How AI Implementation Projects Are Priced (And What Actually Drives the Cost)

Most AI agencies won't tell you how they price. We will. Not because it's simple — it's not — but because you deserve to know what you're actually buying before we talk. Pricing conversations shouldn't feel like hostage negotiations. If you're evaluating whether AI automation makes sense for your business, you need real numbers, not "it depends" and a discovery call that ends with sticker shock.

So here is how we think about cost. Here is what drives it. And here is what we've charged for real projects — within ranges that protect our clients' confidentiality but are honest enough to be useful to you.

The two things that actually drive cost

Every AI project we scope comes down to two variables: the complexity of the workflow being automated, and the depth of integration required.

Workflow complexity means: how many steps does the process have, how much edge-case reasoning is involved, and how often does a human currently need to make a judgment call? A voice receptionist that answers calls, routes to the right department, and takes messages is relatively predictable. A damage assessment agent that ingests photos, cross-references policy documents, applies multi-jurisdiction rules, and outputs a structured claim recommendation is not. Both are AI — the second costs significantly more to build.

Integration depth means: is this a standalone tool, or does it need to talk to three existing systems with ten years of legacy data in formats that were never meant to be machine-readable? Clean integrations with modern APIs are fast. Wrangling a CRM export from 2019 and a PDF-based approval workflow is slow. That time difference is the cost difference.

What does NOT drive cost: hours worked. We don't bill hourly. We price projects as flat deliverables. That means if something takes longer than expected, that is our problem, not yours. You're buying an outcome, not a clock.

What a typical build looks like

Here are three real archetypes we build, with honest pricing ranges.

An AI voice receptionist — answers calls 24/7, routes to the right person or department, takes structured messages when no one is available, sends summaries by email or WhatsApp — typically costs €3,000–5,000 to build and €300–500 per month to run (hosting, voice API, maintenance). The return calculation is straightforward: a single missed after-hours lead per week, in most B2B contexts, recovers the annual investment inside the first quarter.

A custom damage reporting workflow — the kind we built for TraXpert, where field agents photograph damage, the AI generates structured reports in multiple languages, cross-references policy clauses, and routes for approval — is more complex and more deeply integrated. Expect €8,000–15,000 to build, €500–800 per month to run. The return: 80% faster processing time and zero additional headcount to absorb volume spikes.

A relationship intelligence tool — like Corial, which captures voice notes from sales calls, extracts key relationship signals, syncs structured data to a CRM, and surfaces action items before the next meeting — sits in the middle. €5,000–10,000 to build, €400–600 per month to run. The return is harder to quantify in a spreadsheet, but easier to feel: institutional knowledge stops leaking when people leave.

According to McKinsey's 2024 State of AI report, only 16% of SMBs have deployed AI beyond pilots. The gap between awareness and implementation is not primarily technical — it's economic clarity. People don't know what it costs, so they don't pull the trigger. These ranges exist to close that gap.

What's included vs. what's extra

Every project includes: a paid discovery session (€500, credited against the build if you proceed), full design and architecture, the build itself, deployment to your infrastructure or ours, and 90 days of iteration after go-live. That last part matters. In our experience, the first 90 days surface the edge cases that the brief didn't anticipate. We stay engaged to fix them.

The monthly retainer includes: server and API hosting, system maintenance and security updates, monitoring, and a monthly check-in to review performance and flag anything that needs adjustment.

What's extra: integrations that weren't in the original scope (i.e., you want to add a fourth system after we've built for three), entirely new feature modules after launch, and work in languages or regulatory contexts that weren't defined at kickoff. We scope these as addons rather than retrofits. The cleanest and cheapest time to define scope is before we start.

When AI is not the right answer

We tell clients this in every discovery call, and we mean it: AI automation does not make sense for every process. If a workflow runs fewer than 50 times per month, the cost of building and maintaining automation is likely higher than the cost of doing it manually. Simple math. We will tell you that.

If the decision requires genuine human judgment every single time — not pattern-matching, not routing, but actual contextual reasoning that a subject-matter expert would struggle to articulate as a rule — AI will assist but not replace. We design for assistance in those cases, not automation.

If you don't have clean, accessible data — or if the process that needs automating isn't actually documented — we'll spend your budget building scaffolding before we build anything useful. In that case, we'll tell you to document the process first and come back in three months.

We turn away projects we can't deliver honestly. It is the only way to maintain a reputation worth having.

The real question to ask yourself

Don't start with 'what will this cost?' Start with 'what does it cost me NOT to automate this?' Missed calls after 5pm. Claims sitting in an inbox for four days. Sales intelligence that evaporates when a rep leaves. Institutional knowledge that lives in someone's head and walks out the door with them.

If you can put a number on any of those — even a rough one — the investment calculates itself. If you can't, that's useful information too: it might mean the process isn't the bottleneck, and we should be looking elsewhere.

Either way, you deserve an honest answer before you spend anything. That is what the discovery session is for.