What AI adoption actually costs

"What does it cost?" is the question almost nobody answers straight in this market, and for an ugly reason: opacity protects the vendor, not you. Custom quotes that only appear after three calls, prices that depend on "how much value you perceive," and surprises mid-project. This post does the opposite: it puts the numbers on the table before you talk to anyone.

These are our real ranges, the same ones you see on the services page. After the ranges comes what actually matters: what moves the price within each one, the hidden costs nobody mentions until you've already started, and how to build a sensible budget for your first quarter.

The ranges, no runaround

Four stages, four pricing shapes. These are ranges we honor, not anchors to negotiate from.

1. Audit — from MXN $15,000. You receive a workflow and bottleneck map, up to three prioritized opportunities with estimates and assumptions, and a written plan you keep. We agree on final scope and price before starting.
2. Build — USD $5,000 to $50,000 per project. Fixed scope and price, with delivery milestones agreed before work starts and deployment on infrastructure you control. The range is wide because a simple workflow doesn't cost the same as a system with several integrations.
3. Training & adoption — USD $150 to $250 per hour. Hands-on sessions for the team that'll run what was built, plus documentation and an internal owner. It's billed by the hour because every team needs a different amount.
4. Optimization — from USD $1,500 per month. An optional monthly retainer: ongoing tuning, new workflows, and support as your operation grows. You continue while the return justifies it.

The logic of the model: the audit is a diagnosis with its own deliverables, implementation is quoted separately, and optimization is optional. Each stage has a clear scope and currency.

What moves the price of a build

The USD $5,000 to $50,000 range is wide on purpose, because a project can be an afternoon's work or a full system. Five factors move where you land:

1. Number of integrations. Connecting two systems is simple. Connecting five that weren't designed to talk to each other is where the effort grows.
2. Logic complexity. A fixed rule is cheap. Decisions that depend on context, on several tables, or on natural-language text cost more because they need a system that reasons, not a template.
3. State of your data. Clean, accessible data speeds everything up. Dirty data adds work before you even start the interesting part (more on this below).
4. Interface. An automation that runs on its own costs less than one that also needs a screen for your team to interact with.
5. Volume and scale. A system for a hundred transactions a month is designed differently than one for a hundred thousand.

The best way to move toward the low end of the range is to scope tight. A small, well-chosen first build almost always beats a big all-at-once project. The build-versus-subscription decision, and when each one wins, is in build vs. buy for SMBs.

How a build is billed

The fixed price settles the "how much," but the "when you pay" matters for your cash flow too. Fixed-scope projects are billed by milestone: part at kickoff, the rest as the system is delivered. No giant upfront deposit, no open-ended monthly payments for a project that should have an end.

That has a consequence worth putting in writing: everything we ship is yours as we ship it. Code is delivered in your repository as the project progresses. Milestones and acceptance criteria should be written down before work starts.

The hidden costs nobody mentions

The ranges above are the visible price. These are the costs that show up if you don't plan for them, and that sink budgets:

• Dirty data. The number-one hidden cost. If your data lives in silos, is duplicated, or is inconsistent, it has to be cleaned before you build on it. It's not optional: a system on bad data produces bad results. Worth measuring the state of your data first, with the five readiness dimensions.
• Underestimated training. Many people budget the build and forget adoption. A system the team can't use saved nothing. Training isn't an extra, it's what turns software into results.
• Third-party costs. Model APIs, the WhatsApp Business platform, and some SaaS charge by usage. They're yours and they're ongoing. They vary by usage and provider, so put them in the budget from day one instead of discovering them on the first invoice.
• The cost of doing nothing. The least visible one. Every month the manual process continues, it keeps costing hours. That current cost is your real benchmark: a quote is only "expensive" or "cheap" against what you're already paying in time.

How to budget your first quarter

A realistic budget to start isn't a number out of the air. Build it in four steps:

1. Budget for diagnosis. The audit starts at MXN $15,000. Ask for the final scope and price in writing before work begins; its plan helps you decide what to build and what to defer.
2. Budget a single, scoped build. For the first project, aim at the low end of the range: pick the highest-return, lowest-complexity opportunity. The final cost depends on scope, data, and integrations.
3. Reserve for adoption. Set aside training hours in the same budget as the build. Budgeting the build without adoption is the classic mistake that leaves the system unused.
4. Decide optimization later, with data. Don't commit the monthly retainer at kickoff. Let the first build prove its return, then decide whether ongoing optimization is justified.

The underlying principle: the first quarter is for proving AI pays in your operation with a scoped investment, not for transforming everything at once. When the first build proves its return, budgeting the next one stops being an act of faith and becomes a decision with data behind it.

To apply these ranges to your operation, request an audit. The plan includes estimates and their assumptions, not a guaranteed return. Read what the audit includes.

Frequently asked questions

What does the audit price include?

A workflow and bottleneck map, up to three prioritized opportunities with estimates and assumptions, and a written action plan. The document is yours; implementation is quoted separately. The audit starts at MXN $15,000, with the final price agreed before work begins.

Does a build always start at $5,000?

That's the floor of the range, and a well-scoped first project usually lands near it. It climbs toward $50,000 with integrations, logic complexity, the state of your data, and scale. The way to keep it low is to start small and fixed-scope, not to take on everything at once.

Do I have to sign up for the optimization retainer?

No, it's optional. The from-$1,500-a-month optimization exists for when your operation is already growing on AI and you want continuous improvements. The usual path is to start with a build, train the team, and decide on the retainer only later, once the first project has proven it pays off.