Why doesn't Dyvab publish a fixed price list?

Because every process is different. A quote depends on how many systems need to talk to each other, how messy the data is, and how much testing is needed before it can run unsupervised. A fixed catalogue price would either overcharge simple jobs or undercharge complex ones.

Is there a cost after the project is delivered?

Only if you want it. Ongoing support or monitoring is offered as an optional, reduced-rate add-on — it's not baked into the initial price, and it's not required to keep the automation running.

Why fixed price instead of hourly billing?

Hourly billing rewards slow work and punishes efficiency. A fixed price means you know the total cost before anything starts, and the incentive is aligned: the automation has to actually work, not just consume hours.

What makes a project expensive versus cheap?

Mostly the number of systems involved and the state of the data. A single task pulling from one clean source is cheap. A workflow that needs to read, reconcile and write across four different tools with inconsistent formats is not.

How Much Does AI Automation Cost in Spain: Real Ranges

Prices for AI automation projects in Spain range from a few thousand euros for a single well-defined task to tens of thousands for broader integration projects. The single biggest factor is complexity, not hours worked — here's what actually moves the number.

If you've searched for this, you've probably run into the same wall: agencies that won't give a number until after a "discovery call," and vague phrases like "it depends." That's frustrating, but it's also honest — it really does depend, and anyone quoting a fixed number before understanding your process is guessing. What follows is a realistic breakdown of what actually drives the price of an AI automation project in Spain, so you can at least tell whether a quote you receive is in the right ballpark.

What actually determines the price?

Not the technology. Most automation and AI tooling used today is mature and relatively cheap to run — the cost isn't in the AI model itself, it's in the engineering work around it. Four things move the price more than anything else:

How complex is the process being automated?

A process with one input, one clear rule, and one output is simple to automate. A process with exceptions, edge cases, human judgment calls, or steps that change depending on context takes much longer to map out and build reliably — and that mapping work is billable time even before any code gets written.

How many systems need to be connected?

Automating something inside a single tool (say, cleaning and organising data already in one spreadsheet) is far cheaper than a workflow that needs to pull from a CRM, cross-reference an inventory system, and push results into an accounting platform. Each additional system usually means a new API to learn, new authentication to handle, and new failure points to account for.

How much and how messy is the data?

Clean, structured data is cheap to work with. Data scattered across PDFs, inconsistent spreadsheets, scanned documents, or systems with no proper export options adds real time to a project — often more than the automation logic itself.

Does it need to run unsupervised, or with a human in the loop?

A tool that assists a person (drafts something for review, flags anomalies for a human to check) is simpler and cheaper than a fully autonomous system that has to make decisions and act without anyone checking the output. Unsupervised systems need more testing, more error handling, and more safeguards before they're trustworthy enough to leave running.

What do different types of projects actually cost?

These are honest, orientative ranges based on how the market in Spain generally prices this kind of work in 2026 — not a fixed Dyvab price list, since we don't have one (more on why below).

A single, well-defined task automated end to end — for example, generating and sending a recurring report, or classifying and routing incoming requests — tends to fall in the low thousands of euros. The scope is narrow and the systems involved are usually one or two.
A broader workflow connecting several systems, with some data cleanup and business logic in between, typically lands somewhere in the mid-to-high thousands, depending on how many integrations and edge cases are involved.
A more complete integration project — automating a whole department's workflow across multiple tools, with ongoing decision logic and proper error handling — can reach the tens of thousands of euros. This is closer to a small software project than a quick script.
Team training or capability building, where the goal is teaching a team to use AI tools effectively rather than building a specific automation, is usually priced separately and tends to be lower than a full build, since the deliverable is knowledge transfer, not software.

These ranges will vary depending on who you ask and what exactly is included — some providers count only the build, others include weeks of support. Treat any number you see, including these, as a starting reference point, not a quote.

Why doesn't Dyvab just publish a price list?

Because a price list would be dishonest. There are no closed, predefined packages at Dyvab — every project is scoped specifically for the process in front of us, because two automations that sound similar on paper ("automate our invoicing") can differ enormously in actual work depending on the systems involved and the state of the data behind them.

What we do instead is work on a fixed price per project, agreed before starting, not an hourly rate. That means:

You know the total cost before any work begins — no surprise invoices for extra hours.
The incentive is aligned toward finishing something that actually works, not toward logging more time.
Optional maintenance or support after delivery is priced separately, at a reduced rate, and only if you want it — it's never bundled into the upfront number to inflate it.

The trade-off is that we can't give you an instant number on a landing page. What we can do is look at your specific process and tell you, honestly, which of the ranges above it's likely to fall into — and why.

How do you get an accurate estimate?

The short version: describe the process as it actually happens today, including the messy parts. The systems it touches, how the data currently moves between them, and what "done" looks like for you. That's usually enough for a first, realistic estimate — refined further once we look at it properly.