, we tell you honestly and recommend against the project.

Our services in detail

AI-driven process development is the framework. The four services below are how it gets delivered in practice — depending on where your company stands right now.
If you would rather look at what we have already built, browse our and , or read more .

Frequently asked questions

How much does an AI-driven process development project cost?

The initial assessment is free. A tightly scoped pilot is typically in the HUF 1.5–5 million range, while a full production rollout touching several systems can be a multiple of that. We only quote a firm price after the assessment, against a concrete scope — a number given before that is always wrong for one of the two parties. If our numbers say the project will not pay for itself, we say so.

How long until we see results?

The assessment takes 1–2 weeks. The first pilot typically reaches the point of being measurable on real data with real users within 3–6 weeks. For a full rollout across several teams, plan with a 3–6 month horizon, broken into 4–8 week cycles with a concrete milestone at the end of each one.

What about GDPR and the EU AI Act?

Every project is designed to be GDPR-compliant: data minimisation, EU-based processing or appropriate safeguards, a data processing agreement, logging and a deletion process. Wherever possible we anonymise or pseudonymise personal data before it reaches the AI layer. Under the EU AI Act, typical enterprise automation use cases fall into the low-risk category, but we review the classification case by case — where a process is higher risk (HR decisions, credit scoring), we build in the required human oversight and documentation. If you have a data protection officer, we are happy to involve them from the assessment stage.

Do we have to replace our existing systems?

Almost never. In the vast majority of cases your existing ERP, CRM, operational or customer service system stays exactly where it is, and the AI layer is built around it — through APIs, database integration or, where neither exists, another route. We only suggest replacing a system if it is an obstacle in its own right, and even then we treat it as a separate project and a separate decision.

What happens on the first consultation?

Thirty minutes, online, free of charge. It is not a sales call: you walk us through how one or two of your processes work today, what eats the most time and what goes wrong most often. We ask questions back, and at the end we tell you whether we see a realistic AI opportunity — including when the honest answer is 'not yet'. If we do, you get a short written summary of the possible directions with ballpark effort. No obligation attached.

Free 30-minute AI potential consultation

Tell us in a few sentences which process eats the most time in your company. We come back within one business day with the directions we see — with no obligation.