What's the cheapest way to start?

A fixed-fee readiness assessment — often from around US$2,500 — is the lowest-risk first step. It tells you where AI will pay off and what a build would cost, so you don't over-commit before you know.

Why does data cleaning cost so much?

Because it's where the real work is. Getting a business's documents and data organized, accessible, and reliable is consistently 30–50% of the total effort on an AI project. It's also what most failed projects skipped.

What are the ongoing costs?

Two parts: the underlying infrastructure or usage (model and hosting costs), and a retainer to monitor, tune, and extend the system. Together these are typically a monthly figure that scales with how much the system does.

How do we know it's worth it?

Measure the baseline before you build — hours lost, cost per process, error rates — so the after is provable. A good assessment captures that baseline on day one, precisely so ROI isn't a guess.

How much does AI implementation cost for a business?

"How much will this cost?" is the fair first question, and most AI companies dodge it. Here's a straight answer, the ranges you can expect, and — more usefully — what actually drives the number, so you can control it.

The honest ranges

AI work usually comes in three stages, each priced differently:

Assessment — a fixed-fee readiness assessment, often from around US$2,500 for a focused review up to $15,000–$50,000 for an enterprise-grade audit with document and data analysis.
Build — a first governed system (for example, an internal AI assistant on your own knowledge) typically runs US$15,000–$75,000 depending on scope, sources, and integrations.
Run — an ongoing monthly retainer to monitor, tune, and extend the system, scaled to how much it does.

These are market-typical ranges for small and mid-sized businesses; enterprise programs run higher. The point isn't the exact figure — it's that you should expect to pay in stages, starting small.

What actually drives the cost

The AI model is rarely the expensive part. What moves the number is:

Data readiness — cleaning and structuring your documents and data is consistently 30–50% of the total cost of an AI project. Messy data is the single biggest cost driver, and the one most often underestimated.
Scope — one well-defined workflow is far cheaper than "AI across the business." Narrow beats broad, especially first.
Integrations — connecting to your existing systems adds cost; a standalone tool is cheaper than a deeply embedded one.
Governance — controls, evaluation, and audit trails add effort, but they're what make the system safe to rely on (and, in regulated settings, what make it allowed at all).

Assessment, build, run — and why the order matters

Starting with an assessment isn't just prudent, it's cheaper overall. It scopes the build accurately (so you don't overpay for guesswork), catches the data problems that blow up budgets mid-project, and often credits its fee against the build if you proceed. Skipping straight to a build without knowing your data readiness is how projects run over — and how they end up in the majority that never deliver value.

How to avoid overspending

Start with one workflow, not a transformation. Prove value, then expand.
Fix the data question first — an assessment tells you how much cleaning is really needed.
Prefer fixed-fee for defined deliverables, so cost risk sits with the provider, not you.
Measure the baseline before you build, so you can prove the return.

Why we quote fixed-fee

Axys prices the assessment as a fixed fee, and quotes the build from the assessment's evidence — because you shouldn't carry the risk of unknowns that a proper diagnostic should have surfaced. See the assessment and its two scopes →