There's no honest price tag we can put on a webpage — but there's a very real structure to where the money goes. Here's what drives the number, what to nail down before you budget, and how to read an estimate, so you walk into the conversation informed.
The cost of an AI project is dominated by the work around the model — the data, the integrations, and keeping it running. The model itself is a rounding error by comparison.
Cleaning, labeling, and wiring up your data is the single biggest line item on most AI builds.
Model and compute fees are a rounding error next to all the work that surrounds the model.
Monitoring, retraining, and upkeep costs to maintain the solution ROI.
Hardening a working demo into a system real people can depend on multiplies the cost this much.
Ranges reflect 2026 industry benchmarks (TechTarget, Gartner, and others). If a partner leads with model pricing, ask what they're leaving out.
In rough order of impact. Almost nothing about scoping AI is about the AI — it's about the environment it has to live in.
Clean, governed, accessible data means you're ready to build. Scattered and owned by nobody is the project before the project — good data habits cut build cost 20–35%.
How many systems must the AI read, write, or sit beside? One is a prototype, three a project, seven a program — and integration alone adds 20–50% to the budget.
"Use AI for support" is a research project. "Sort tickets into seven types at 90% accuracy" is a build. Vague scope isn't cheaper — it's just longer to pin down with you.
An AI system needs a human owner to monitor it, retrain it, and rule on edge cases. With nobody named, the handoff that keeps it working never happens, and run cost climbs.
Every build runs on dozens of small calls — which field, which edge case, good enough or not. Teams that decide in days ship; teams that debate for weeks pay for the wait.
Almost never the biggest cost, and model pricing keeps falling fast. Unless you're doing something unusually heavy, it's a rounding error next to the five above.
Most AI estimates are wrong because the inputs were fuzzy — a 2025 survey found a quarter of organizations underestimate AI cost by 50% or more. Nail these down and any number gets sharper.
A rough sense of scale — no dollar figures, because the real number comes from the six factors above, not a webpage.
One job, clean inputs, a clear outcome — like drafting recap emails or summarizing documents. Often lives inside a tool you already have.
AI inside a defined path, connected to two or three systems, with a human in the loop — like drafting AR collection emails from your ERP.
Multiple steps, live integrations, and real data work underneath — like an operations command center that reads across your tools.
Several use cases, governance, change management, and a roadmap — best broken into phases so value lands before it's all done.
We don't quote from a webpage. Cost gets pinned down as we move through Dream, Plan, and Build, and each step makes the next one's number sharper.
Workshops demystify AI and surface real use cases, scored on value and data readiness. You leave knowing what's worth building first — and roughly how heavy each idea is.
A short, fixed-fee engagement turns the priority idea into a concrete scope built on your real data and systems — not a guess. This is where an estimate becomes a real number.
We build what Plan proved, priced from a scope you've already seen and signed off on. The number was set before a line of production code — so there's nothing to flinch at later.
If an estimate can't answer these, it isn't a real estimate yet.
Tell us what you're trying to build, and we'll turn it into a scope and a number you can take to a budget owner — in about 30 minutes, no slides.